# A Comprehensive Survey of Few-shot Learning: Evolution, Applications,
Challenges, and Opportunities

Yisheng Song, Ting Wang∗, Subrota K Mondal, Jyoti Prakash Sahoo (Corresponding
author: Ting Wang.) Yisheng Song and Ting Wang are with the Engineering
Research Center of Software/Hardware Co-design Technology and Application,
Ministry of Education; the Shanghai Key Laboratory of Trustworthy Computing;
East China Normal University, Shanghai 200062, China (email:
<EMAIL_ADDRESS>twang@sei.ecnu.edu.cn). Subrota K Mondal is with
the Faculty of Information Technology, Macau University of Science and
Technology, Macao, China (email: skmondal@must.edu.mo). Jyoti Prakash Sahoo is
with the Department of Computer Science & Information Technology, Institute of
Technical Education and Research, Siksha ‘O’ Anusandhan University, India
(email: jpsahoo@ieee.org).

###### Abstract

Few-shot learning (FSL) has emerged as an effective learning method and shows
great potential. Despite the recent creative works in tackling FSL tasks,
learning valid information rapidly from just a few or even zero samples still
remains a serious challenge. In this context, we extensively investigated 200+
latest papers on FSL published in the past three years, aiming to present a
timely and comprehensive overview of the most recent advances in FSL along
with impartial comparisons of strengths and weaknesses of the existing works.
For the sake of avoiding conceptual confusion, we first elaborate and compare
a set of similar concepts including few-shot learning, transfer learning, and
meta-learning. Furthermore, we propose a novel taxonomy to classify the
existing work according to the level of abstraction of knowledge in accordance
with the challenges of FSL. To enrich this survey, in each subsection we
provide in-depth analysis and insightful discussion about recent advances on
these topics. Moreover, taking computer vision as an example, we highlight the
important application of FSL, covering various research hotspots. Finally, we
conclude the survey with unique insights into the technology evolution trends
together with potential future research opportunities in the hope of providing
guidance to follow-up research.

###### Index Terms:

Cross-domain, Few-shot Learning, Fine Tuning, Meta learning, Transfer
Learning.

##  1 Introduction

Recent advances in hardware and information technology have accelerated the
interconnection of billions of devices in various IoT-enabled application
domains. Smart and adaptive devices are increasingly deployed in critical
infrastructures such as health, transportation, industrial production,
environmental detection, home automation, and many other justifying the
Internet of Everything (IoE) frameworks. These massive number of terminal
devices have been generating a huge amount of data, which need to be sent back
to the server for central processing and storage. Although the total amount of
generated data at the edge is very large, the volume of every dataset
generated by a single device or single scene is extremely limited with very
few samples. Traditional data-driven and single-domain algorithms do not
perform well in these settings. To this end, numerous research has been
conducted in exploring effective learning methods based on few samples and
cross-domain scenes. Few-shot learning (FSL) as well as meta-learning have
inevitably emerged as a promising way. However, how to effectively obtain
valid information from small sample data set or even cross-domain still
remains the greatest challenge faced by FSL today.

Besides, data distribution in real-world scenarios often has long-tail effects
and it is difficult to generalize the same model across diverse domains.
Taking the smart manufacturing industrial inspection as an example, such poor
generalization issue has become one of the key challenges affecting the
performance of its intelligent models. Specifically, current industrial
quality inspection equipment requires certain specific lighting conditions,
and the AI models trained under one lighting condition are difficult to
”generalize” to other lighting conditions. In addition, considering the high
accuracy requirements of industrial quality inspection scenarios, current AI
models, usually using supervised learning, require a large number of defective
samples for training. However, it is difficult to collect a sufficient number
of satisfactory samples as the proportion of defective products in actual
production scenes is small. Moreover, it is not possible to transfer across
domains either. For example, a model for PC appearance defect detection cannot
be directly used to detect defects of mobile phone screens, refrigerators,
washing machines, or even different models of PCs. Similarly, as another
example, when recognizing character images of components and circuit boards,
as there are many suppliers of components, many types of devices, and many
different character styles, thus it is not possible to collect a sufficient
number of all kinds of samples of character images for one supplier, resulting
in few or no samples of each type. Table I provides a detailed summary of
these challenges.

TABLE I: Current challenges that industry urgently needs to address Scenes | Challenges | Key Solutions
---|---|---
Quality Inspection Line |  | Susceptible to light
---
Few sample data
Unable to transfer across-domains
| Few shot cross-domain transfer
---
Robust model generalization
Electronic Component Identification |  | A variety of colors, sizes and brands
---
Insufficient samples of each brand
Existence of unseen new brands
| Few-shot learning
---
Transfer learning
Unified feature representation

To address these challenges more effectively, FSL has produced some creative
work on data, algorithms, and models. Up to now, as one of the most classical
taxonomy, FSL is classified into meta-learning and metric-based learning. In
this review, from the perspective of challenges, we divide the FSL into data
augmentation, transfer learning, meta-learning, and multimodal learning. Data
augmentation focuses on simulating data in different scenarios by metric or
generative methods to maximize the actual data distribution. Transfer learning
is mainly combined with pre-training and fine-tuning to extract prior
knowledge from large-scale auxiliary data sets. When domain relevance is
relatively uncommon or large auxiliary datasets are not available, transfer
learning has definite limitations. Meta-learning is currently the mainstream
approach to solve the FSL problem. In recent years, some scholars have
questioned ”Is such a kind of meta or episodic-training paradigm really
responsible and optimal for the FSL problem?”. This has led to extensive
discussions [1, 2] on the necessity of meta-learning for FSL. As for
multimodal learning, it integrates different dimensions of information, such
as language, images, and audio. Multimodal learning is expected to break the
dilemma of insufficient useful information for FSL in the real human
information world.

Due to the specificity of FSL, each method of FSL is confronting multifaceted
challenges to varying degrees. One of the most direct challenges in data
augmentation is that the data samples are too limited and the model cannot
evaluate the true data distribution by relying solely on a few samples. As a
result, the model trained in this setting is biased and easily falls into
over-fitting. In transfer learning, features can effectively alleviate the
problem of FSL, where the volume of data is small and cannot be migrated
across similar domains. Nevertheless, how to represent features effectively,
how to reuse features between different tasks, and how to establish an
effective mapping between data and labels are great challenges that exist in
transfer learning. Moreover, in the meta-learning paradigm, when training the
meta-learner with a set of tasks, it not only samples the data space but also
the task space. By constantly adapting to each specific task, it makes the
network have an abstract learning ability. When the training task and the
target task are distinctly different, the effect of meta-learning is minimal.
Furthermore, in the field of multimodal learning, extensive studies have been
conducted to explore how to effectively integrate information from multiple
modalities to assist the FSL.

Several existing survey papers have investigated the related work of FSL, for
instance, the work [3] categorizes FSL approaches into experience learning and
conception learning. The work [4] classifies FSL approaches into generative
models and discriminative models according to probability distribution.
Lately, the work [5] proposes a new taxonomy to classify the FSL approaches
from the aspect of data, models, and algorithms. Nevertheless, to the best of
our knowledge, no one paper has ever provided a taxonomy from the perspective
of challenges in FSL. By summarizing the challenges of FSL, readers can better
grasp the motivation and principle behind the FSL, rather than being limited
to various models. A list of key acronyms used in this paper is summarized in
Table II.

TABLE II: A List Of Key Acronyms NOMENCLATURE
---
Full Form | Abbreviation | Full Form | Abbreviation
Artificial Intelligence | AI | Few-Shot Learning | FSL
Deep Learning | DL | Machine Learning | ML
Zero-Shot Learning | ZSL | One-Shot Learning | OSL
Neural Architecture Search | NAS | Conventional Neural Network | CNN
K-NearestNeighbor | KNN | Support Vector Machine | SVM
Nearestcentroid Classifier | NCC | Graph Few-Shot learning | GFL
Variational Auto Encoders | VAE | Few-Shot Object Detection | FSOD
Long Short-Term Memory | LSTM | Data Augmentation | DA
Few-Shot Cross-Domain | FSCD | Contrast Learning | CL

###  1.1 Organization of the Survey

The remainder of this survey is organized as follows. Section 2 provides an
overview of FSL, introducing FSL, comparatively analyzing machine learning,
meta-learning, and transfer learning along with summarizing the current
variants of FSL and challenges. Furthermore, to tackle the obstacles
systematically, in this section we demonstrate a new taxonomy to classify the
existing FSL related works. Section 3 to Section 6 present a systematic
investigation on the current mainstream researches from the perspective of
challenges in FSL and provide a comparative analysis from various aspects.
With this taxonomy, a discussion and summary are provided at the end of each
section, giving our insights into the respective fields accompanied by some
potential research opportunities. Section 7 takes computer vision as an
example, counting the latest progress of FSL in image classification, object
detection, semantics segmentation, and instance segmentation in chronological
order. Section 8 delves into exploring the current challenges faced by FSL and
how to seek breakthroughs in each branch. The overall outline of this paper is
shown in Fig. 1.

Figure 1: The conceptual map of the survey.

The main contributions of this survey can be summarized as follows:

  * •

We start with the edge computing scenario, in which the few-shot learning
challenges arise, explaining and clarifying several similar concepts that are
easily confused with FSL. This will be beneficial to help readers establish
the relationship between few-shot learning, transfer learning, and meta-
learning.

  * •

We comprehensively investigate the FSL related work from the perspective of
challenges through knowledge graphs and heat maps. With this taxonomy, we
divide the FSL into several different levels, where the highest level is
multimodal learning that mainly uses various semantic knowledge to assist
judgment, and the second, third, and fourth levels are single-modal learning
that addresses data level, feature level, and task level challenges,
respectively. Notably, we also provide insightful discussions on FSL cross-
domain research, which is currently the more challenging direction in the
field of FSL.

  * •

We investigate an adequate number of papers in recent three years and
summarize the main achievements of FSL in the field of computer vision,
including image classification, object detection, semantic segmentation, and
instance segmentation.

  * •

With these challenges mentioned at the end of the survey, combined with
practical applications, we delve into the current challenges of FSL and
explore how to find breakthrough points in each branch to jointly drive the
research of FSL towards a more practical direction.

  * •

We provide unique insights into the evolution of FSL and identify several
future directions and potential research opportunities concerning each
challenge.

##  2 Concepts and Preliminaries

As a branch of machine learning (ML), FSL is still a young field. What is FSL,
and how does it relate to machine learning, transfer learning, and meta-
learning? What are the variants of FSL that currently exist? What benchmark
datasets frequently appear in research papers? In this section, we will
address the obstacles to FSL for readers by answering these questions.

###  2.1 What Is Few-Shot Learning?

The concept of FSL is inspired by the robust reasoning and analytical
capabilities of humans, and it is widely found in edge computing scenarios. In
2020, Wang et al. [5] give a detailed definition of FSL through experience,
task, and performance of machine learning, which is one of the most recognized
definitions so far: A computer program is said to learn from experience $E$
with respect to some classes of task $T$ and performance measure $P$ if its
performance can improve with $E$ on $T$ measured by $P$. It is worth
mentioning here that $E$ in FSL is very small. In recent years, relevant
neural scientific evidence [6, 7] has shown that innate human abilities are
related to various memory systems, including parametric slow learning
neocortical systems and non-parametric fast hippocampal learning systems,
which correspond to FSL’s data-based slow learning and feature-based fast
learning, respectively.

To better understand FSL, it is necessary to introduce two concepts, one is
$N$-way-$K$-shot problem and the other is cross-domain FSL. The
$N$-way-$K$-shot problem is often used to describe the specific problems
encountered by FSL. In this case, the support set represents the small dataset
used in the training phase, which generates reference information for the
second phase of testing. The query set is the task on which the model actually
needs to predict. Notice that the query set classes never appear in the
support set. Classical $N$-way-$K$-shot represents support set with $N$
categories and $K$ samples per category, then the whole task has only $N$ $*$
$K$ samples. As thus, $N$-way-$1$-shot represents one-shot learning and
$N$-way-$0$-shot represents zero-shot learning. The concept of cross-domain
originates from transfer learning, which refers to the transfer the knowledge
from source domain to target domain. There usually exist domain gaps between
these domains. Cross-domain FSL integrates the features of cross-domain and
FSL, and is a challenging direction that has recently emerged.

At this stage, there still exist many challenges in FSL, which are generated
from various aspects, including but not limited to data, models, and
algorithms. In this context, the challenges can be generally summarized
according to the degree of integration of knowledge as follows:

  * •

`Inaccurate data distribution assessment`: FSL does not have access to large
datasets due to costs, ethical, legal, or other reasons. Consequently, relying
on few samples for learning produces biases in estimating the actual data
distribution, which may be fatal for some tasks. To this end, maximizing the
exploration of data distributions with limited information becomes the most
significant challenge for FSL. Data augmentation is the direct way to address
the inaccurate estimation of FSL. The primary efforts currently focus on
exploring migratable intra-class or inter-class features and customizing
specific images using generators.

  * •

`Feature reuse sensitivity`: Continuous accumulation of a priori knowledge by
sampling large-scale auxiliary datasets. Transfer learning can easily use it
from the source domain to the similar target domain. Pre-training aims to
extract high-dimensional feature vectors through a feature extractor, while
the goal of fine-tuning is to make minor adjustments to the initial parameters
of the pre-training. Transfer learning focuses on the data level and obtains
more valuable features independent of the task by mapping data to labels. It
has an outstanding performance in optimizing specific tasks, but it is
generally limited by the characteristics of current tasks and has a poor
generalization to future tasks. Especially when there is a large shift in the
domain, without filtering and alignment of features may result in negative
knowledge transfer.

  * •

`Generality of future tasks`: Differing from transfer learning, meta-learning
learns to quickly build mappings from known tasks to target models in
previously unseen tasks by double sampling the task and data. In FSL, by
exploring the task space, summarizing meta-knowledge in different tasks can
result in fast aggregation of unseen tasks at a lower cost. As a general
learning framework, meta-learning is independent of specific problems and more
oriented to future tasks instead of optimizing the current one. However, meta-
learning has proven effective only when the testing and training tasks are
relatively similar, and it is highly depends on the network structure and
lacks flexibility. When training meta-learners with a set of tasks at the same
time, it is even difficult to adapt to the distribution of tasks, requiring a
redesign of the network structure.

