# Continual Object Detection: A review of definitions, strategies, and
challenges

Angelo G. Menezes<EMAIL_ADDRESS>Gustavo de Moura  Cézanne Alves  André
C. P. L. F. de Carvalho

###### Abstract

The field of Continual Learning investigates the ability to learn consecutive
tasks without losing performance on those previously learned. Its focus has
been mainly on incremental classification tasks. We believe that research in
continual object detection deserves even more attention due to its vast range
of applications in robotics and autonomous vehicles. This scenario is more
complex than conventional classification given the occurrence of instances of
classes that are unknown at the time, but can appear in subsequent tasks as a
new class to be learned, resulting in missing annotations and conflicts with
the background label. In this review, we analyze the current strategies
proposed to tackle the problem of class-incremental object detection. Our main
contributions are: (1) a short and systematic review of the methods that
propose solutions to traditional incremental object detection scenarios; (2) A
comprehensive evaluation of the existing approaches using a new metric to
quantify the stability and plasticity of each technique in a standard way; (3)
an overview of the current trends within continual object detection and a
discussion of possible future research directions.

###### keywords:

Continual Learning , Object Detection , Systematic Review , Benchmarks

††journal: Neural Networks

[inst1] organization=Institute of Mathematics and Computer Sciences,
University of São Paulo, addressline=Av. Trab. São Carlense, 400 - Centro,
city=São Carlos, postcode=13566-590, state=São Paulo, country=Brazil

[inst2] organization=Eldorado Research Institute, addressline=Av. Alan Turing,
275, Cidade Universitária, city=Campinas, postcode=13083-898, state=São Paulo,
country=Brazil

##  1 Introduction

Deep Neural Networks (DNNs) are computational distributed models able to learn
representations from raw data through a structure of hierarchical layers,
similar to how the brain handles new information. However, they are a powerful
solution only when being used with data that is carefully shuffled, balanced,
and standardized [1]. As real-world data may come in large streams and vary
considerably from what was available during the initial training, some
necessary assumptions for DNNs might not be met. In this case, they can fail
entirely or suffer from a fast decay in performance for early learned tasks,
commonly described as catastrophic forgetting or catastrophic interference
[2].

These circumstances have influenced the introduction of continual learning
(CL), in which techniques are mainly refined to deal with different data-
dynamic scenarios. Although the interest in this area has grown notably since
2016 [3], over the years several names have been used to refer to the search
for models that continually adapt. Some of them are “incremental learning”,
“lifelong learning” and “never-ending learning”. Yet, the recent desiderata
assigned to CL models have become broader and involve not only the forgetting
aspect, but also the scalability, computational efficiency, and fast
adaptability features [4].

Within the context of computer vision, the search for strategies able to deal
with the modeling of a dynamic world is not new [5]. Several applications
associated with streams of images can benefit from having models able to
naturally work with changing and incremental contexts, such as autonomous
cars, Unmanned Aerial Vehicles and house robots [6]. Notwithstanding, most of
the current solutions for CL consider the classification task as its main
conundrum. In this way, the task of continual object detection, which involves
both localization and classification of object samples, is not yet well
explored, having its foundational work dating back to 2017 [7].

Continual Object Detection (COD) is a more complex task than conventional
classification, since the predictive model needs to deal with situations where
new objects, that were unknown previously, appeared in the previous training
data but were not labeled and therefore considered as “background”. This issue
affects the notion of “objectness” of the model and may interfere in its
performance towards either favoring the detection of only previously known
objects or exclusively the new ones. This tradeoff is also in part due to the
natural “tug-of-war” effect that each task creates on the model parameters
during training [1].

This short review aims to provide an overview of the definitions, strategies,
and desiderata that involve the field of COD, with the focus on exploring the
scenario where object instances are introduced incrementally. To the best of
our knowledge, this is the first review to address the topic and provide tools
that researchers can use to standardize their research regarding incremental
object detectors. In this way, we propose the following contributions:

  * •

A short and systematic review of the main strategies proposed for solving the
problem of continually learning and detecting new object instances.

  * •

A comprehensive evaluation of the main proposed methods for class-incremental
object detection using a new metric properly adapted to identify the
stability-plasticity power of a strategy according to its supposed upper-
bound.

  * •

An overview of the possible research directions and trends in the field.

##  2 Technical Background

The field of continual object detection, as previously mentioned, presents the
combination of CL strategies to deal with the forgetting and transferability
of knowledge between object detection tasks. In this way, a general
understanding of both topics is needed to identify opportunities in the field
and interpret the findings of this review.