  * •

`Defects of single-modal information`: It is difficult to learn features
effectively because FSL is inherently information-limited. This situation is
improved to a great extent when aided by getting information from other
modalities. In this respect, semantic assistance [8, 9] is an excellent method
to provide external prior knowledge, where through the introduction or
generation of semantic information as weak supervision, adaptive
classification can be accomplished in conjunction with the original model.

###  2.2 What Is Machine Learning And How It Is Different From Few-shot
Learning?

The traditional Von Neumann computer architecture allows users to execute a
series of instructions step by step in the form of a program [10]. However,
this method does not work in ML. On the contrary, ML uses large-scale datasets
as input. Its judgment on a new sample is based on statistical results
extracted by historical data. Now, the burgeoning of 5G [11] provides massive
connectivity for millions of end devices, enabling an interconnection of
everything. The total amount of data generated by terminal devices is huge,
but the amount of one single data set is extremely small. Hence, traditional
ML, whose performance strongly depends on large data sets, cannot perform well
in this setting with few samples. To this end, FSL emerges and provides a
promising way to handle the data scarcity scenario.

In recent ten years, the research on FSL has been extensively conducted, and
significant research progress has been achieved, e.g. the KGBert [12] proposed
by Alibaba surpasses humans in the field of FSL for the first time. Fig. 2
exhibits the statistics of paper publications related to FSL in recent ten
years based on the statistical results of the Web of Science. As revealed,
there are relatively few related papers from 2011 to 2015 due to that the FSL
theory is still incomplete. With the rise of deep learning since 2015, the
number of FSL related research papers has increased linearly almost every
year. In the past 2020, the number of relevant papers has reached as high as
239, and the number of citations has reached 2731 times, accordingly. Fig. 3
provides a knowledge map covering the hot research topics and cutting-edge
developments in the field of FSL in recent years, including but not limited to
zero-shot learning, one-shot learning, transfer learning, multi-task learning,
and meta-learning. FSL related tasks include feature representation,
visualization, robotics and cross-domain transfer. Among them, domain
adaptation is a widely utilized method for few-shot cross-domain learning.
Computer vision with predominant green color is the most active research
field, including image classification, object detection, semantic
segmentation, and instance segmentation.

Figure 2: The number of FSL-related papers published in prestigious journals
from 2010 to the first half of 2021, excluding citations. Figure 3: The
knowledge graph uses Few-shot learning, One-shot learning and Zero-shot
learning as keywords to relate the main advances and research directions in
the field of Few-shot learning in recent three years.

The most significant difference between FSL and traditional machine learning
is that the set of classes of the support set and the query set are disjoint.
In machine learning, the classes of the test set are included in the training
set in advance. FSL combines the limited supervision information with prior
knowledge to train the model. The input of the model is generally given in the
form of tasks. Through continuously collecting tasks, the model can recognize
the similarities and differences between data as well as task. When the model
encounters an unseen task, knowledge transfer can be accomplished quickly with
just a few iterative training steps with appropriate initialization
parameters. In contrast, traditional machine learning requires optimization
through the loss function generated by a large scale data sets in the model.
In conclusion, FSL is only a very young branch of machine learning, which
mainly addresses the issue of difficult access to quality data sets in machine
learning scenarios.

###  2.3 What Is Transfer Learning and How It Is Different From Few-shot
Learning?

Traditional transfer learning involves applying knowledge learned in the
source domain to a different but related target domain. In FSL, the limited
amount of training data, domain variations, and task modifications are the key
factors that cause the model to fail to transfer well from the source domain
to the target domain. For instance, a medical image dataset with low
similarity to the natural image dataset imagenet is difficult to identify
accurately without the help of relevant expertise, even for a human-guided by
only a few images. Certainly, it is also effective when the source domain and
target domain are relative similar. The end in FSL tasks, if the prior
knowledge is obtained from other tasks or domains by pre-training, FSL can
belong to transfer learning, which mainly learns the mapping of data to
labels.

According to the taxonomy of transfer learning, there are many variants of FSL
problems, including one-shot learning (OSL), zero-shot learning (ZSL), and
cross-domain few-shot learning.

  * •

`One-shot learning`: OSL has only one correct label for each sample in the
support data set, which aims to find the most similar class as a match among
the seen-classes. During the police interrogation, these two processes are
incredibly similar. The witness just looked once at the suspect, and the
photos given by the police can be regarded as the query image. The witness
only needs to answer ’yes’ or ’no’ towards those photos. Similarly, one-shot
learning does not classify the data specifically, but simply makes a cluster
in order of similarity function. According to the existing work, one-shot
learning can be divided into two main approaches. One is to use generative
models to caste prior knowledge [13, 14, 15], where bayesian programming
learning [16] is the most representative framework [17] in this field. Another
method is to convert a OSL classification task into a verification task [18,
19].

  * •

`Zero-shot learning`: ZSL was first proposed by Lampert et al. [20], which
considers a more extreme case in FSL. In the absence of any query samples, the
inference mechanism is solely relied on to identify samples that have not been
seen before. ZSL is essentially done by using high-dimensional semantic
features [21, 22, 23] to replace the low-dimensional raw data. Embedding
representations and autoencoders are the most efficient ways to construct
intermediate semantic spaces, which contains attributes that more
comprehensively define the categories. Up to now, zero-shot learning is one of
the closest methods to human intelligence that discerns previously unobserved
categories. One-shot learning and FSL can essentially be considered as special
ZSL.

  * •

`Cross-domian few-shot learning`: In transfer learning, each class in the
target domain has a sufficiently large number of available samples. When a
large domain shift occurs between the source domains and target domains,
knowledge transfer tends to become very challenging. Cross-domain few-shot
learning combines the challenges of transfer learning and FSL. In the
existence of domain gaps, where the intersection of classes in the source and
target domains is empty, and the available sample size for each class in the
target domain is extremely small. The improvement of the model’s
generalization capability through source domain data alone brings very limited
performance to the model. The present work mainly focuses on the shift
transformation of features and the construction of auxiliary datasets. Cross-
domian few-shot learning can be regarded as one of the most challenging
setting in the field of FSL at present.

###  2.4 What Is Meta-Learning and How It Is Different From Few-shot Learning?

Meta-learning is a general learning paradigm that provides training on tasks
in an episodic-training mechanism. Fig. 4 illustrates the three-steps involved
in meta-learning [24] training. Meta-learning focuses on improving
generalization for unseen tasks using prior knowledge. If prior knowledge is
used to teach the model how to learn on a specific task, meta-learning can be
regarded as a variant of FSL. It is emphasized that meta learning is not
equivalent to FSL. FSL should be seen rather as an ultimate goal. It aims to
achieve robust generations without relying on a large scale of datasets. By
dual sampling of data and task space, meta-learning is enabled to construct a
large number of auxiliary tasks related to the unseen task. Even if some
papers do not use meta-learning, it is likely to improve the model’s
performance if episodic-training mechanism can be considered, such as meta
reinforcement learning [25, 26], meta video detection [27], and so on.

Figure 4: Meta-learning training three-step approach includes: find the
learning algorithm, define loss function using tasks, find parameter that can
minimze loss function.

Nevertheless, meta-learning has its own limitations: when the training and
testing tasks exist obviously domain gap, Meta-learning is rarely used to
initialize parameter weights. It can easily lead to negative migration of the
model. In addition, meta-learning is highly dependent on the structure of the
network, and needs to be redesigned for widely varying tasks. In spite of
this, meta-learning is still one of the most effective methods to solve FSL.

###  2.5 Datasets

Before the availability of FSL benchmark datasets, researchers regularly used
tasks like manually constructing N-way-K-shots to evaluate the performance of
models. However, these simple tasks cannot reflect the complexity of real-
world problems. After 10 years of evolution, the FSL benchmark dataset has
completed the transition from a single domain, single dataset to a cross-
domain, multiple dataset.

Figure 5: There are eight most frequently used datasets in few-shot learning,
including the number of papers on mainstream benchmark datasets (2017-2021).
There may be one paper that tests all mainstream benchmark datasets. Data from
“paperswithcode” platform.

As shown in Fig. 5, during 2017-2021, 898 papers used the CUB-200-2011 [28]
dataset, accounting for 46.6% of the total number of statistics; 587 papers
used the Mini-ImageNet [29] dataset, accounting for 30.5%; and 335 papers
using the Omniglot [30] dataset, accounting for 17.4% ; 44 papers used the
PASCAL-5i [31] dataset, and 46 papers used the Meta-DataSet [32]. The other
specific datasets are Paris-Lille-3D [33], N-Digit MNIST [34], SUN397 [35],
which are used in 15 papers in the past five years. In terms of quantity, the
CUB-200-2011, Mini-ImageNet, and Omniglot benchmark datasets occupy a dominant
position in the field of FSL. Table. III compares the datasets mentioned above
from different dimensions. By the publication of the article, a more objective
benchmark dataset [36] for evaluating the cross-domain ability of FSL was
proposed. 1) CropDiseases [37], a plant diseases dataset , 2) EuroSAT [38], a
dataset for satellite images, 3) ISIC [39] a medical skin image dataset, 4)
ChestX [40], a dataset for X-ray chest images. The similarity comparing to
MiniImageNet is decreas across these datasets.

TABLE III: The latest performance of FSL in the main tasks of machine vision

Dataset Variant | Leader | Numbers/Classes | Train/Test | Content | Main FSL Task | License
---|---|---|---|---|---|---
CUB-200 | Wah el al. [28] | 11788/200 | 5994/5794 | Birds | Few-shot Image classifition | Attribution 4.0
Mini-ImageNet | Vinyals et al. [29] | 600/100 | 480/120 | Real scene | Few-shot Image classifition | MIT
Omniglot | Lake et al. [30] | 32460/50 | 4800/1692 | Character | Few-shot Image classifition | MIT
PASCAL-5i | Shaban et al. [31] | 576/20 | 11530/- | Really scence | Few-shot Image classifition | MIT
Meta-Dataset | Triantafillou et al. [32] | 10 datasets | -/- | Really scence | Few-shot Image classifition | Multiple licenses
BSCD-FSL | Guo et al. [36] | 4 datasets | -/- | Really scence, Satellite and medical image | Cross-domain few-shot learning | MIT
Paris-Lille-3D | Roynard et al. [33] | 450000000/50 | 450000000/380000000 | Point cloud | Few-shot Semantic Segmentation | CC BY-NC-ND 3.0
N-Digit MNIST | Oh et al. [34] | -/- | -/- | Character | Metric Learning | Apache License
SUN397 | Oh et al. [35] | 108,753/397 | 76128/21750 | Really scence | Few-shot Image classifition | -

###  2.6 Taxonomy

According to the degree of integration of knowledge, FSL is broaderly divided
into a single-modal learning and a multi-modal learning. In this survey, The
single-modal learning can be further divided into data augmentation, transfer
learning, and meta learning. It mainly focuses on abstracting or transferring
limited information into higher-level feature vectors or meta-knowledge.
Multimodal learning is more close to the real world of human intelligence,
which no longer relies on the limited sample, and tries to find the space of
other modalities to assist the FSL. With this taxonomy, we exhaustively review
and discuss each method. Fig. 6 vividly demonstrates the FSL’s taxonomy under
the challenge perspective.

Figure 6: The entire taxonomy is presented in the form of a pyramid. The
bottom level represents the ”cloud-edge-terminal” edge computing scenario,
which is characterized by few-shot real-time computation under high traffic.
Based on this, the challenges of FSL are classified into four levels according
to the degree of integration of the required knowledge. Among them, the
challenges represented by data augmentation, transfer learning, and meta-
learning are single-modal challenges.

  * •

`Evaluate The True Data Distribution`: The key to the difficulty of FSL is
that limited samples cannot reflect the actual data distribution. The most
intuitive idea of machine learning is to generate additional data based on a
certain probability model or to extend the auxiliary data set using a large
volume of unlabeled data from extending data. Existing work focuses on
exploring feature differences that can be learned between classes or with
external datasets at the semantic level. Handcrafted rules and automatic
learning data processing are the two main approaches at this stage.

  * •

`Build Data-To-Label Mappings`: Furthermore, if a large number of features
from the benchmark dataset can be reused, which will significantly reduce the
pressure of the model on the data. Pre-training and fine-tuning assist FSL by
learning effective representation of data to labels, coupled with effective
regularization of the underlying semantic features. In particular, the pre-
training stage learns the optimal initialization parameters in a variety of
different tasks, and the fine-tuning stage freezes most of the lower-level
parameters and retrains only the parameters of the classification layer.

  * •

`Derive Task-To-Target Model Mappings`: Fine-tuning already has a good
performance in baseline models with small samples. Nevertheless, in multi-task
learning, a large scale of tasks are learned just as one task, which leads to
a terrible generalization of the model. In contrast, meta-learning dual-
samples the data and task space using episodic-training mechanism, finding
latent associations between different tasks and thus having a good description
of the whole task space.

  * •

`Complementary Learning With Limited Information`:Multimodal learning has been
proposed in deep learning for a long time, but it has only started to be
integrated with FSL in recent years. Information in multimodal dimensions is
closest to the real human information world, and it compensates to some extent
for the inability of FSL to make accurate assessments of data distributions in
a single modality. Pixels, semantics, and sounds can be supervised signals for
FSL tasks, and even more recently unsupervised learning has been used to
explore more robust feature representations using contrast learning.

##  3 Data Augmentation To Evaluate The True Data Distribution With Maximum
Probability

In real-world FSL tasks, the number of samples in the support and query sets
is usually limited due to privacy, collection costs, and labeling costs. To
mitigate this issue, data augmentation is recognized as the most direct way to
increase the sample richness in FSL. Nevertheless, the core risk of the FSL
data augmentation is how likely the augmented dataset can evaluate the
distribution behind the real data. Based on whether the data augmentation
techniques can be reused on other tasks, FSL data augmentation is divided into
hand-crafted rules and automatic learning data processing.

###  3.1 Hand-Crafted Rules

Hand-Crafted rules require guidance from experts with specialized domain
knowledge. A representative result is that Bouthillier et al. proposed to
randomly discard pixels [41] on a random rectangular area to generate black
rectangular blocks of simulated noise. Similar operations also include random
erase [42] and fill [43, 44] in FSL. Nevertheless, briefly relying on the
simple transformation of single-sample pixels cannot prevent the risk of
overfitting. Further, the hand-crafted rules contains data level and feature
level according to the dimension of information. Table. IV summarizes the data
augmentation methods for hand-crafted rules making.

TABLE IV: The latest performance of FSL in the hand-crafted rules.

Model | Core View | Key Approach | Experimental Dataset | Using External Dataset | Data Level | Feature Level
---|---|---|---|---|---|---
FTT [45] | Enriching Instant Attributes | Places-CNN | Transient Attributes Database | ✔ | ✔ |
CSEI [46] | Erase Repair | Metric based | miniImageNet | ✗ | ✔ |
Image Deformation [15] | Semantic Invariance | Meta-learning | miniImageNet | ✔ | ✔ |
AdarGCN [47] | Denoising the collected web images | GCN layer | - | ✔ | ✔ | -
| Covariance-Preserving Adversarial
---
Augmentation Network [48]
| ”Variability” of covariance information
---
as base instances
Generative Adversarial Network | ImageNet | ✔ |  | ✔
Spot and Learn [49] | extracts varying sequences of patches | reinforcement learning | miniImagenet | ✗ |  | ✔
Saliency-guided Hallucination [50] | Background - Prospective Learning | Realation network | miniImagenet | ✗ |  | ✔
Laso [51] | Explore the reliable differences between labels | Transfer learning | MS-COCO | ✔ |  | ✔
Dual TriNet [52] | Semantic Synthesis Example | Auto Encoder | MS-COCO | ✗ |  | ✔

Figure 7: FSL data augmentation based on data level mainly includes internet
data collection, environment variation, difference transfer and random crop
filling. Here a picture of a panda is used as an example to implement the
above variation.

####  3.1.1 Data Level

The data level augmentation is mainly a transformation of the input data that
aims to scale up existing data by making modifications to the data marginally
for achieving diversity in model input. Random erasure [42] and random
cropping [43, 53] are classical algorithms by simulating the images with
different degrees of damage and thus improving the generalization of the
model. Inspired by this, Li et al. [46] discarded the traditional approach
based on a entire feature extractor for images and instead focused on local
patch images. These methods require large-scale datasets as support. It is not
easy to achieve in the FSL settings. Conversely, CSEI [46] does not require
extra data sets. The specific operation is to erase most of the discriminatory
area in the support set derived from the metric function and replace it with
an image fill using the restoring operation. FTT [45] enriches the data set by
linear interpolation of some transiently transformed attributes, such as
different weather and lighting. Z Chen et al. [15] proposes an end-to-end
approach to partitioning images as a whole inspired with the idea of MIXUP
[54], which argues that images preserve important semantic information even
after they have undergone various distortions. The most significant difference
between image distortion and GNN is that image distortion simply stitches two
images together in a linear pattern. This method is able to achieve maximum
deformation without loss of classification. In addition, it is a good
direction for data expansion by using the large amount of unlabelled data sets
in the real world for supplementation. Finally, when both the source and
target classes both have only a limited number of samples, AdarGCN’s [47]
implementation crawls data from internet resources and automatically removes
irrelevant noise to achieve controllable data augmentation. At the same time,
AdarGCN can automatically determine how far the information has been
propagated in each graph node. In conclusion, data augmentation at the data
level focuses on increasing the number of samples by means of pixel
transformations and pixel generation. Fig. 7 shows the main methods based on
data level under hand-crafted rules.

Figure 8: Feature level-based data enhancement can be mainly divided into
global features and local features. Global features focus on the whole image,
including the foreground and background. Local features, on the other hand,
selectively focus on the subject part in the foreground.

####  3.1.2 Feature Level

Feature level data augmentation mostly maps pixel information into a high-
dimensional latent space. It carries more valid information than mere original
pixels. Gao et al. [48] first explored the underlying distribution behind few-
shot data and proposed an adversarial covariance augmentation network to
overcome the limitations of FSL. Its experiments have shown that relying
solely on learning the features of the entire image brings noise into the
results. Chu et al. [49] tried to compute feature representations for each
patch, rather than the entire image. Each small patch is connected by RNN and
the features of the image are further fused. This heuristic algorithm is far
superior to simple attentional models[55]. Zhang et al. [50] explain partial
feature learning from another perspective, proposing to use a pre-trained
model to decompose visual features into three parts and then select the
original, foreground, and background images to be re-stitched into new visual
features. Similarly, Laso [51] explores the differences in features between
different datasets in a high-dimensional space. Combining different labels
through the intersection and complementation of sets allows images to contain
key information from multiple classes at the feature level simultaneously.
Training this part of the image as a support set can significantly improve the
classification performance of small samples. Chen et al. [56] go further by
extending the features to a high-dimensional semantic space. In FSL, feature-
level augmentation is more effective than data-level augmentation by modeling
the valid information in a compressed manner. Fig. 8 shows the main methods
based on features level under hand-crafted rules.

Figure 9: Learned Data Processing aims to learn a policy generator in multiple
task spaces so as to automatically match different tasks. Its biggest benefit
over hand-crafted rule is that it can be reused.

###  3.2 Learning Data Processing

In 2018, data augmentation entered the area of auto augmentation with the
maturation of meta-learning. Through the combination of meta-learning with
other data augmentation methods, a large amount of excellent work emerged
during this period. Hu et al. [57] was inspired by the DARTS algorithm to
abstract the data augmentation into multiple sub-strategies, each with a
certain probability of being selected according to the different few-shot
tasks. In addition to the probability-based method, another approach is based
on generation. Li et al. [58] proposed the adversarial feature phantom
network-AFHN. The phantom diversity and discriminative features are
conditional on a small number of labelled samples. Chen et al. [56] attempt to
train a meta-learner and generate a network to learn similarities and
differences between images end-to-end by fusing pairs of images. MetaGAN [59],
on top of which an adversarial generator conditional on the task is
introduced, which helps FSL tasks form generalizable decision boundaries
between different classes. On the other hand, Zhang et al. [50] further
demonstrate the usefulness of phantom data generation for FSL and propose a
low-cost automated data generation method that uses a direct foreground-
background combination to generate feature space-level data for training. In
addition, it is also effective to explore the migratable differences between
and within classes in support datasets. Delta-encoder [60] uses auto-encoder
[61] to learn differences in the same class for transfer learning, which is
different from metric-based computation [62] of visual similarity. Table. V
summarizes the data augmentation methods for learning data process. Fig. 9
shows the main approaches involved in auto-learning data processing under FSL.

TABLE V: The latest performance of FSL in the field of learn data process.

Model | Core View | Key Approach | Experimental Dataset | Using External Data
---|---|---|---|---
DADA [57] |  | Automatic generation of
---
enhancement policies
Gradient Descent | CIFAR-10 | ✔
AFHN [58] | Condition-based generation | Generative Adversarial Network | MNIST | ✔
MetaGAN [59] | Generate extral data | Generative Adversarial Network | MNIST | ✔
Delta-encoder [60] | Differential transfer | Auto Encoder | Mini-ImageNet | ✔
MSFN [62] |  | Calculate multi-scale features
---
and the similarity of
each class of labels
Metric Learning | Omniglot | ✔

###  3.3  Discussion and Summary

To maximize the evaluation of the distribution of the real data in FSL
setting, data augmentation has from the hand-crafted rules to the auto learned
data processing stage. The watershed is the maturity of meta-learning in 2018.
This section comprehensively investigates the emerging representative
technologies in the field of data augmentation and reviews the evolution of
few-shot data augmentation. Table. V summarizes the model in different
dimensions clearly.

##  4 Transfer Learning Builds Data-To-Label Mappings For Specific Problems

Transfer learning [63] is a classical learning paradigm, which aims to solve
the challenging problem that there are only a few or even no labelled samples
[64] in the FSL [65]. Feature reuse is the core idea of transfer learning to
solve FSL absence of data setting. The basic operation is to pre-train the
model on an extensive dataset and then fine-tune on the limited support set.
When source and target domains exist a large gap, knowledge transfer is
invariably much less effective. This cross-domain setting brings a new
challenge for FSL. In FSL, transfer learning can be broadly divided into pre-
training and fine-tuning stage, which can also be referred to the baseline.
Fig. 10 illustrates the general process.

Figure 10: Transfer learning can be divided into pre-training and fine-tuning
stages, where the baseline model can be combined with other techniques to
improve the model performance.

###  4.1 Pre-training and Fine-Tuning

From 2012 to 2018, a large number of excellent works have emerged in the field
of computer vision and natural language processing, such as MobileNet [66],
ResNet [67], ELMO [68], GPT [69], and BERT [70]. In particular, the area of
natural language processing was slow to progress before the advent of pre-
training models. It has grown considerably under the leadership of BERT as
computing power has increased and excellent pre-training models have been
proposed.

As a downstream task, how to use these excellent models to obtain features
will largely alleviate the pressure on the data for FSL. Especially for few-
shot image classification, as a pre-training model [71, 72], it needs to use
an external large-scale label dataset to extract prior knowledge from similar
task. The most common practice is to design a backbone model without
classifier layer, which includes convolutional neural networks or auto-
encoders. The input of the model is an array of images, and the output is the
feature vector embedding in a high-dimensional space [73]. High-dimensional
feature vectors obtain sufficient valid semantic information about the target
image. After the pre-training was put forward, the researchers proposed fine-
tuning later. Most parameters in the pre-training are frozen, and only the
classification layer parameters are updated in the testing stage. Many recent
works [74, 1] have proved that fine-tuning can improve the 5-way-1-shot tasks
accuracy rate by 2%-7% compared with the baseline model. Although the number
of samples in the support set and query set is small, pre-training and fine-
tuning are still very helpful for improving the accuracy of FSL. The
conclusions are analogous in natural language processing as well. The authors
in [75, 76, 77] have also shown that fine-tuning can be embedded in state-of-
the-art meta-learning or semi-supervised learning frameworks for optimizing
model parameters.

Dhillon et al. [76] replaced the standard activation function with cosine
similarity, and Nakamura et al. [74] replaced conventional gradient descent
with an adaptive gradient optimizer, which both improve the fine-tuning
process in the accuracy of the model. Currently, fine-tuning is usually
combined with meta-learning. Cai et al. [78] attempted to integrate them to
train networks with specific layers. However, experimental results suggest
that since the support and query sets do not overlap in FSL setting,
transferring whole knowledge from the source dataset is not the best solution
for FSL. Shen et al. [79] suggest that knowledge should be transferred
specifically for parts. The degree of transferability needs to be controlled
by freezing or fine-tuning specific layers in the backbone model. Similarly,
fine-tuning can also be used to prevent new classes of networks from polluting
the feature space of the basic classes. Up to now, the FSL and fine-tuning
have been widely used in tasks like plant disease and insect pest
identification [80], road detection [81], and automatic question and answer
[82].

###  4.2 Cross-Domain Few-shot Learning

The latest progress of FSL largely depends on the labelled data of the
training stage. However, it is unrealistic to collect various forms of
datasets for specific tasks in many practical applications, which results in
challenging of FSL between intensely different domains. Cross-domain few shot
learning integrates FSL and domain adaptive problems, which is a relatively
comprehensive and challenging setting. For a long time, the benchmark datasets
commonly used for FSL have suffered from a standardized dataset structure and
large similarity of natural scenes, which leads to that models perform well on
standard datasets but get unacceptable results in the real world task. Google
first released a FSL cross-domain dataset named Meta-Dataset [32] in 2020,
which includes a total of 10 public image datasets including ImageNet,
CUB-200-2011, etc. Yet these datasets are still focused on natural scenarios
and cannot be broadly regarded as cross-domain few-shot benchmark datasets.
Until the availability of BSCD-FSL [36] datasets. According to the degree of
similarity with the ImageNet, it is divided into CropDiseases [37], EuroSAT
[38], ISIC [39], ChestX [40]. The authors extensively evaluate the performance
of current FSL methods, and experiments show that the accuracy of all methods
is correlated with the proposed natural image data similarity metric.
Nowadays, cross-domain FSL focuses on distinguishing domain-irrelevant
features and domain adaptive techniques with transfer learning.

The objective of domain adaptation is to transfer knowledge from the source
domain to the target domain, which has the same set of classes but a different
data distribution than the source domain. Recently, much work has used
adaptive networks to align their features with a new domain or to select
domain-irrelevant features from multiple backbone model. Dvornik et al. [83]
obtained multiple domain representations separately by training a set of
feature extractors with different domains. Setting the model to a dataset with
multiple domains during training allows an attempt to migrate to other domains
during the testing stage. Nevertheless, this approach may not be effective
during the meta-training and meta-testing phases when the domains are
orthogonal. Based on this, FRN [84] explores the potential space for few-shot
image classification, using ridge regression to reconstruct and normalize the
feature map without adding new learning parameters. FWT [85] utilizes only the
source data for the affine transformation of features, as do LRP-GNN [86] and
SBMTL [87]. FD-MIXUP [88] constructs auxiliary datasets by mixup and uses
encoders to learn domain-irrelevant features to guide the network
generalization to other tasks. STARTUP [89] takes advantage of not only the
source data but also assumes that the model has access to a lot of unlabeled
target data during training. A large amount of unlabeled data is used to
enhance the generalizability of the model to other domains. Metric-based
approaches are frequently used for semi-supervised and unsupervised cross-
domain FSL. A recent paper by Lu et al. [90] uses attention as a metric
strategy to reweight and combine domain-specific representations. Chen et al.
[72] based on a meta-baseline by pre-training the classifier on all base
classes and classifying a small number of samples based on the nearest
centroid algorithms for meta-learning, which greatly surpasses the latest
state-of-the-art methods. Li et al. [91] inspired by [92, 93], proposes to map
domain-specific features to the same shared space, thus achieving a domain-
irrelevant universal representation.

###  4.3 Discussion and Summary

While meta-learning methods have higher performance than transfer learning in
standard FSL settings, the situation is reversed in cross-domain FSL settings.
A newly published paper recently pointed out that the improvements from fine-
tuning and pre-training are similarly very limited when the domains appear
orthogonal. In the pre-trained feature space, the base classes form compact
clusters, while the new classes are distributed in large difference groups.
Currently, the actual deployment of trained models into production
environments is often not adapted to rapidly changing environments. Pre-
training can be seen as a task with many learning classes, but it is only a
single learning task.