For the scope of CL, we refer to tasks as a description of the type of
prediction being made comprising a closed set of classes. For example, we can
have a certain detection task $t_{1}$ that predicts the position and label of
some classes $c_{1}$ and $c_{2}$, and another detection task $t_{2}$, that
predicts the same for some other classes $c_{3}$ and $c_{4}$.

###  2.1 Continual Learning with Neural Networks

Continual learning, or lifelong learning, has been coined as the ability to
learn consecutive tasks without forgetting how to perform on the previously
trained ones [8]. Some researchers have pointed out over the years that
research on this topic might lead to the development of an artificial general
intelligence [9, 10] since such behavior is expected from intelligent agents.

As the amount of data available increases over the years and current machine
learning (ML) systems still have poor ability to solve for new tasks without
being properly retrained, solutions that involve continual and multi-task
learning will become more prevalent [11]. Also, as deep learning techniques
are the state-of-the-art for several tasks in areas such as computer vision
and natural language processing [12, 13], the adaptation of the ongoing
strategies in these fields for the continual paradigm becomes a natural
promising research direction.

Despite not being a new research topic [8], there is still no consensus on all
the characteristics that a CL model should consider essential (i.e., CL
Desiderata) during its optimization process [14, 15]. Most of the definitions
favor a specific direction based on the researched topic the author is
involved. For example, one may say that constant memory and forward transfer
are fundamental for robotics. At the same time, for recommendation systems,
one could argue that online learning and fast adaptation are more important
features. Following this line of thought, for the continual object detection
venue, in especial the class-incremental setting, we argue that the following
desiderata should be aimed:

  * •

Quasi-constant memory: A CL model should work with bounded memory.

  * •

Backward Transfer: A CL model should have the ability to improve the
performance of previously learned tasks by learning a new one.

  * •

Forward Transfer: A CL model should have the ability to improve the
performance of future tasks using previously acquired knowledge.

  * •

Fast adaptation and recovery: A CL model should be able to adapt quickly for
new tasks, and, in case a class was gracefully forgotten (better described in
the work of Ahn et al. [16], the model should recover the previous performance
at the same speed.

Also, the ability to identify when a sample object is unknown at test time and
decide whether to learn from it during incremental training is of interest for
applications in autonomous robots [17]. This scenario, which is related to
other different ML paradigms (e.g., out-of-distribution detection, open-set
and open-world recognition), might be a pursued direction for having less
human interference in the learning process [15, 18].

####  2.1.1 Scenarios

When working with classical CL benchmarks [19], there are three general
situations in which data might be introduced:

  * •

New Instances (NI): New training samples of previously known classes.

  * •

New Classes (NC): Only new training samples of new classes.

  * •

New Instances and Classes (NIC): New training samples from both old and new
classes.

When working in classification tasks, the presence of the task ID dictates the
space of possible classes and distributions that can be recognized during test
time. Thus, it describes whether it is possible to create task-specific
solutions or if a more general CL strategy is needed [20]. Following this
trend, the CL literature has mostly adopted the convention from Van de Ven and
Tolias [21] for three general task scenarios:

  * •

Task-Incremental Learning: Assumes the model has information about the task ID
during training and testing. The situation allows for task-specific solutions.

  * •

Domain-Incremental Learning: Assumes the task ID is not given during test
time, but the structure of the task is maintained. Class labels are usually
kept, but the data distribution might change.

  * •

Class-Incremental Learning: Assumes the task ID is not given during test time,
and model needs to infer it. In this way, the model needs to expand its range
of predictions and incrementally add new classes.

Additionally, Task-Free or Task-Agnostic CL [22, 23] represents an additional
scenario for when the task labels are not given during either training or
testing, which makes it the most challenging scheme. For that, the model does
not have any information on task boundaries and still needs to deal with data
distribution changes. The generality related to each mentioned scenario is
described in Figure 1.

Figure 1: General scenarios for CL

####  2.1.2 Evaluation

For evaluating CL models on incremental benchmarks, metrics should assess the
desired characteristics we expect the system to have. To this extent, a CL
model, in general, should be evaluated not only on its final performance but
also on how transferable its knowledge is and how fast it learns and forgets
tasks. The usual procedure adopted by the CL community to comply with this
scheme was first introduced by Lopez-Paz and Ranzato [24] with three metrics.

Average Accuracy (ACC) is the average final accuracy over all seen $T$ tasks
as described by Equation 1.

$ACC=\frac{1}{T}\sum_{i=1}^{T}R_{T,i}$ (1)

Backward Transfer (BWT), as shown by Equation 2, is the measure of the
influence that learning a new task has on the tasks learned so far. A negative
value for this metric indicates the forgetting of old classes.

$BWT=\frac{1}{T-1}\sum_{i=1}^{T-1}R_{T,i}-R_{i,i}$ (2)

Forward Transfer (FWT), as demonstrated in Equation 3, represents the impact
that learning a new task will have on the consecutive tasks. A positive
forward transfer is an indication that the model can perform “zero-shot”
learning.

$FWT=\frac{1}{T-1}\sum_{i=2}^{T}R_{i-1,i}-\bar{b}_{i}$ (3)

For these metrics, $R_{i,j}$ stands for the final test accuracy on task
$t_{j}$ after observing the samples of task $t_{i}$, and $\bar{b}$ the test
accuracy of each task when trained with random initialization. The metrics
above assume the model has access to all tasks beforehand and can be evaluated
on all $T$ tasks right after it finishes the training in each individual task
$t_{i}$.

For measuring how far an incremental model response is from an ideal setting
and therefore assessing its overall stability-plasticity, Hayes et al. [25]
proposed $\Omega$ as the ratio between the model‘s response and the one from
the joint-training equivalent (i.e., a model trained offline with all task
data) as shown by Equation 4. We will refer to this metric as the upper-bound
ratio.

$Upper\mbox{-}bound\ ratio\
(\Omega_{all})=\sum_{t=1}^{T}\frac{R_{T,t}}{R{joint,t}}$ (4)

Although there are interesting adaptations of these metrics that account for
the performance of a CL model along each timestep in training time, in an
application context, a good final performance at test time is usually what is
considered. Additionally, some other metrics provide helpful information
regarding the CL desiderata, such as computational efficiency and memory size
[4], but we will not explore them in the current context of this review.

####  2.1.3 Strategies

Research to overcome catastrophic forgetting is as old as the own field of
neural networks [26, 2], but previously had its focus on solving the problem
for shallow networks. When dealing with deep architectures, the main methods
have been commonly divided into three families of techniques based on:
parameter isolation, regularization, and replay [20].

Parameter isolation techniques

Parameter isolation strategies aim to mitigate forgetting by specifying
parameters to deal with each individual task. This setup typically requires
the freezing of some network parameters and then either dynamically expanding
the network‘s capacity [27] when new tasks arrive or learning specific sparse
masks [28]. One of the base works for this family was proposed by Rusu et al.
[29], where a deep neural network column of layers is trained to execute a
single task. When a new task arrives, the previously trained weights are
frozen, and a new column of layers with a lateral connection to the first
column is added and then trained to execute the new task. Other works also
expand on this strategy to deal with the issues caused by the increased final
model size by applying network pruning and quantization [30]. For this family
of techniques, it is generally guaranteed that the network will perform
equally well as if it was trained from scratch at the cost of having a more
significant memory footprint. Additionally, models in this group often have
the disadvantage of needing a task oracle to reveal the task ID at test time
[20].

Regularization-based techniques

Regularization-based methods introduce strategies to prevent the network
parameters from deviating too much from the learned values that performed well
for the old classes. The Elastic Weight Consolidation (EWC) strategy proposed
by Kirkpatrick et al. [31] first finds important parameters for the learned
tasks and then penalizes their changes when new tasks are presented. Besides
penalty-based regularization, Li and Hoiem [32] proposed the Learning Without
Forgetting (LWF) strategy in which a copy of the network trained on the base
classes is created and knowledge distillation is applied to transfer the
knowledge of the copy to the network trained on the new data. For this whole
family of methods, there is generally no need for storing old data or changing
the current architecture. This is based on the assumption that the task‘s
knowledge is included on the weights, and can be preserved by either
penalizing their change directly or by constraining the updates for new data
using the old activations and logits. However, for this group of techniques,
performance is often limited when compared to other CL strategies [33, 34].

Replay techniques

Methods based on replay, often called rehearsal, store samples from previously
seen data or use generative models to create pseudo-samples that follow the
previous data distribution. The replay samples are then mixed with the ones of
the new task to ensure that the data distribution of the new task does not
deviate much from the previously learned data distribution. Following this
line, Rebuffi et al. [11] proposed the iCaRL strategy in which the samples
that best represent the class means in the feature space are stored and used
at test time with a nearest-mean classifier. In a different way, Lopez-Paz and
Ranzato [24] proposed the Gradient Episodic Memory (GEM) technique to
constrain the model optimization by using replay samples to limit the
gradients for the new task in a way that the approximated loss from the
previous tasks will not increase.