##  5 Meta-learning Derive Task-To-Target Model Mappings Independent Of
Specific Problems

Meta-learning learns historical prior knowledge from a dual sampling of data
and tasks, then extracts meta-knowledge to apply to future tasks. Meta-
learning is independent of the specific problem, and exploring an optimal
initialization parameter in task space, discarding the task-independent
feature representation under traditional supervised learning. Up to now, most
of the meta-learning models are updated with parameters using traditional
gradient descent. Absolutely, there are also non-gradient descent methods
based on reinforcement learning and metric methods. In FSL, meta-learning can
be used to automate the learning of model parameters, metrics function, and
the transfer of information

###  5.1 Learning Model Parameters

Most of the deep learning frameworks use different parameter initialization
methods, such as uniform distribution, normal distribution, and so on. The
biggest problem with this random initialization is that it easily falls into
the local optimal position. The goal of meta-learning is to train a
hyperparameter generator, the classical methods being MAML [94], Repital [95]
even their derived variants. MAML identifies the global optimization direction
by calculating the optimization direction for each task. Compared to MAML,
Reptile can update fewer parameters at once. The biggest difference between
meta learning and multi-task learning is that multi-task learning only focuses
on the performance of the current task. Meta-learning was demonstrated to
perform better than transfer learning with a standard FSL benchmark dataset.
Nevertheless, meta-learning is more sensitive to network structure and
requires fine tuning of hyperparameters. After that more versions have evolved
to address these issues separately. Such as MAML++ [96], First-order MAML
(FOMAML) [95], Meta-SGD [97], TAML [98], iMAML [99], iTMAML [100]. Of which
Meta-SGD, expect MAML, finds the optimal learning rate and update the
direction of the parameters at the same time, in addition to learning the
initialization parameters. TAML [98] is a task-independent method, which
overcomes the problem that MAML can only use an external model. Subsequently,
IMAML [99] proposes a new loss function and a corresponding method for
computing the gradient, making it possible to obtain the gradient of the
parameters by calculating only the solution of the loss function, without
caring its specific optimization method. iTMAML [100] based on TAML, which
implements automatic task recognition. It can be quickly adapted to new tasks
by updating when the data is in a continuous state. At present, MAML has been
widely used in various tasks [101, 102, 103], producing different variants.
Table. VI distinguishes between MAML, Reptile and their variants in various
perspective.

TABLE VI: Summary MAML, Reptile and their variants

Model | Directions for improvement | Key Approach | First order gradient | Two-step gradient
---|---|---|---|---
MAML [94] | Original | Inner-loop+outer-loop | ✗ | ✔
Reptile [95] | Computational Complexity | Standard stochastic gradient descent | ✔ | ✗
FOMAML [95] | Simplify secondary gradient updates | Using the gradient calculated from the previous task | ✔ | ✗
Meta-SGD [97] | Increasing the Volume of the Model | Increase the learning rate vector parameter | ✗ | ✔
TAML [98] | Task unbiased estimation | Introducing Entropy and Inequality Metrics | ✗ | ✔
iMAML [99] | Gradient disappearance | Propose new loss functions and optimization methods | ✗ | ✔
iTMAML [100] | Automatic task identification | Data is in continuous state | ✗ | ✔

Learning optimizers are another important direction for learning model
parameters. LSTM as the base optimizer [104, 105], which accepts the
difference at time $t$ and the hidden state of the meta-network at time $t-1$.
The output of the original network is an updating of the model’s weight and
bias. In 2016, Xu et al. [106] proposed the BPTT to supervise LSTM training.
It is notable that this is performed in the context of supervised learning.
What update should be required to the optimization if it is in the setting of
unsupervised and active learning? Inspired by this, there has been a long
period of work focusing on reinforcement learning [107], Bayesian inference
[108] and evolutionary algorithms [109] in an attempt to automatically find
optimization strategies through heuristic algorithms.

Finally, traditional Neural Architecture Search (NAS) also incorporated the
idea of meta-learning and adapted it accordingly under FSL. To our best
knowledge, the shared [110] and randomly selected supernet weights [111, 112]
were early solutions for FSL. Recently, a large volume of work [112, 113, 114]
has shown that performance differences still exist between one-shot NAS and
traditional NAS. The one-shot NAS uses weight-sharing networks to train the
supernetwork only once and then perform a single round of inference to get an
accurate prediction, greatly reducing the amount of computation required for
the experiment. Subsequently, Zhao et al. [115] proposed the few-shot NAS
based on one-shot NAS. The core idea is to divide the supernets into multiple
sub-supernets to search different regions of the search space. With a slight
increase in the number of supernets, the accuracy of few-shot NAS is greatly
improved. MetaNAS [116] is the first method that completely integrates meta-
learning and traditional NAS. MetaNAS be capable of better initialization
parameters with the help of meta-learning ideas. It completely replaces the
weighted summation in the DARTS algorithm to reduce different operations, and
the experimental results also show that it is more adaptable to more
downstream learning tasks.

###  5.2 Learning Metric Algorithm

Metric learning [117] is different from classical meta-learning, metric
learning no longer divides the model into training and testing stages. In many
previous papers [4, 5, 3, 118], metric learning is always introduced
separately. In our context, metric learning will be explained under the frame
of meta-learning. Fig. 11 illustrates one of the most representative learning
methods, which is based on a prototype network that has been improved to
obtain substantial improvements on a benchmark dataset for classification
tasks.

The siamese neural network [119] is a relatively early model in the metric
learning. It can be simply regarded as a binary classification problem. The
input to the model composes of a set of positive or negative sample pairs, and
the model needs to evaluate the similarity of the images during inference
stage. Triple loss [120] is another way to deal with more than pairs input in
FSL metric learning. Contrary to the Siamese neural network, triple loss
requires positive samples, negative samples, and anchor samples to be
available at the same time. If training samples are easily distinguished from
each other, this would not be beneficial for the model to better learn
discriminative features. The hard sample selection technique [121]
incorporates the absolute distance between positive sample pairs in addition
to considering the relative distance between positive and negative samples. In
addition to this, Li et al. [122] revisited the classical triplet network and
extended it to a K-tuple network for FSL.

Compared to the Siamese neural network, the prototype network [123] realizes
the true meaning of classification. The most significant difference is that
the model allows for more data as input. By feature averaging it is feasible
to find the most representative sample as a prototype. However, simple feature
averaging is easily disturbed by noise. On this basis, many works [124, 125,
126, 127, 128] have explored how to make the distance between prototypes
larger and larger. One of the most representative works is the proposal of
positive and negative margins [129], which further reduce the over-fitting and
enhance the generalization based on maximizing the discriminative ability of
the model.

Figure 11: The framework [128] performs end-to-end learning of the embedding
model and prototype learning jointly, and the learned embedding features are
used to compute the distance between the query image and the prototype,
pushing the distance between different classes farther and bringing the
distance between the same classes closer.

Matching Networks [29] is a more general network framework that maps few-shot
datasets and unlabeled data to vectors in the embedding space. The matching
network combines the best features of parametric and nonparametric models of
the nearest neighbour algorithm to model the sample distance distribution by
learning the embedding representation. Experiments have proved [130] that
embedding propagation produces a smoother embedding manifold. How to learn
high-quality embedding representations in a limited time is substantial for
improving the model’s accuracy. GVSE [131] fuses visual embeddings, semantic
embeddings, and gating metrics automatically balances the relative importance
of each metric dimension by the model. Subsequently, Arvind Srinivasan et al.
[132] proposed a new architecture to improve Inception-Net, U-Net, Attention
U-Net, and Squeeze-Net, which takes the time to generate embedding quality as
a cost. The processing based on the embedding representation plays a vital
role in FSL.

The relational network [133] differs from the three models mentioned above in
that its similarity is calculated by using a neural network. In contrast to
the Siamese neural networks and prototype networks, relational networks can be
seen as providing a learnable nonlinear classifier for determining
relationships. The classifier can be a feature extractor of a pre-trained
neural network [122] or a multiple embedded module [134]. The most significant
contribution of the relational network is that it breaks away from a single
linear metric function and explores the use of an alternative model to
generate similarity. Table. VII categorizes each of the representative metric
learning algorithms, comparing their innovations on the original approach.

TABLE VII: A Summary of Metric Learning by base approach.

Model | Key idea | Metric function | Improvement
---|---|---|---
Siamese Neural Network [119] | A pair of inputs | Cosine | Original
Triple loss [120] | Triple input | Cosine |  | Maximize intra-class distance and
---
minimize inter-class distance
E-nagivate sample [121] | Difficult sample training | Cosine | Compare more samples at once
K-tuple network [122] | K inputs | Cosine | Compare more samples at once
Prototype Network [123] | Prototype representation | European | Original
Negative Margin Matters [129] | Negative margin loss | Cosine | Balancing discriminative and migratory
Attentive Prototype [128] |  | Consider spatial association
---
between features
European | Weighted summation to obtain the prototype
SEN [135] | Feature normalization | European |  | The modal length of the constraint feature
---
approximates the modal length of the prototype
Prototype Rectification [136] |  | Modifying prototypes with
---
query sets
Cosine | Consistent distribution of query sets and support sets
Matching Network [29] |  | Attention mechanism to
---
access the memory matrix
Cosine | Original
GVSE [131] |  | The relative importance of the
---
automatic balancing model for
each metric
Cosine |  | Fuses visual embeddings, semantic embeddings,
---
and gating metrics
Optimization of image embeddings [132] | Monitoring embedded quality | Manhattan | Improved quality of embedding
Relational Network [133] | Using models to compute similarity | Model | Original
Revisiting metric learning [122] | Using a simple but powerful baseline | non-linear distance | Proposing a deep K-tuplet network
BSNet [134] | Simultaneously by two similarity measures | Euclidean and cosine |  | Learning feature maps based on the similarity
---
of two different features

###  5.3 Learning To Transmit Information

It is proved that graph neural networks (GNNs) [137] have performed well on
relational-based tasks in recent years [138]. Researchers have found that its
classes-based transfer of information can work well to help FSL learn to
identify new class, while avoiding these classes being dominated by
proprietary features. Primarily, early graph neural networks simulate the
propagation of weights between different nodes by creating full connections
between support and query sets. The nodes can be represented by either a one-
hot encoding or an embedding vector, and the connections between nodes can be
passed through edges. Given the complexity of graph neural network algorithms,
most graph neural networks currently have a shallow number of layers. In order
to better accommodate FSL, graph neural networks have been uniquely designed
with nodes and edges in recent developments. Fig. 12 shows a recent
representative algorithm for FSL of graph neural networks from the perspective
of exploring small sample distributions.

Figure 12: DPGN [139] is concerned with the relationship between samples in
GNN, in addition to the relationship between sample distributions. Where Point
Graph is used to describe the samples and Distribution Graph is used to
describe the distribution. The two GNNs fuse the instance-level and
distribution-level relationships by passing information.

The EGNN uses vertex sets, edge sets, and task sets to encode the labels of
nodes. When updates occur between nodes, both the similarity and the
difference are considered, which greatly improves the generalization
performance of graph neural networks to FSL. Meta-GCN [140] further
incorporates the idea of meta-learning, which enables the updating of weights
of graphs under FSL to also be optimized according to the gradient descent
steps, the whole process requires very few gradient steps and can receive new
data quickly. Subsequently, several models based on improvements in the graph
structure itself have emerged. The prototype network was improved by GFL [141]
network that focuses on learning small samples of data with a graph structure.
DPGN uses a dual graph neural network that describes samples while modelling
their distribution. Furthermore, GERN [142] uses embedding of graph neural
network connections to achieve more robust intra-class weight transfer.
Nevertheless, none of these approaches addressed the problem of shallow layers
of graph neural networks until 2021, HGNN [143] designed three sections of
bottom-to-top, and skip connections to remove the pitfall of ordinary GNNs
losing the hierarchical association between nodes. Based on this, Frog-GNN
[144] uses multidimensional information to synthesize information about the
adjacency between nodes to form pairwise relational features of intra-class
similarity and inter-class dissimilarity. At present, graph neural networks
are widely used for tasks such as few-shot image classification [145, 146],
semantic segmentation [147] and instance segmentation tasks.

###  5.4 Discussion and Summary

In FSL, meta-learning mainly explore the mapping from the task to the target
model. It trains a super-tuning device that gives a good set of
hyperparameters as it converges according to the different tasks. In contrast
to multi-task learning, which learns only focus on single task. However, meta-
learning is not universal for all conditions. The current idea of meta-
learning is to have enough historical tasks. If there are not enough tasks on
certain problems, then meta-learning may not be able to solve those problems.
Similarly, if the domain gap between source and target is too large, the
results will also become terrible.

##  6 Multimodal Complementary Learning Of Small Samples With Limited
Information

Until now, FSL has made significant progress in the unimodal domain. Within
unimodal learning, models are primarily responsible for representing
information as feature vectors that can be processed by a computer or further
abstracted into higher-level semantic vectors. Particularly, multimodal
learning in FSL refers to learning better feature representations by
exploiting complementarities between multiple modalities and removing
redundancies between modalities. In real life, when parents teach their babies
about things, they always include general information along with semantic
descriptions. This is crucial for FSL, which inherently comes with little
valid information to make a good evaluation of the data or feature
distribution. Inspired by this, many research works [8, 148, 149] consider the
introduction of other modal information when solving FSL. By fusing multimodal
information, the ability of the model to perceive small sample data can be
improved. Fig. 13 shows the main paths of FSL under multimodality,

Figure 13: Multimodal FSL scenarios, how to effectively model other modal
information under the condition of feature representation by fusion,
alignment, and assistance to compensate for the lack of valid information in
itself. TABLE VIII: The challenge of learning from small samples in
multimodality.