When working with unstructured data (e.g., images and videos), the required
memory buffer to store old samples might be considerably large, making its use
impracticable for some real-world scenarios [33]. Techniques based on pseudo-
rehearsal, a.k.a. generative replay, were established to overcome this
limitation. Shin et al. [35] proposed to train a generative model on the old
data distribution and use it to generate fake samples that help in mitigating
the forgetting of old classes. Although having the downside of the model‘s
performance being upper-bounded by the joint-training in all tasks [20], the
replay family has been the most consistently used strategy in real-world
applications of CL [6, 36].

####  2.1.4 Other Continual Learning Paradigms

Some other learning paradigms have been adjusted to diminish the forgetting of
CL systems by allowing the model to learn the desired adaptability and
stability directly from the data [37, 38].

Meta-Learning for Continual Learning

Meta-learning, a.k.a. “learning-to-learn”, uses knowledge obtained from
learning tasks to improve the learning of new ones. Because of the general
terminology, there are several perspectives proposed in the literature that
relate to the topic, such as transfer learning, AutoML, and multi-task
learning [38]. In the context of neural networks, meta-learning has been
framed as an end-to-end pipeline with two levels where an outer algorithm
adjusts the learning of an inner algorithm so that the outer model objective
is improved in the end. In simpler words, it is the search for inductive
biases in a neural network that leads to the fulfillment of a meta-level
objective. This meta-objective can be applied for diverse goals such as
generalization performance, fast adaptation, or even the avoidance of
catastrophic forgetting [39].

The application of meta-learning to solve CL meta-objectives has been referred
to as meta-continual learning (Meta-CL) [40] and can take different forms.
Rajasegaran et al. [41] introduced the use of meta-learning for finding a set
of generic weights that can generalize well for all seen tasks by quickly
adapting to them at test time with minimum forgetting. Javed and White [42]
proposed a meta-objective for finding task-independent network representations
that minimize the forgetting of old tasks and accelerate future learning of
new ones. Beaulieu et al. [34] presented the ANML strategy which uses a
neuromodulatory network to modulate the learning of a base network by gating
the neurons in a specific layer during the forward and backward passes.

Self-Supervision for Continual Learning

Self-supervision is the paradigm in which the data generates its own labels
and learns to predict them back as a pretext task. Some examples of pretext
tasks are colorizing grayscale images, predicting rotation of objects, and
matching different augmented views of the same image [43]. The advantage of
having the data to generate its own supervision signal is to be able to use
large-scale unlabeled datasets and obtain robust representations that can be
used for other downstream tasks such as image classification, object
detection, and semantic segmentation [44]. Recently, self-supervised pre-
trained networks outperformed their supervised counterpart for downstream
tasks of classification and detection in large benchmarks [45, 46].

In the context of CL, the feature extraction backbone is generally frozen for
not allowing gradual changes in the representations during online updates.
This inevitably causes the need for networks that can produce more general
features, which favors the use of self-supervision in their training. In fact,
Gallardo et al. [47] showed empirically that self-supervised pre-trained
models provide representations that generalize better for class-incremental
learning scenarios. Pham et al. [48] proposed a learning structure based on
the human brain complementary learning system, in which a model is optimized
via self-supervision on stored samples to produce general representations that
are then refined by supervised learning for quick knowledge acquisition on the
labeled data. Beyond that, Caccia and Pineau [37] expanded the generality of
self-supervised representations to the meta-learning world by having models
optimized to match different augmented views of the same image and at the same
time generate representations that minimize the forgetting of old classes.

###  2.2 Object Detection with Neural Networks

Object detection is a computer vision task that involves the localization and
classification of items of interest in an image. The goal of an object
detector is to predict the coordinates of each bounding box that surrounds the
objects of interest and assign a category to it. Previous to 2012, most
solutions related to the topic were based on heuristics and hand-crafted
visual descriptors [49, 50] which limited its application in several domains.
After the success that convolutional neural networks (CNNs) had in generating
rich features for classification, they started to compose strategies for the
more challenging task of object localization and recognition [51, 52]. Since
then, they have presented outstanding results in large competitions related to
the detection task and became their baseline solution [53].

Object detectors based on DNNs can usually be divided into two modalities:
two-stage and one-stage detectors. Both have in common the presence of a
backbone network for providing useful feature maps to be used in localization
and identification of object categories [43]. These features can be resumed in
a single 3D tensor extracted directly from the output of a single layer in a
pre-trained architecture (e.g., C4 layer in ResNet-50) or a multi-dimensional
tensor resulting from the gathering of the output of several layers from a
top-down architecture with lateral pathways as in the work of Lin et al. [54].
The backbones used for detection tasks are generally deep CNNs pre-trained on
large image datasets (e.g., ImageNet) intended for classification [55].

####  2.2.1 Two-Stage Detectors

This class of detectors uses a separate structure to generate a set of
“guesses” of where the objects are present in the image. These assumptions on
the image, also called region proposals or just proposals, will be then
classified into the known categories and have their bounding box refined to
correctly identify the object’s limits. R-CNNs [51] were one of the first two-
stage strategies for object detection and used Selective Search [56] for
selecting its region proposals. The problem with this setup was that every
proposal was processed separately by the CNN for feature extraction, which
caused the inference process to be too slow. In the following work of the same
authors, they propose the Fast-RCNN [52] in which a CNN first processes the
image to extract the features maps. Then, the external proposals are used to
select the regions within the feature maps through a Region of Interest (RoI)
pooling layer, to be processed by the classification and regression heads as
illustrated in Figure 2.

Figure 2: Fast-RCNN architecture.

In the work of Ren et al. [12], the authors ceased the use of heuristics for
selecting region proposals by using a separate network called Region Proposal
Network (RPN) able to be optimized specifically for identifying more probable
regions of objects within an image. Their solution used the same structure as
Fast-RCNN. Still, it was way faster than its counterpart, which resulted in it
being named Faster-RCNN. Lin et al. [54] improved the network backbone
performance in generating robust features able to identify smaller objects.
Their strategy, called Feature Pyramid Networks (FPN), exploited the “inherent
multi-scale pyramidal hierarchy” that deep CNNs carry through exploring a top-
down architecture with lateral connections that helps in the propagation of
information from the higher layers to the lower ones.

####  2.2.2 One-Stage Detectors

One-stage models, also known as single-stage detectors, are often faster than
their two-stage counterparts at the cost of having lower predictive
performance [43]. There is no region proposal heuristic or network for this
class of models since it usually considers that every position on the image
might have an object, leaving the model to classify each position as either
background or the target category. The You Only Look Once (YOLO) detector [57]
was one of the first successful models to show a good balance between accuracy
and speed by dividing the whole image into a set of grid cells and predicting
the presence of one or more objects in each of them.

Improving on the inferior ability of the first YOLO architecture for detecting
smaller objects, Liu et al. [58] proposed the Single Shot Multibox Detector
(SSD), which made use of a more elaborated CNN architecture and a set of pre-
defined anchors in multiple scales and aspect-ratios. These additional
features helped the model reach a decent performance while still operating in
real-time. Building on top of that, Lin et al. [59] RetinaNet focused on
dealing with the large number of negative samples that are generated by the
pre-defined anchors using their Focal Loss, which weights down the importance
of easy negative samples while increasing the focus of the network weight
updates on the hard ones. This network also uses FPN in its architecture and
has reached results that compare to Faster-RCNN. An illustration of the
general pipeline used in the YOLO and RetinaNet detectors is shown in Figure
3.

(a) YOLO

(b) RetinaNet

Figure 3: The general pipeline throughout the YOLO and RetinaNet
architectures.

Later on, several versions of the YOLO architecture, which are commonly
referred to as the “YOLO family”, have been proposed and optimized for
decreasing the gap against two-stage models regarding $mAP$ performance [60]
while keeping the real-time characteristic. Moreover, recently a few more
elaborated strategies, such as CenterNet [61] and FCOS [62], that do not make
use of either pre-defined anchor boxes or proposals, have raised the bar for
the performance in popular detection benchmarks.

####  2.2.3 Benchmarks

Training large DNNs requires the availability of large datasets since they
tend to be more accurate as more data gets processed [63]. Considering that
detection annotations are harder to be obtained than just labels for the whole
image, the most popular benchmarks on the topic have become the ones from
competitions organized by resourceful universities or big tech companies. The
two most explored are the Pascal VOC [64] and MS COCO [65]. Although there are
different versions of the datasets based on the year of the challenges,
researchers have adopted the VOC 2007 and COCO 2014 as references. Table 1
displays some statistics related to these benchmarks.

Table 1: Statistics for the main object detection benchmarks [66].

Dataset | VOC 2007 | COCO 2014
---|---|---
Number of classes | 20 | 80
Number of training images (train+val) | 5,011 | 123,287
Number of training instances | 12,608 | 896,782
Number of testing images | 4,952 | 81,434
Mean of bounding boxes per each training image | 2.51 | 7.27

Recently, the LVIS dataset [67] was released with the promise of being a more
complex (and natural) benchmark due to its vast number of categories but a low
amount of samples in some of them. The dataset has over 164,000 images with
more than 1000 categories and 2.2 million high-quality annotations, making it
a tough challenge for generalization on “long-tailed” categories.