Approach | Representation | Alignment | Fusion | Co-Learning | Translation
---|---|---|---|---|---
Wang et al. [8] | ✔ |  |  |  |
Li et al. [148] |  |  |  | ✔ |
Eli et al. [150] |  |  | ✔ |  |
Peng et al. [151] | ✔ |  |  |  |
Schonfeld et al. [149] |  | ✔ |  |  |
Wang et al. [152] |  | ✔ |  |  |
Pade et al. [153] | ✔ |  |  |  |
Fortin et al. [154] |  |  |  | ✔ |
Zhang et al. [155] | ✔ |  |  |  |
Sharma et al. [156] |  |  |  |  | ✔
Akata et al. [157] | ✔ |  |  |  |
Elhoseiny et al. [158] |  |  |  |  | ✔
Zhu et al. [159] |  |  |  | ✔ |
Xian et al. [21] |  |  | ✔ |  |
Pahde et al. [153] |  |  |  |  | ✔

###  6.1 Multimodal embedding

Recent works [9, 148, 149, 150, 160] proved the limitations of visual features
for FSL of certain tasks. Semantic space as auxiliary information can provide
effective context for visual features and help FSL. Experiments have shown [8,
148, 149] that adaptive combinations of two or more modalities are much better
than unimodal FSL. Wang et al. [8] constructed weak semantic supervision for
each category by integrating multiple visual features. Schonfeld et al. [149]
instead used variational autoencoders (VAEs) to model semantic features based
on latent visual features. Subsequently, Schwartz et al. [150] and Peng et al.
[151] further extended the semantic information by adding classes labels,
attribute and natural language descriptions, and knowledge inference. The
additional semantic information is aligned with visual features by embedding
loss functions [152] to largely reduce the cost of knowledge transfer. Based
on this, Karpathy et al. [160] used multimodal alignment to find potential
correspondences that exist between image patches in the training set images
and their descriptive utterances. Aoxue et al. [148] went further by using
semantic information to model classes as hierarchy.

###  6.2 Generate semantic information from images

In addition to this, another related area using multimodal FSL is text-to-
image generation. In few-shot visual classification tasks, the visual and
semantic-based approaches [153], which try to use textual descriptions to
generate additional training images, have a considerable advantage. Pade et
al. [153] used generative adversarial networks as data generators to train the
model, which can purposefully generate corresponding visual features based on
semantic information, and enhanced visual features can be obtained by
combining the original visual features. Zhu et al. [159] and Xian et al. [21]
explored generative images and feature vectors, respectively, making promising
progress in the field of ZSL.

Similarly, Fortin et al. [154] migrated text-to-image generation to the target
detection task, which can be integrated with current FSL to implement a more
general module in the contextual joint learning phase. Zhang et al. [155]
improved the resolution of the generated images by concatenating two CGANs
based on ordinary generative networks. The first subtask generates a
relatively blurred image from the text, and the second subtask generates a
high resolution image from the blurred image. Eventually, the model will use
more details to generate images. Nevertheless, sometimes text descriptions
contain multiple targets and a single text description does not capture all
the details in an image. Sharma et al. [156] provided a dialogue interface
that uses textual information from the dialogue to obtain more detailed
information about the image.

Another set of text-to-image algorithms is based on the variant auto-encoder
with embedding. Unlike the generative approach, the input to the encoder is a
vector of attributes. Akata et al. [157] explored semantic features from
different sources, such as WordNet and word embeddings. However, these methods
were unable to recognize parts of an image without part-term annotation.
Elhoseiny et al. [158] used a visual classifier to detect patches from bird
image dataset by using only text terms and tests without partial annotation.
The results show that visual text information and bird parts can be linked
with zero samples.

###  6.3 Discussion and Summary

Multi-modal FSL is still in the developing stage and there are currently
several challenges until now: how to combine data from heterogeneous domains,
how to deal with the different levels of noise that occur during the
combination of different modalities, and how to learn together. Table. VIII
classifies FSL tasks in multimodality as representation, alignment, fusion,
co-learning, and translation. In a multimodal FSL, a good feature
representation should be able to fill in the missing modalities based on the
observed modal information. More approaches will emerge in the future, going
well beyond modal embedding and generating semantic information from images.

##  7 FSL Applications In Computer Vision

In the past five years, we systematically combed and summarized FSL in the
field of computer vision [161] and divided tasks into image classification,
object detection, semantic segmentation, and instance segmentation. Following
is a detailed summary in the form of graphs and tables based on the time
dimension. By reading this section, the reader will be able to gain a
comprehensive grasp of FSL in the field of computer vision.

###  7.1 Few-shot Image Classification

Except like Google and Facebook, most researchers in real life do not have
access to a large dataset of good quality. In FSL computer vision
classification tasks, each task may contain only one or a few samples. Solving
few-shot image classification tasks is mainly addressed by data augmentation,
transfer learning, meta-learning, and multimodal fusion learning. At present,
the top three methods in terms of accuracy are all based on feature
augmentation and feature transformation of the backbone model. In this
section, we investigates all few-shot image classification models from 2016 to
the present and counts the best performance of all models on the mini-ImageNet
benchmark dataset. Here we use 5-way-1-shot and 5-way-5-shot as baseline
tasks. Table. IX and Fig. 15 illustrate our investigation results.

Figure 14: Best performance of metric learning in image classification tasks
during 2017-2021

Figure 15: Best performance of metric learning in object detection tasks
during 2017-2021

TABLE IX: The latest performance of FSL in the image classification tasks of
computer vision

Ref | Model | 5-way-1-shot accuary | 5-way-5-shot accuary | Extra Training Data | Approach Features | Pubilsed Date | Available code
---|---|---|---|---|---|---|---
Lee et al. [162] | ESFR | 76.84% | 84.36% | ✔ |  | Early-Stage Feature Reconst
---
-ruction
Jun 2021 | ✔
Wang et al. [163] | Multi-Task Learning | 59.84% | 77.72% | ✗ |  | Multi-Task Learning and Meta
---
-Learning
Jun 2021 | ✔
Afham et al. [164] | RS-FSL | 65.33% | - | ✔ | Semantic assistance | Apr 2021 | ✔
Rizve et al. [165] | Invariance-Equivariance | 67.28% | 84.78% | ✗ |  | Invariant and Equivariant Repres
---
-entations
Apr 2021 | ✔
Esfandiarpoor et al. [166] | pseudo-shots | 73.35% | 82.51% | ✗ | Exploiting Existing Resources | Dec 2020 | ✔
Chen et al. [167] | MATANet | 53.63% | 72.67% | ✗ | Multi-scale Adaptive + Attention | Nov 2020 |
Wang et al. [168] | MTUNet | 55.03% | 56.12% | ✗ |  | using backbone model and weight
---
generated
Nov 2020 | ✔
Khacef et al. [169] | WRN + Self-Organizing Map | 71.5% | 82.2% | ✗ | Self-Organizing Maps | Sep 2020 | ✗
Xue et al. [170] | RCN - ResNet12 | 57.40% | 75.19% | ✗ | Transfer learning | Sep 2020 | ✔
Zhong et al. [171] | MCRNET | 62.53% | 80.34% | ✗ | Meta-learning | Jul 2020 | ✔
Ziko et al. [172] | LaplacianShot | 75.57% | 84.72% | ✗ | Transductive Laplacian-regularized | Jul 2020 | ✔
Bateni et al. [173] | Transductive CNAPS + FETI | 79.9% | 91.5% | ✔ | Data Augmentation | Jun 2020 | ✗
Rajasegaran et al. [174] | SKD | 67.04% | 83.54% | ✗ | Knowledge Distillation | Jun 2020 | ✔
Hu et al. [175] | PT+MAP | 82.92% | 88.82% | ✗ | Feature Distribution | Jun 2020 | ✔
Simon et al. [176] | Adaptive Subspace Network | 67.09% | 81.65% | ✗ | central block of a dynamic classifier | Jun 2020 | ✔
Bateni et al. [173] | Transductive CNAPS | 55.6% | 73.1% | ✗ | Self-Organizing Maps | Jun 2020 | ✔
Li et al. [177] | TRAML | 67.10% | 79.54% | ✗ | Adaptive Margin Loss | May 2020 | ✔
Hu et al. [178] | SIB | 70.0% | 79.2% | ✗ | Empirical Bayes Transductive | May 2020 | ✔
Wang et al. [179] | ICI | 69.66% | 80.11% | ✗ | Instance Credibility Inference | Apr 2020 | ✔
Rodríguez et al. [130] | EPNet + SSL | - | 88.05% | ✔ | Embedding Propagation | Apr 2020 | ✔
Nguyen et al. [180] | SImPa | 52.11% | 63.87% | ✗ | PAC-Bayes framework | Mar 2020 | ✗
Guan et al. [181] | DAPNA | 71.88% | 84.07% | ✔ | Domain Adaptation | Feb 2020 | ✗
Chen et al. [182] | AmdimNet | 76.82% | 90.98% | ✔ | Embedding network | Nov 2019 | ✔
Xu et al. [183] | MetaFun-Attention | 64.13% | 80.82% | ✗ |  | Use similar gradient descent
---
to encode labeled data
to predict unlabeled data.
Jau 2020 | ✔
Liu et al. [184] | MetaOptNet-SVM+Task Aug | 65.38% | 82.13 % | ✔ | Embedding Propagation | Nov 2019 | ✔
Song et al. [185] | ACC + Amphibian | 62.21% | 80.75% | ✔ |  | Uing pre-trained base model
---
to generalize novel model
Nov 2019 | ✔
Rodríguez et al. [186] | DKT + BNCosSim | 62.96% | 64% | ✗ |  | Learn a kernel that
---
transfers to new tasks
Dec 2019 | ✔
Mangla et al. [187] | S2M2R | 64.93% | 83.18% | ✔ | Embedding Propagation | Apr 2020 | ✔
Li et al. [188] | LST | 70.1% | 78.7% | ✔ |  | Train a small sample
---
model to predict fake
signatures on unlabeled data
Sep 2019 | ✔
Yoon et al. [189] | TapNet | 61.65% | 76.36% | ✔ |  | Learn the reference vector
---
of each class in
different tasks
May 2019 | ✔
Kim et al. [190] | EGNN + Transduction | - | 76.37% | ✗ |  | Using graph neural network
---
to model intra-class similarity
May 2019 | ✔
Ye et al.[191] | feat+ | 61.72% | 78.38% | ✗ |  | Set-to-set applied
---
to embedded functions
Jau 2019 | ✔
Li et al. [192] | DN4 | 51.24% | 71.02% | ✗ |  | Use image local descriptors
---
for measurement
Jun 2019 | ✔
Park et al. [193] | MC2+ | 55.73% | 70.33% | ✗ | Factorization matrix | Jau 2019 | ✔

###  7.2 Few-shot Object Detection

Few-Shot Object Detection (FSOD) is the task of detecting rare objects from
several samples. There has been a lot of progress in FSL for image
classification, but rarely for object detection. At present the evolution of
few-shot object detection can be divided into three main camps: Data
augmentation, transfer learning, and meta-learning. Out of them, Attention
mechanisms plays a pivotal role in small sample target detection. Equally, the
issue of slow inference for a few-shot object detections to meet real-time
requirements remains serious. The Table. X and Fig. 15 are used to show recent
advances in object detection in FSL.

Figure 16: Best performance of metric learning in semantic segmentation tasks
during 2018-2021

Figure 17: Best performance of metric learning in instance segmentation tasks
during 2017-2021

TABLE X: The latest performance of FSL in the tasks of few-shot object
detection during 2019-2021

Ref | Model |  | 10-shot AP/
---
30-shot AP
Extra Training Data | Core idea | Approach Taxonomy | Pubilsed Date | Available code
Zhang et al. [26] | Meta-DETR | 17.8/22.9 | ✗ |  | Use semantic alignment to
---
perform specific encoding and
feature-independent decoding
of images
Feature Reconstruction | Jun 2021 | ✔
Sun et al. [194] | FSCE | 11.1/15.3 | ✗ |  | Compare proposal coding loss
---
to improve intra-class compactness
and inter-class variance
Feature embedding | Jun 2021 | ✔
Zhu et al. [195] | SSR-FSD | 11.3/14.7 | ✔ |  | Learning Semantic Embeddings
---
Using the Invariance of
Semantic Relations
Embedding learning | Mar 2021 | ✗
Xiao et al. [196] | FsDetView | 12.5/14.7 | ✔ |  | Share the features of
---
the base class and
the new class
Meta-learning | Jul 2020 | ✔
Wu et al. [197] | MPSR | 9.8/14.1 | ✗ |  | Refinement of samples using
---
multi-scale techniques
Data augmentation | Jul 2020 | ✔
Wang et al. [198] | TFA w/ cos | 10/13.7 | ✗ |  | Fine-tuning the final
---
layer of the detector
Fine-tuning | Mar 2020 | ✔
Yan et al. [199] | Meta R-CNN | -/12.4 | ✗ |  | Meta-learning using partial
---
features
Meta-learning | Sep 2019 | ✔
Wang et al. [200] | MetaDet | 7.1/11.3 | ✔ |  | Prediction of component-specific
---
parameters from several samples
Meta-learning | Sep 2020 | ✗

###  7.3 Few-shot Semantic Segmentation

Few-shot semantic segmentation was first proposed in [31] until 2017. And it
has been widely used in scenes such as medical images and driverless cars.
Unlike traditional semantic segmentation, few-shot semantic segmentation has
less pixel annotation information in support data set. To our best knowledge,
few-shot semantic segmentation can be broadly classified into supervised
semantic segmentation, unsupervised semantic segmentation, and video semantic
segmentation. In the machine learning stage, the more classical approach is to
use probabilistic mappings as prior knowledge for derivation. In the deep
learning phase, a large number of efficient algorithms for segmentation tools
have emerged, but these models often require a large number of manual sample
annotations. Recently, [201] has made significant improvements to few-shot
semantic segmentation by proposing a more concise paradigm where only the
classifier is meta-learned and the feature encoding decoder remains trained
using a conventional segmentation model. Providing the Table. 17 and Fig. 15
for showing few-shot semantic segmentation.