####  2.2.4 Evaluation

The evaluation of object detection models is conducted by assessing how much
each predicted bounding box misses or hits a ground truth based on a
threshold. The equation that governs this metric is the intersection over
union (IOU), also known as the Jaccard Index (Equation 5) in which $B_{pred}$
is the coordinate of the predicted bounding box and $B_{gt}$ is the ground
truth equivalent [68]. An illustration of these terms is shown in Figure 4.

$Jaccard\ Index=IOU=\frac{area(B_{pred}\cap B_{gt})}{area(B_{pred}\cup
B_{gt})}$ (5)

Figure 4: Illustration of the Intersection Over Union equation. Image adapted
from Padilla et al. [68].

The threshold value indicates how much overlap is needed to consider that a
prediction was in fact a true positive. Then, the comparison of detection
models can be made by calculating the average precision (AP) (i.e., the ratio
of true positives over the sum of true positives and false positives) and
average recall (AR) (i.e., the ratio of true positives over the sum of true
positives and false negatives) for a given threshold. Equations 6 and 7
describe both metrics.

$Precision=\frac{TP}{TP+FP}=\frac{TP}{all\ detections}$ (6)
$Recall=\frac{TP}{TP+FN}=\frac{TP}{all\ ground\ truths}$ (7)

A common value used for the threshold is $0.5$ (e.g.: $AP^{50}$). The standard
evaluation procedure is to consider the mean average precision ($mAP$) at a
given threshold for all classes that a detector is able to recognize.
Moreover, for better dealing with false negatives, the $mAP$ term is commonly
assigned as the area under the curve (AUC) of the precision against the recall
curve using the specified threshold [68]. In addition to that determination,
for some situations, benchmarks may also use the mean over the average
precision of each class for several thresholds (e.g., $mAP@[.5:.95]$) [65] to
indicate a more stable performance.

##  3 Continual Learning for Object Detection

The general goal of the continual learning paradigm for object detection is to
learn a sequence of tasks $[t_{1},t_{2},t_{3},...]$ and have a model able to
successfully localize and identify all the involved classes at test time as
illustrated by Figure 5.

Figure 5: A generic class-incremental scenario for object detection.

The area of applications of continual learning methods to the object detection
task is still young and in active development [7, 69, 70]. Strategies are
mostly split into two large pools: Class-Incremental Object Detection (CIOD)
and Domain-Incremental Object Detection (DIOD). The former looks at problems
where the model has learned the representation of base classes and then needs
to extend its prediction power over new unknown classes sequentially. The
latter is formed by solutions to problems where the classes are fixed, but
their distribution can change over time. In this situation, the model needs to
be able to identify the classes in both contexts correctly [71].

For DIOD, a recent competition [72] showed through their winning solutions
that general strategies that account mainly for classification might suffice
(e.g., simple random replay, using larger networks) [73, 74, 75] even in
challenging scenarios. For that, we advise the reader to analyze the general
findings and discussions present in related surveys and review papers [3, 1,
20]. Contrastively, the CIOD paradigm needs a more specific treatment due to
its inherent challenges and complexity.

The task of incrementally adding classes to a trained detector is considered
of substantial importance for several applications that deal with memory and
computational constraints [6]. The main issue that makes detection a more
difficult task than only classification for class-incremental scenarios is
that the same image can have several instances of different objects that are
unknown apriori. Since these objects are not identified, the network learns to
treat their visual cues as background instances. Later, when images of the
unknown instances present before are shown as a new class, the model tends to
either not converge to a decent solution or only prioritize the learning of
the new category. In other words, this label conflict favors the interference
on the weights specific to each task within the network.

(a) Learning $t_{1}$

(b) Learning $t_{2}$

(c) Incrementally learning $t_{2}$ after $t_{1}$

(d) Incrementally learning a $t_{3}$ after $[t_{1},t_{2}]$.

Figure 6: Examples of learning separately some task $t_{1}$ and $t_{2}$; and
incrementally learning $[t_{1},t_{2},t_{3}]$.

Figure 6 exemplifies the process of incrementally learning some classes, after
a previous one was already learned. Figures 6(a) and 6(b) shows an example of
two classes being learned separately, whilst Figure 6(c) shows the new class
for task $t_{2}$ being learned after $t_{1}$, at last Figure 6(d) shows a
third class added to the model on the task $t_{3}$. To exemplify why CIOD is
considered a harder task than classification, the class “person” for the first
learning task represented on Figure 6(a) is considered as background on the
second and third tasks (Figure 6(b)). This naturally results in a label
conflict that might induce catastrophic forgetting and harm the final
detection performance.