TABLE XI: The latest performance of FSL in the tasks of few-shot semantic
segmentation during 2019-2021

Ref | Model |  | 1-shot Mean IoU/
---
5-shot Mean IoU
Extra Training Data | Core idea | Approach Taxonomy | Pubilsed Date | Available code
Zhang et al. [202] | CyCTR | 64.3/66.6 | ✗ |  | Aggregate supported and pixel
---
features into query sets
Transfer learning | Jun 2021 | ✔
Min et al.[203] | HSNet | 66.2/70.4 | ✔ |  | Extracting different sets of
---
feature composition from different
levels of intermediate convolutional
layers
Feature Engineering | Apr 2021 | ✔
Yang et al. [204] | RPMM | 56.3/- | ✗ |  | Different image regions are
---
associated with multiple prototypes
to obtain a semantic
representation
Metric learning | Sep 2020 | ✔
Tian et al. [205] | PFENet | 60.8/- | ✗ | Feature enrichment + a priori mask | Feature Engineering | Aug 2020 | ✔
Liu et al. [206] | PPNet | 51.5/62.0 | ✔ |  | Refinement of samples using
---
multi-scale techniques
GNN | Sep 2020 | ✔
Nguyen et al. [207] | FWB | 56.2/59.9 | ✗ |  | Measuring the cosine similarity
---
of class feature vectors
and query feature vectors
Metric learning | Sep 2019 | ✔
Wang et al. [208] | PANet | 48.1/55.7 | ✗ | Each pixel is compared to the prototype | Metric learning | Feb 2020 | ✔
Zhang et al. [209] | CANet | 55.4/57.1 | ✗ |  | Support for performing multi
---
-level feature comparisons between
images and query images
Metric learning | Mar 2019 | ✔

###  7.4 Few-Shot Instance Segmentation

In contrast to semantic segmentation, instance segmentation involves
identifying each pixel in an image and labelling it separately. Recently, few
studies are dealing with the problem of segmenting few samples of instances.
Current work still focuses on how to improve R-CNNs using some effective
tools. The most recent work [210] proposes an incremental few-shot instance
segmentation algorithm, which greatly improves the performance on benchmark
data sets. In this section, we survey papers of recent three years on few-shot
instance segmentation. Table. XII and Fig. 15 show the research progress of
the few-shot instance segmentation.

TABLE XII: The latest performance of FSL in the tasks of few-shot instance
segmentation during 2018-2021.

Ref | Model | 1-shot MAP50 | Extra Training Data | Core idea | Approach Taxonomy | Pubilsed Date | Available code
---|---|---|---|---|---|---|---
Ganea et al. [210] | ONCE iMTFA | 20.13 | ✗ |  | Learning discriminative
---
embedding vectors
Metric learning | Jun 2021 | ✔
Nguyen et al. [211] | FAPIS | 16.3 | ✗ | Modeling of shared parts | Metric learning | Apr 2021 | ✔
Fan et al. [212] | FGN | 16.2 | ✗ |  | Using attention and
---
relationships to guide
generalization
Based model | Sep 2020 | ✔
He et al. [213] | Mask R-CNN | 14.8 | ✗ |  | Adding masks to
---
predicted objects
Data Augmentation | Aug 2017 | ✔
Michaelis et al. [214] | Siamese Mask R-CNN | 14.5 | ✗ |  | Encoding reference image
---
subjects
Metric learning | Nov 2018 | ✔

##  8 Future Direction and Opportunities of FSL

Considerable recent work has made promising progress on various task settings
for FSL. Nonetheless, for more challenging scenes, both the training and
validation data sets are minimal, where the distribution of other data neither
helps real samples to be evaluated nor has extensive training data or
validation datasets for transfer learning. Moreover, meta-learning also does
not have enough tasks to initialize the parameters. With the taxonomy proposed
in this survey, in this section we put forward several possible future
research directions in FSL. Furthermore, recent advances in applications and
algorithms are also presented through this comprehensive survey of FSL.

###  8.1 Better evaluation of data distribution

The essence of FSL is that the support data sets are too small to evaluate the
true data distribution. So what exactly can be done to maximize the evaluation
of the true data distribution using a limited number of samples? The latest
work [215] is making a useful attempt in this direction, proposing the idea of
distribution correction where the mean and covariance of the base class are
computationally corrected and then a linear classifier can be used directly to
obtain good results. In fact, the difference between FSL and traditional deep
learning is not big enough when the few samples are accurate enough to
estimate the true data distribution. This is an exciting direction to explore.
Similarly, in the field of computer vision, there are no task settings or
datasets based on real application scenarios for FSL. Most of the work is
still focused on leveraging and mining information from image data. The
current mainstream benchmark datasets have more or less various problems: the
mini-Imagenet dataset has some inappropriate samples or too difficult samples,
such as solid occlusion, multiple objects in the same image, etc. The Omniglot
dataset is far away from practical applications and is not easily inspired in
real-world applications. BSCD-FSL [36] provides a more violent cross-domain
FSL benchmark dataset involving satellite images, medical images. Until now
there is no benchmark dataset to evaluate the generalization ability of a
model at a fine grained detail. Developing and completing a benchmark dataset
in the field of FSL will provide a more realistic evaluation of the current
state-of-the-arts in FSL.

###  8.2 Improving the robustness of data-to-label mapping

A new challenge to FSL is posed by the emergence of BSCD-FSL. Its emergence
explores and reveals the limitations of current FSL solutions for cross-domain
learning. Recent research has produced some excellent results in this area,
such as skillfully designed task tuning, more sophisticated hyperparameter
tuning, formation of auxiliary data sets, and extraction of domain-irrelevant
features. Currently, fine-tuning is already performing very robustly at the
intersection of transfer learning and meta-learning. Nonetheless, both
techniques are still very distinct. Pre-training can be seen as learning many
categories of tasks, but it is single-task learning. Meta-learning, on the
other hand, is a multi-task learning approach. Whether there is a better model
that can integrate meta-learning and fine-tuning to maximize the performance
of the model while reducing the computational complexity of meta-learning is a
direction worthy of deeper discussion and exchange among researchers at the
moment.

###  8.3 Learn meta-knowledge more effectively from historical tasks

Meta learning is still limited to performance in a specific task space under a
defined network structure. In the case of classification tasks, only
associations between classification tasks can currently be considered. Is it
possible to have a framework that can take into account tasks such as
classification, detection, prediction, and generation at the same time? This
would enable meta-learning to be somewhat separated from the conception of
tasks. Some recent work has attempted to optimize each small batch as a whole.
In this case, how to optimize the inner loop will be an important direction of
optimization for efficient applications. In the future, pre-training and fine-
tuning will become the mainstream algorithms for FSL. At present, meta-
learning is still exploring the correlation between tasks, and no relevant
theory has yet emerged to explain the causal relationship behind meta-
learning. As the causation theory framework evolves, meta-learning would
probably tend to become a more general framework.

###  8.4 Full convergence of multimodal information

Multimodal learning is currently an emerging approach for solving FSL problems
by automatically learning small sample tasks in edge scenarios without
supervised information and quickly migrating to data from different domains.
It is widely regarded as a path exploration from weak AI in limited domains
towards general AI. The implementation of pre-training and fine-tuning in
multimodal learning scenarios can largely enable the usage of a uniform
feature representation across different tasks. For instance, cross-modal
understanding, and cross-modal generation. The emergence of multimodal pre-
training models can support multiple tasks, generalize across many scenarios,
and have a substantial ability to generalize and replicate at scale. Extensive
work has been done on fusing two or more types of information, including
semantic information. Nonetheless, the main work is still focused on pixels
and semantic information, with a relatively single function. In order to
effectively address feature reuse under multiple modalities and reduce the
cost of data annotation, there is an urgent need for the industry to
materialize a powerful pre-trained model involving the fusion of three and
more modalities.

##  9 CONCLUSION

As an important branch of deep learning, few-shot Learning does not require a
large amount of data but chooses a softer approach to solve problems, where it
can be perfectly integrated with techniques such as transfer learning, meta-
learning and data augmentation. In this paper, we provide a comprehensive
survey of FSL in the form of questions and answers that easily distinguish the
confused concepts and summarize the rich baseline dataset under FSL. Besides,
we provide unique insights into the challenges in the development of FSL
following a new taxonomy. The evolution of relevant research methods is
analyzed in depth according to the degree of integration of knowledge in each
stage. Furthermore, for the sake of completeness of the exposition, we also
compare and analyze the recent advances of FSL in the field of computer
vision. Finally, we present a list of possible future research directions and
opportunities in light of the extensive recent literature. Overall, this paper
provides an overall comprehensive summary of the frontier advances in FSL over
the past three years and is expected to contribute to the synergistic
development of FSL and its related fields.

## References

  * [1] Wei-Yu Chen, Yen-Cheng Liu, Zsolt Kira, Yu-Chiang Frank Wang, and Jia-Bin Huang.  A closer look at few-shot classification.  arXiv preprint arXiv:1904.04232, 2019.
  * [2] Carl Doersch, Ankush Gupta, and Andrew Zisserman.  Crosstransformers: spatially-aware few-shot transfer.  arXiv preprint arXiv:2007.11498, 2020.
  * [3] Jun Shu, Zongben Xu, and Deyu Meng.  Small sample learning in big data era.  arXiv preprint arXiv:1808.04572, 2018.
  * [4] Jiang Lu, Pinghua Gong, Jieping Ye, and Changshui Zhang.  Learning from very few samples: A survey.  arXiv preprint arXiv:2009.02653, 2020.
  * [5] Yaqing Wang, Quanming Yao, James T Kwok, and Lionel M Ni.  Generalizing from a few examples: A survey on few-shot learning.  ACM Computing Surveys (CSUR), 53(3):1–34, 2020.
  * [6] Endel Tulving.  How many memory systems are there?  American psychologist, 40(4):385, 1985.
  * [7] Endel Tulving.  Episodic memory: From mind to brain.  Annual review of psychology, 53(1):1–25, 2002.
  * [8] Shuo Wang, Jun Yue, Jianzhuang Liu, Qi Tian, and Meng Wang.  Large-scale few-shot learning via multi-modal knowledge discovery.  In European Conference on Computer Vision, pages 718–734. Springer, 2020.
  * [9] Chen Xing, Negar Rostamzadeh, Boris Oreshkin, and Pedro O O Pinheiro.  Adaptive cross-modal few-shot learning.  Advances in Neural Information Processing Systems, 32:4847–4857, 2019.
  * [10] Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, and Aram Galstyan.  A survey on bias and fairness in machine learning.  ACM Computing Surveys (CSUR), 54(6):1–35, 2021.
  * [11] Sakshi Painuly, Sachin Sharma, and Priya Matta.  Future trends and challenges in next generation smart application of 5g-iot.  In 2021 5th International Conference on Computing Methodologies and Communication (ICCMC), pages 354–357. IEEE, 2021.
  * [12] Liang Yao, Chengsheng Mao, and Yuan Luo.  Kg-bert: Bert for knowledge graph completion.  arXiv preprint arXiv:1909.03193, 2019.
  * [13] Mark Woodward and Chelsea Finn.  Active one-shot learning.  arXiv preprint arXiv:1702.06559, 2017.
  * [14] Akshay Mehrotra and Ambedkar Dukkipati.  Generative adversarial residual pairwise networks for one shot learning.  arXiv preprint arXiv:1703.08033, 2017.
  * [15] Zitian Chen, Yanwei Fu, Yu-Xiong Wang, Lin Ma, Wei Liu, and Martial Hebert.  Image deformation meta-networks for one-shot learning.  In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8680–8689, 2019.
  * [16] Ruslan Salakhutdinov, Joshua Tenenbaum, and Antonio Torralba.  One-shot learning with a hierarchical nonparametric bayesian model.  In Proceedings of ICML Workshop on Unsupervised and Transfer Learning, pages 195–206. JMLR Workshop and Conference Proceedings, 2012.
  * [17] Eli Schwartz, Leonid Karlinsky, Joseph Shtok, Sivan Harary, Mattias Marder, Sharathchandra Pankanti, Rogerio Feris, Abhishek Kumar, Raja Giries, and Alex M Bronstein.  Repmet: Representative-based metric learning for classification and one-shot object detection.  arXiv preprint arXiv:1806.04728, 4323, 2018.
  * [18] Kwangjin Yoon, Jeonghwan Gwak, Young-Min Song, Young-Chul Yoon, and Moon-Gu Jeon.  Oneshotda: Online multi-object tracker with one-shot-learning-based data association.  IEEE Access, 8:38060–38072, 2020.
  * [19] Shruti Jadon and Aditya Arcot Srinivasan.  Improving siamese networks for one-shot learning using kernel-based activation functions.  In Data Management, Analytics and Innovation, pages 353–367. Springer, 2021.
  * [20] Christoph H Lampert, Hannes Nickisch, and Stefan Harmeling.  Learning to detect unseen object classes by between-class attribute transfer.  In 2009 IEEE Conference on Computer Vision and Pattern Recognition, pages 951–958. IEEE, 2009.
  * [21] Yongqin Xian, Tobias Lorenz, Bernt Schiele, and Zeynep Akata.  Feature generating networks for zero-shot learning.  In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 5542–5551, 2018.
  * [22] Elyor Kodirov, Tao Xiang, and Shaogang Gong.  Semantic autoencoder for zero-shot learning.  In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3174–3183, 2017.
  * [23] Muhammad Ferjad Naeem, Yongqin Xian, Federico Tombari, and Zeynep Akata.  Learning graph embeddings for compositional zero-shot learning.  In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 953–962, 2021.
  * [24] Joaquin Vanschoren.  Meta-learning: A survey.  arXiv preprint arXiv:1810.03548, 2018.
  * [25] Kevin Li, Abhishek Gupta, Ashwin Reddy, Vitchyr H Pong, Aurick Zhou, Justin Yu, and Sergey Levine.  Mural: Meta-learning uncertainty-aware rewards for outcome-driven reinforcement learning.  In International Conference on Machine Learning, pages 6346–6356. PMLR, 2021.
  * [26] Gongjie Zhang, Zhipeng Luo, Kaiwen Cui, and Shijian Lu.  Meta-detr: Few-shot object detection via unified image-level meta-learning.  arXiv preprint arXiv:2103.11731, 2021.
  * [27] Meng Cheng, Hanli Wang, and Yu Long.  Meta-learning based incremental few-shot object detection.  IEEE Transactions on Circuits and Systems for Video Technology, 2021\.
  * [28] C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie.  The Caltech-UCSD Birds-200-2011 Dataset.  Technical Report CNS-TR-2011-001, California Institute of Technology, 2011\.
  * [29] Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Daan Wierstra, et al.  Matching networks for one shot learning.  Advances in neural information processing systems, 29:3630–3638, 2016.
  * [30] Brenden M Lake, Ruslan Salakhutdinov, and Joshua B Tenenbaum.  Human-level concept learning through probabilistic program induction.  Science, 350(6266):1332–1338, 2015.
  * [31] Amirreza Shaban, Shray Bansal, Zhen Liu, Irfan Essa, and Byron Boots.  One-shot learning for semantic segmentation.  arXiv preprint arXiv:1709.03410, 2017.
  * [32] Eleni Triantafillou, Tyler Zhu, Vincent Dumoulin, Pascal Lamblin, Utku Evci, Kelvin Xu, Ross Goroshin, Carles Gelada, Kevin Swersky, Pierre-Antoine Manzagol, et al.  Meta-dataset: A dataset of datasets for learning to learn from few examples.  arXiv preprint arXiv:1903.03096, 2019.
  * [33] Xavier Roynard, Jean-Emmanuel Deschaud, and François Goulette.  Paris-lille-3d: A large and high-quality ground-truth urban point cloud dataset for automatic segmentation and classification.  The International Journal of Robotics Research, 37(6):545–557, 2018\.
  * [34] Seong Joon Oh, Kevin Murphy, Jiyan Pan, Joseph Roth, Florian Schroff, and Andrew Gallagher.  Modeling uncertainty with hedged instance embedding.  arXiv preprint arXiv:1810.00319, 2018.
  * [35] Jianxiong Xiao, James Hays, Krista A Ehinger, Aude Oliva, and Antonio Torralba.  Sun database: Large-scale scene recognition from abbey to zoo.  In 2010 IEEE computer society conference on computer vision and pattern recognition, pages 3485–3492. IEEE, 2010.
  * [36] Yunhui Guo, Noel C Codella, Leonid Karlinsky, James V Codella, John R Smith, Kate Saenko, Tajana Rosing, and Rogerio Feris.  A broader study of cross-domain few-shot learning.  In European Conference on Computer Vision, pages 124–141. Springer, 2020.
  * [37] Sharada P Mohanty, David P Hughes, and Marcel Salathé.  Using deep learning for image-based plant disease detection.  Frontiers in plant science, 7:1419, 2016.
  * [38] Patrick Helber, Benjamin Bischke, Andreas Dengel, and Damian Borth.  Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification.  IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(7):2217–2226, 2019.
  * [39] Xiaosong Wang, Yifan Peng, Le Lu, Zhiyong Lu, Mohammadhadi Bagheri, and Ronald M Summers.  Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases.  In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2097–2106, 2017.
  * [40] Noel Codella, Veronica Rotemberg, Philipp Tschandl, M Emre Celebi, Stephen Dusza, David Gutman, Brian Helba, Aadi Kalloo, Konstantinos Liopyris, Michael Marchetti, et al.  Skin lesion analysis toward melanoma detection 2018: A challenge hosted by the international skin imaging collaboration (isic).  arXiv preprint arXiv:1902.03368, 2019.
  * [41] Xavier Bouthillier, Kishore Konda, Pascal Vincent, and Roland Memisevic.  Dropout as data augmentation.  arXiv preprint arXiv:1506.08700, 2015.
  * [42] Zhun Zhong, Liang Zheng, Guoliang Kang, Shaozi Li, and Yi Yang.  Random erasing data augmentation.  In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, pages 13001–13008, 2020.
  * [43] Terrance DeVries and Graham W Taylor.  Improved regularization of convolutional neural networks with cutout.  arXiv preprint arXiv:1708.04552, 2017.
  * [44] Sangdoo Yun, Dongyoon Han, Seong Joon Oh, Sanghyuk Chun, Junsuk Choe, and Youngjoon Yoo.  Cutmix: Regularization strategy to train strong classifiers with localizable features.  In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 6023–6032, 2019.
  * [45] Roland Kwitt, Sebastian Hegenbart, and Marc Niethammer.  One-shot learning of scene locations via feature trajectory transfer.  In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 78–86, 2016.
  * [46] Junjie Li, Zilei Wang, and Xiaoming Hu.  Learning intact features by erasing-inpainting for few-shot classification.  In Proceedings of the AAAI Conference on Artificial Intelligence, volume 35, pages 8401–8409, 2021.
  * [47] Jianhong Zhang, Manli Zhang, Zhiwu Lu, and Tao Xiang.  Adargcn: Adaptive aggregation gcn for few-shot learning.  In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 3482–3491, 2021.
  * [48] Hang Gao, Zheng Shou, Alireza Zareian, Hanwang Zhang, and Shih-Fu Chang.  Low-shot learning via covariance-preserving adversarial augmentation networks.  arXiv preprint arXiv:1810.11730, 2018.
  * [49] Wen-Hsuan Chu, Yu-Jhe Li, Jing-Cheng Chang, and Yu-Chiang Frank Wang.  Spot and learn: A maximum-entropy patch sampler for few-shot image classification.  In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6251–6260, 2019.
  * [50] Hongguang Zhang, Jing Zhang, and Piotr Koniusz.  Few-shot learning via saliency-guided hallucination of samples.  In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2770–2779, 2019.
  * [51] Amit Alfassy, Leonid Karlinsky, Amit Aides, Joseph Shtok, Sivan Harary, Rogerio Feris, Raja Giryes, and Alex M Bronstein.  Laso: Label-set operations networks for multi-label few-shot learning.  In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6548–6557, 2019.
  * [52] Zitian Chen, Yanwei Fu, Yinda Zhang, Yu-Gang Jiang, Xiangyang Xue, and Leonid Sigal.  Semantic feature augmentation in few-shot learning.  arXiv preprint arXiv:1804.05298, 86:89, 2018.
  * [53] Hiroshi Inoue.  Data augmentation by pairing samples for images classification.  arXiv preprint arXiv:1801.02929, 2018.
  * [54] Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz.  mixup: Beyond empirical risk minimization.  arXiv preprint arXiv:1710.09412, 2017.
  * [55] Li-Qun Chen, Xing Xie, Xin Fan, Wei-Ying Ma, Hong-Jiang Zhang, and He-Qin Zhou.  A visual attention model for adapting images on small displays.  Multimedia systems, 9(4):353–364, 2003.
  * [56] Zitian Chen, Yanwei Fu, Yinda Zhang, Yu-Gang Jiang, Xiangyang Xue, and Leonid Sigal.  Multi-level semantic feature augmentation for one-shot learning.  IEEE Transactions on Image Processing, 28(9):4594–4605, 2019.
  * [57] Yonggang Li, Guosheng Hu, Yongtao Wang, Timothy Hospedales, Neil M Robertson, and Yongxin Yang.  Dada: Differentiable automatic data augmentation.  arXiv preprint arXiv:2003.03780, 2020.
  * [58] Mingyu Kang, Yifan Yang, Duxin Chen, and Wenwu Yu.  Cwgan: A graph vector based traffic missing data adversarial generation approach.  In 2020 Chinese Automation Congress (CAC), pages 6234–6238. IEEE, 2020.
  * [59] Ying Ma, Guoqiang Zhong, Yanan Wang, and Wen Liu.  Metacgan: A novel gan model for generating high quality and diversity images with few training data.  In 2020 International Joint Conference on Neural Networks (IJCNN), pages 1–7. IEEE, 2020.
  * [60] Eli Schwartz, Leonid Karlinsky, Joseph Shtok, Sivan Harary, Mattias Marder, Rogerio Feris, Abhishek Kumar, Raja Giryes, and Alex M Bronstein.  Delta-encoder: an effective sample synthesis method for few-shot object recognition.  arXiv preprint arXiv:1806.04734, 2018.
  * [61] Cheng-Yuan Liou, Wei-Chen Cheng, Jiun-Wei Liou, and Daw-Ran Liou.  Autoencoder for words.  Neurocomputing, 139:84–96, 2014.
  * [62] Junsik Kim, Tae-Hyun Oh, Seokju Lee, Fei Pan, and In So Kweon.  Variational prototyping-encoder: One-shot learning with prototypical images.  In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9462–9470, 2019.
  * [63] Fuzhen Zhuang, Zhiyuan Qi, Keyu Duan, Dongbo Xi, Yongchun Zhu, Hengshu Zhu, Hui Xiong, and Qing He.  A comprehensive survey on transfer learning.  Proceedings of the IEEE, 109(1):43–76, 2020.
  * [64] I Kevin, Kai Wang, Xiaokang Zhou, Wei Liang, Zheng Yan, and Jinhua She.  Federated transfer learning based cross-domain prediction for smart manufacturing.  IEEE Transactions on Industrial Informatics, 2021.
  * [65] Chenjing Cai, Shiwei Wang, Youjun Xu, Weilin Zhang, Ke Tang, Qi Ouyang, Luhua Lai, and Jianfeng Pei.  Transfer learning for drug discovery.  Journal of Medicinal Chemistry, 63(16):8683–8694, 2020.
  * [66] Andrew G Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam.  Mobilenets: Efficient convolutional neural networks for mobile vision applications.  arXiv preprint arXiv:1704.04861, 2017.
  * [67] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun.  Deep residual learning for image recognition.  In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770–778, 2016.
  * [68] Matthew E Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer.  Deep contextualized word representations.  arXiv preprint arXiv:1802.05365, 2018.
  * [69] Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever.  Improving language understanding by generative pre-training.  2018\.
  * [70] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova.  Bert: Pre-training of deep bidirectional transformers for language understanding.  arXiv preprint arXiv:1810.04805, 2018.
  * [71] Guneet S Dhillon, Pratik Chaudhari, Avinash Ravichandran, and Stefano Soatto.  A baseline for few-shot image classification.  arXiv preprint arXiv:1909.02729, 2019.
  * [72] Yinbo Chen, Xiaolong Wang, Zhuang Liu, Huijuan Xu, and Trevor Darrell.  A new meta-baseline for few-shot learning.  arXiv preprint arXiv:2003.04390, 2020.
  * [73] Mahbub Hussain, Jordan J Bird, and Diego R Faria.  A study on cnn transfer learning for image classification.  In UK Workshop on computational Intelligence, pages 191–202. Springer, 2018.
  * [74] Akihiro Nakamura and Tatsuya Harada.  Revisiting fine-tuning for few-shot learning.  arXiv preprint arXiv:1910.00216, 2019.
  * [75] Alexander R Fabbri, Simeng Han, Haoyuan Li, Haoran Li, Marjan Ghazvininejad, Shafiq Joty, Dragomir Radev, and Yashar Mehdad.  Improving zero and few-shot abstractive summarization with intermediate fine-tuning and data augmentation.  arXiv preprint arXiv:2010.12836, 2020.
  * [76] Yann Lifchitz, Yannis Avrithis, Sylvaine Picard, and Andrei Bursuc.  Dense classification and implanting for few-shot learning.  In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9258–9267, 2019.
  * [77] Zhongjie Yu, Lin Chen, Zhongwei Cheng, and Jiebo Luo.  Transmatch: A transfer-learning scheme for semi-supervised few-shot learning.  In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12856–12864, 2020.
  * [78] John Cai and Sheng Mei Shen.  Cross-domain few-shot learning with meta fine-tuning.  arXiv preprint arXiv:2005.10544, 2020.
  * [79] Zhiqiang Shen, Zechun Liu, Jie Qin, Marios Savvides, and Kwang-Ting Cheng.  Partial is better than all: Revisiting fine-tuning strategy for few-shot learning.  In Proceedings of the AAAI Conference on Artificial Intelligence, volume 35, pages 9594–9602, 2021.
  * [80] David Argüeso, Artzai Picon, Unai Irusta, Alfonso Medela, Miguel G San-Emeterio, Arantza Bereciartua, and Aitor Alvarez-Gila.  Few-shot learning approach for plant disease classification using images taken in the field.  Computers and Electronics in Agriculture, 175:105542, 2020.