Although still in its first steps, the CIOD field has a more established
corpus of strategies and some of them can also be applied within the domain-
incremental option [73, 71]. The first proposed strategy for CIOD dates back
to 2017 in the seminal paper written by Shmelkov et al. [7]. Since then,
several methods have been presented with the goal of tackling forgetting while
making DNNs localize and recognize classes incrementally. For a more concise
way of analyzing all the recent contributions to this field, we performed a
systematic review of all the papers that included evaluations within the scope
of continual object detection for class-incremental scenarios.

###  3.1 Considerations about the Literature Review

For gathering the most influential work related to the CIOD field, we took
advantage of the fact that the initial paper of Shmelkov et al. [7] presented
a solid baseline for the problem, which indirectly guided the field to always
make comparisons to it. In this way, we chose to perform a snowballing
literature review followed by the guidelines described on Wohlin [76]. In this
review technique, a paper (or a set of papers) has its citations and
references explored in a forward and backward iterative process in order to
find all works that deal with the topic of interest. A general description of
the review pipeline is described in Figure 7.

Figure 7: The adopted snowballing review process.

Since the research field is reasonably new, some relevant work will certainly
be placed first on arXiv as pre-prints. Because of that, we decided to use the
Google Scholar database for checking the citations and references since they
aggregate all the results from pre-print sources (e.g., arXiv and bioRxiv) to
several popular scientific databases such as IEEE Xplore, ACM Digital Library,
Scopus, and Science Direct.

####  3.1.1 Research Questions

With this review, we aimed to answer the following research questions:

RQ1:

What are the main proposed strategies for CIOD ?

RQ2:

What are the main benchmarks ?

RQ3:

What are the main metrics ?

RQ4:

What is the current state-of-the-art with respect to performance ?

####  3.1.2 Inclusion and Exclusion criteria

For starting the forward and backward process inherent to the snowballing
technique, we considered the following inclusion criteria:

  1. ✓

Papers that cited Shmelkov et al. [7] or appeared in its reference list.

Then, we iteratively checked all the papers that made citations (up to March
2022) or appeared in the reference list of the first pool of gathered papers
and proceeded in a loop until no more studies could be considered. At the same
time, for selecting the works that mattered to this proposal from this large
set, we established the following exclusion criteria:

  1. $\times$

Paper was not written in English.

  2. $\times$

Paper did not propose a technique, benchmark or metric related to the CIOD
paradigm.

  3. $\times$

Paper did not go under the peer-review process or, if published as pre-print
online, did not have citations.

As stated above, we adopted the requirement for citations as a quality measure
only for the works published as pre-prints online. This strategy was adopted
considering that the CIOD field is recent (i.e., many papers will be placed as
pre-prints before being published), and we value public acceptance as a way to
evaluate a paper‘s integrity. After analyzing all related work, 26 research
papers followed the criteria and provided answers to the aspects indicated by
the aforementioned research questions.

###  3.2 Literature Review Results

In this subsection we proceed with the discussion of the review results and
the formulation of answers to each research question.

####  3.2.1 RQ1: What are the main proposed strategies for CIOD?

For dissecting the contributions of each paper, we evaluated the selected
works on the choice of strategy to mitigate forgetting, used architecture and
backbone, benchmarks and evaluation methods. The results are presented in
Table 2 with some colored cells to aid in the analysis.