  * [81] Anay Majee, Kshitij Agrawal, and Anbumani Subramanian.  Few-shot learning for road object detection.  arXiv preprint arXiv:2101.12543, 2021.
  * [82] Ori Ram, Yuval Kirstain, Jonathan Berant, Amir Globerson, and Omer Levy.  Few-shot question answering by pretraining span selection.  arXiv preprint arXiv:2101.00438, 2021.
  * [83] Nikita Dvornik, Cordelia Schmid, and Julien Mairal.  Selecting relevant features from a multi-domain representation for few-shot classification.  In European Conference on Computer Vision, pages 769–786. Springer, 2020.
  * [84] Davis Wertheimer, Luming Tang, and Bharath Hariharan.  Few-shot classification with feature map reconstruction networks.  In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8012–8021, 2021.
  * [85] Hung-Yu Tseng, Hsin-Ying Lee, Jia-Bin Huang, and Ming-Hsuan Yang.  Cross-domain few-shot classification via learned feature-wise transformation.  arXiv preprint arXiv:2001.08735, 2020.
  * [86] Jiamei Sun, Sebastian Lapuschkin, Wojciech Samek, Yunqing Zhao, Ngai-Man Cheung, and Alexander Binder.  Explanation-guided training for cross-domain few-shot classification.  In 2020 25th International Conference on Pattern Recognition (ICPR), pages 7609–7616. IEEE, 2021.
  * [87] Andrei A Rusu, Dushyant Rao, Jakub Sygnowski, Oriol Vinyals, Razvan Pascanu, Simon Osindero, and Raia Hadsell.  Meta-learning with latent embedding optimization.  arXiv preprint arXiv:1807.05960, 2018.
  * [88] Yuqian Fu, Yanwei Fu, and Yu-Gang Jiang.  Meta-fdmixup: Cross-domain few-shot learning guided by labeled target data.  In Proceedings of the 29th ACM International Conference on Multimedia, pages 5326–5334, 2021.
  * [89] Cheng Perng Phoo and Bharath Hariharan.  Self-training for few-shot transfer across extreme task differences.  arXiv preprint arXiv:2010.07734, 2020.
  * [90] Lu Liu, William Hamilton, Guodong Long, Jing Jiang, and Hugo Larochelle.  A universal representation transformer layer for few-shot image classification.  arXiv preprint arXiv:2006.11702, 2020.
  * [91] Wei-Hong Li, Xialei Liu, and Hakan Bilen.  Universal representation learning from multiple domains for few-shot classification.  arXiv preprint arXiv:2103.13841, 2021.
  * [92] Hakan Bilen and Andrea Vedaldi.  Universal representations: The missing link between faces, text, planktons, and cat breeds.  arXiv preprint arXiv:1701.07275, 2017.
  * [93] Sylvestre-Alvise Rebuffi, Hakan Bilen, and Andrea Vedaldi.  Learning multiple visual domains with residual adapters.  arXiv preprint arXiv:1705.08045, 2017.
  * [94] Chelsea Finn, Pieter Abbeel, and Sergey Levine.  Model-agnostic meta-learning for fast adaptation of deep networks.  In International Conference on Machine Learning, pages 1126–1135. PMLR, 2017.
  * [95] Alex Nichol, Joshua Achiam, and John Schulman.  On first-order meta-learning algorithms.  arXiv preprint arXiv:1803.02999, 2018.
  * [96] Aniruddh Raghu, Maithra Raghu, Samy Bengio, and Oriol Vinyals.  Rapid learning or feature reuse? towards understanding the effectiveness of maml.  arXiv preprint arXiv:1909.09157, 2019.
  * [97] Zhenguo Li, Fengwei Zhou, Fei Chen, and Hang Li.  Meta-sgd: Learning to learn quickly for few-shot learning.  arXiv preprint arXiv:1707.09835, 2017.
  * [98] Muhammad Abdullah Jamal and Guo-Jun Qi.  Task agnostic meta-learning for few-shot learning.  In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11719–11727, 2019.
  * [99] Aravind Rajeswaran, Chelsea Finn, Sham Kakade, and Sergey Levine.  Meta-learning with implicit gradients, 2019.
  * [100] Jathushan Rajasegaran, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and Mubarak Shah.  itaml: An incremental task-agnostic meta-learning approach.  In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13588–13597, 2020.
  * [101] Joel Joseph and Alex Gu.  Reproducibility report: La-maml: Look-ahead meta learning for continual learning.  arXiv preprint arXiv:2102.05824, 2021.
  * [102] Anugunj Naman and Liliana Mancini.  Fixed-maml for few shot classification in multilingual speech emotion recognition.  arXiv preprint arXiv:2101.01356, 2021.
  * [103] Taewon Jeong and Heeyoung Kim.  Ood-maml: Meta-learning for few-shot out-of-distribution detection and classification.  Advances in Neural Information Processing Systems, 33, 2020.
  * [104] Olga Wichrowska, Niru Maheswaranathan, Matthew W Hoffman, Sergio Gomez Colmenarejo, Misha Denil, Nando Freitas, and Jascha Sohl-Dickstein.  Learned optimizers that scale and generalize.  In International Conference on Machine Learning, pages 3751–3760. PMLR, 2017.
  * [105] Kiran Kumar Chandriah and Raghavendra V Naraganahalli.  Rnn/lstm with modified adam optimizer in deep learning approach for automobile spare parts demand forecasting.  Multimedia Tools and Applications, pages 1–15, 2021.
  * [106] Marcin Andrychowicz, Misha Denil, Sergio Gomez, Matthew W Hoffman, David Pfau, Tom Schaul, Brendan Shillingford, and Nando De Freitas.  Learning to learn by gradient descent by gradient descent.  In Advances in neural information processing systems, pages 3981–3989, 2016.
  * [107] Rein Houthooft, Richard Y Chen, Phillip Isola, Bradly C Stadie, Filip Wolski, Jonathan Ho, and Pieter Abbeel.  Evolved policy gradients.  arXiv preprint arXiv:1802.04821, 2018.
  * [108] Luisa Zintgraf, Kyriacos Shiarlis, Maximilian Igl, Sebastian Schulze, Yarin Gal, Katja Hofmann, and Shimon Whiteson.  Varibad: A very good method for bayes-adaptive deep rl via meta-learning.  arXiv preprint arXiv:1910.08348, 2019.
  * [109] AJ Piergiovanni, Anelia Angelova, and Michael S Ryoo.  Evolving losses for unsupervised video representation learning.  In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 133–142, 2020.
  * [110] Hieu Pham, Melody Guan, Barret Zoph, Quoc Le, and Jeff Dean.  Efficient neural architecture search via parameters sharing.  In International Conference on Machine Learning, pages 4095–4104. PMLR, 2018.
  * [111] Hanxiao Liu, Karen Simonyan, and Yiming Yang.  Darts: Differentiable architecture search.  arXiv preprint arXiv:1806.09055, 2018.
  * [112] Gabriel Bender, Pieter-Jan Kindermans, Barret Zoph, Vijay Vasudevan, and Quoc Le.  Understanding and simplifying one-shot architecture search.  In International Conference on Machine Learning, pages 550–559. PMLR, 2018.
  * [113] Kaicheng Yu, Christian Sciuto, Martin Jaggi, Claudiu Musat, and Mathieu Salzmann.  Evaluating the search phase of neural architecture search.  arXiv preprint arXiv:1902.08142, 2019.
  * [114] Xuanyi Dong and Yi Yang.  Nas-bench-201: Extending the scope of reproducible neural architecture search.  arXiv preprint arXiv:2001.00326, 2020.
  * [115] Yiyang Zhao, Linnan Wang, Yuandong Tian, Rodrigo Fonseca, and Tian Guo.  Few-shot neural architecture search.  In International Conference on Machine Learning, pages 12707–12718. PMLR, 2021.
  * [116] Thomas Elsken, Benedikt Staffler, Jan Hendrik Metzen, and Frank Hutter.  Meta-learning of neural architectures for few-shot learning.  In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12365–12375, 2020.
  * [117] Kevin Musgrave, Serge Belongie, and Ser-Nam Lim.  A metric learning reality check.  In European Conference on Computer Vision, pages 681–699. Springer, 2020.
  * [118] Nihar Bendre, Hugo Terashima Marín, and Peyman Najafirad.  Learning from few samples: A survey.  arXiv preprint arXiv:2007.15484, 2020.
  * [119] Gregory Koch, Richard Zemel, Ruslan Salakhutdinov, et al.  Siamese neural networks for one-shot image recognition.  In ICML deep learning workshop, volume 2. Lille, 2015.
  * [120] Elad Hoffer and Nir Ailon.  Deep metric learning using triplet network.  In International workshop on similarity-based pattern recognition, pages 84–92. Springer, 2015.
  * [121] Weihua Chen, Xiaotang Chen, Jianguo Zhang, and Kaiqi Huang.  Beyond triplet loss: a deep quadruplet network for person re-identification.  In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 403–412, 2017.
  * [122] Xiaomeng Li, Lequan Yu, Chi-Wing Fu, Meng Fang, and Pheng-Ann Heng.  Revisiting metric learning for few-shot image classification.  Neurocomputing, 406:49–58, 2020.
  * [123] Jake Snell, Kevin Swersky, and Richard S Zemel.  Prototypical networks for few-shot learning.  arXiv preprint arXiv:1703.05175, 2017.
  * [124] Liu Yang and Rong Jin.  Distance metric learning: A comprehensive survey.  Michigan State Universiy, 2(2):4, 2006.
  * [125] Matthew Schultz and Thorsten Joachims.  Learning a distance metric from relative comparisons.  Advances in neural information processing systems, 16:41–48, 2004\.
  * [126] Shihyen Chen, Bin Ma, and Kaizhong Zhang.  On the similarity metric and the distance metric.  Theoretical Computer Science, 410(24-25):2365–2376, 2009.
  * [127] Kilian Q Weinberger, John Blitzer, and Lawrence K Saul.  Distance metric learning for large margin nearest neighbor classification.  In Advances in neural information processing systems, pages 1473–1480, 2006.
  * [128] Fangyu Wu, Jeremy S Smith, Wenjin Lu, Chaoyi Pang, and Bailing Zhang.  Attentive prototype few-shot learning with capsule network-based embedding.  In European Conference on Computer Vision, pages 237–253. Springer, 2020.
  * [129] Bin Liu, Yue Cao, Yutong Lin, Qi Li, Zheng Zhang, Mingsheng Long, and Han Hu.  Negative margin matters: Understanding margin in few-shot classification.  In European Conference on Computer Vision, pages 438–455. Springer, 2020.
  * [130] Pau Rodríguez, Issam Laradji, Alexandre Drouin, and Alexandre Lacoste.  Embedding propagation: Smoother manifold for few-shot classification.  In European Conference on Computer Vision, pages 121–138. Springer, 2020.
  * [131] Yan Huang, Yang Long, and Liang Wang.  Few-shot image and sentence matching via gated visual-semantic embedding.  In Proceedings of the AAAI Conference on Artificial Intelligence, volume 33, pages 8489–8496, 2019.
  * [132] Arvind Srinivasan, Aprameya Bharadwaj, Manasa Sathyan, and S Natarajan.  Optimization of image embeddings for few shot learning.  arXiv preprint arXiv:2004.02034, 2020.
  * [133] Flood Sung, Yongxin Yang, Li Zhang, Tao Xiang, Philip HS Torr, and Timothy M Hospedales.  Learning to compare: Relation network for few-shot learning.  In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1199–1208, 2018.
  * [134] Xiaoxu Li, Jijie Wu, Zhuo Sun, Zhanyu Ma, Jie Cao, and Jing-Hao Xue.  Bsnet: Bi-similarity network for few-shot fine-grained image classification.  IEEE Transactions on Image Processing, 30:1318–1331, 2020.
  * [135] Van Nhan Nguyen, Sigurd Løkse, Kristoffer Wickstrøm, Michael Kampffmeyer, Davide Roverso, and Robert Jenssen.  Sen: A novel feature normalization dissimilarity measure for prototypical few-shot learning networks.  In Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIII 16, pages 118–134. Springer, 2020.
  * [136] Jinlu Liu, Liang Song, and Yongqiang Qin.  Prototype rectification for few-shot learning.  In Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part I 16, pages 741–756. Springer, 2020.
  * [137] Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and S Yu Philip.  A comprehensive survey on graph neural networks.  IEEE transactions on neural networks and learning systems, 32(1):4–24, 2020.
  * [138] Victor Garcia and Joan Bruna.  Few-shot learning with graph neural networks.  arXiv preprint arXiv:1711.04043, 2017.
  * [139] Ling Yang, Liangliang Li, Zilun Zhang, Xinyu Zhou, Erjin Zhou, and Yu Liu.  Dpgn: Distribution propagation graph network for few-shot learning.  In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13390–13399, 2020.
  * [140] Avishek Joey Bose, Ankit Jain, Piero Molino, and William L Hamilton.  Meta-graph: Few shot link prediction via meta learning.  arXiv preprint arXiv:1912.09867, 2019.
  * [141] Huaxiu Yao, Chuxu Zhang, Ying Wei, Meng Jiang, Suhang Wang, Junzhou Huang, Nitesh Chawla, and Zhenhui Li.  Graph few-shot learning via knowledge transfer.  In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, pages 6656–6663, 2020.
  * [142] Zhen Liu, Yitong Xia, and Baochang Zhang.  Graph embedding relation network for few-shot learning.  In 2020 39th Chinese Control Conference (CCC), pages 7328–7334. IEEE, 2020.
  * [143] Cen Chen, Kenli Li, Wei Wei, Joey Tianyi Zhou, and Zeng Zeng.  Hierarchical graph neural networks for few-shot learning.  IEEE Transactions on Circuits and Systems for Video Technology, 2021\.
  * [144] Shiyao Xu and Yang Xiang.  Frog-gnn: Multi-perspective aggregation based graph neural network for few-shot text classification.  Expert Systems with Applications, 176:114795, 2021.
  * [145] Spyros Gidaris and Nikos Komodakis.  Generating classification weights with gnn denoising autoencoders for few-shot learning.  In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 21–30, 2019.
  * [146] Chao Xiong, Wen Li, Yun Liu, and Minghui Wang.  Multi-dimensional edge features graph neural network on few-shot image classification.  IEEE Signal Processing Letters, 28:573–577, 2021.
  * [147] Guo-Sen Xie, Jie Liu, Huan Xiong, and Ling Shao.  Scale-aware graph neural network for few-shot semantic segmentation.  In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5475–5484, 2021.
  * [148] Aoxue Li, Tiange Luo, Zhiwu Lu, Tao Xiang, and Liwei Wang.  Large-scale few-shot learning: Knowledge transfer with class hierarchy.  In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7212–7220, 2019.
  * [149] Edgar Schonfeld, Sayna Ebrahimi, Samarth Sinha, Trevor Darrell, and Zeynep Akata.  Generalized zero-and few-shot learning via aligned variational autoencoders.  In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8247–8255, 2019.
  * [150] Eli Schwartz, Leonid Karlinsky, Rogerio Feris, Raja Giryes, and Alex M Bronstein.  Baby steps towards few-shot learning with multiple semantics.  arXiv preprint arXiv:1906.01905, 2019.
  * [151] Zhimao Peng, Zechao Li, Junge Zhang, Yan Li, Guo-Jun Qi, and Jinhui Tang.  Few-shot image recognition with knowledge transfer.  In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 441–449, 2019.
  * [152] Xin Wang, Fisher Yu, Ruth Wang, Trevor Darrell, and Joseph E Gonzalez.  Tafe-net: Task-aware feature embeddings for low shot learning.  In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1831–1840, 2019.
  * [153] Frederik Pahde, Mihai Puscas, Tassilo Klein, and Moin Nabi.  Multimodal prototypical networks for few-shot learning.  In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 2644–2653, 2021.
  * [154] Mathieu Pagé Fortin and Brahim Chaib-Draa.  Multimodal sentiment analysis: A multitask learning approach.  In ICPRAM, pages 368–376, 2019.
  * [155] Han Zhang, Tao Xu, Hongsheng Li, Shaoting Zhang, Xiaogang Wang, Xiaolei Huang, and Dimitris N Metaxas.  Stackgan: Text to photo-realistic image synthesis with stacked generative adversarial networks.  In Proceedings of the IEEE international conference on computer vision, pages 5907–5915, 2017.
  * [156] Shikhar Sharma, Dendi Suhubdy, Vincent Michalski, Samira Ebrahimi Kahou, and Yoshua Bengio.  Chatpainter: Improving text to image generation using dialogue.  arXiv preprint arXiv:1802.08216, 2018.
  * [157] Scott Reed, Zeynep Akata, Honglak Lee, and Bernt Schiele.  Learning deep representations of fine-grained visual descriptions.  In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 49–58, 2016.
  * [158] Mohamed Elhoseiny, Yizhe Zhu, Han Zhang, and Ahmed Elgammal.  Link the head to the” beak”: Zero shot learning from noisy text description at part precision.  In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 5640–5649, 2017.
  * [159] Yizhe Zhu, Mohamed Elhoseiny, Bingchen Liu, Xi Peng, and Ahmed Elgammal.  A generative adversarial approach for zero-shot learning from noisy texts.  In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1004–1013, 2018.
  * [160] Andrej Karpathy and Li Fei-Fei.  Deep visual-semantic alignments for generating image descriptions.  In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3128–3137, 2015.