Table 2: Class-Incremental Object Detection main papers

References | Strategy | Benchmark | Backbone | Object Detector | Evaluation
---|---|---|---|---|---
|  Shmelkov et al. [7] (ILOD)
---
Knowledge Distillation |  | VOC 2007
---
COCO 2014
ResNet-50 | Fast-RCNN |  | Multiple Classes
---
Sequential Classes
Li et al. [77] (MMN) | Parameter Isolation | VOC 2007 | VGG-16 | SSD-300 |  | Multiple Classes
---
Sequential Classes
Guan et al. [78] | Pseudo-Labels |  | VOC 2007
---
TSD-MAX
Darknet-19 | Yolo-V2 | Multiple Classes
Hao et al. [79] (CIFRCN) | Knowledge Distillation |  | VOC 2007
---
COCO 2014
ResNet-101 |  | Faster-RCNN +
---
Nearest Neighbor
| Multiple Classes
---
Sequential Classes
Chen et al. [80] | Knowledge Distillation | VOC 2007 | ResNet | Faster-RCNN |  | Multiple Classes
---
Sequential Classes
Li et al. [81] (RILOD) |  | Knowledge Distillation
---
External Data
| VOC 2007
---
iKitchen
ResNet-50 | RetinaNet |  | Multiple Classes
---
Sequential Classes
Hao et al. [82] (FCIOD) |  | Knowledge Distillation
---
Replay
TGFS | ResNet-101 | Faster-RCNN | Multiple Classes
Liu et al. [83] (IncDet) |  | Pseudo-Labels
---
EWC
| VOC 2007
---
COCO 2014
ResNet-50 |  | Fast-RCNN
---
Faster-RCNN
| Multiple Classes
---
Sequential Classes
|  Acharya et al. [84] (RODEO)
---
Replay |  | VOC 2007
---
COCO 2014
ResNet-50 | Fast-RCNN | Sequential Classes
|  Peng et al. [69] (Faster ILOD)
---
Knowledge Distillation |  | VOC 2007
---
COCO 2014
ResNet-50 | Faster-RCNN |  | Multiple Classes
---
Sequential Classes
Zhou et al. [85] | Knowledge Distillation |  | VOC 2007
---
COCO 2014
ResNet-50 | Faster-RCNN |  | Multiple Classes
---
Sequential Classes
Zhang et al. [86] (DMC) |  | Knowledge Distillation
---
External Data
VOC 2007 |  | ResNet-50 /
---
ResNet-34
RetinaNet | Multiple Classes
Liu et al. [87] (AFD) |  | Knowledge Distillation
---
Replay
| KITTI / Kitchen
---
VOC 2007
COCO 2014
Comic / Watercolor
SE-ResNet-50 | Faster-RCNN | Multiple Classes
Yang et al. [88] |  | Pseudo-Labels
---
Knowledge Distillation
| VOC 2007
---
COCO 2014
ResNet-50 | Faster-RCNN |  | Multiple Classes
---
Sequential Classes
Shieh et al. [36] | Replay |  | VOC 2007
---
ITRI-DriveNet-60
Darknet-53 | Yolo-V3 | Multiple Classes
|  Ramakrishnan et al. [89] (RKT)
---
Knowledge Distillation |  | VOC 2007
---
VOC 2012
KITTI
ResNet | Fast-RCNN |  | Multiple Classes
---
Sequential Classes
Chen et al. [90] | Knowledge Distillation | DOTA / DIOR |  | Custom with
---
FPN
| Custom with
---
two stages
Multiple Classes
Peng et al. [91] (SID) | Knowledge Distillation |  | VOC 2007
---
COCO 2014
ResNet-50 |  | CenterNet
---
FCOS
| Multiple Classes
---
Sequential Classes
Joseph et al. [17] (ORE) |  | Pseudo-Labels
---
Replay
VOC 2007 | ResNet-50 |  | Faster-RCNN +
---
Nearest Neighbor
Multiple Classes
Yang et al. [70] | Knowledge Distillation |  | VOC 2007
---
COCO 2014
ResNet-50 | Faster-RCNN |  | Multiple Classes
---
Sequential Classes
Yang et al. [92] | Replay | VOC 2007 | ResNet-50 | Faster-RCNN | Multiple Classes
Kj et al. [93] (Meta-ILOD) |  | Knowledge Distillation
---
Replay Meta-Learning
| VOC 2007
---
COCO 2014
ResNet-50 | Faster-RCNN |  | Multiple Classes
---
Sequential Classes
ul Haq et al. [94] | Knowledge Distillation | VOC 2007 | Darknet-53 | Yolo-V3 | Sequential Classes
Zhang et al. [95] | Parameter Isolation | VOC 2007 |  | Darknet-53 +
---
ResNet
Yolo-V3 | Sequential Classes
Dong et al. [96] |  | Knowledge Distillation
---
External Data
| VOC 2007
---
COCO 2014
ResNet-50 | Faster-RCNN |  | Multiple Classes
---
Sequential Classes
Wang et al. [97] | - | OAK | ResNet-50 | Faster-RCNN | Sequential Classes

Knowledge Distillation

As mentioned previously, Shmelkov et al. [7] introduced the first work to deal
with the CIOD problem through the use of “vanilla” knowledge distillation. The
authors adapted the Fast-RCNN architecture to learn incrementally by using a
copy of the network trained on the base classes as the teacher and another as
the student. The teacher has its weights frozen and the student has to not
only detect the newly introduced categories but also repeat the distribution
of responses of the frozen teacher. This behavior is achieved by using an
additional regularization loss based on the bounding box predictions and
logits produced by both networks, inspired by the work of Li and Hoiem [32].
Since they were the first to propose a strategy for this problem, most
consecutive papers built solutions on top of their initial regularization
approach and compared them to it.

Hao et al. [79] adapted the Faster-RCNN architecture to the CIOD context with
the expansion of the RPN to consider the new class as foreground. They
evaluated the classification results using a fully connected network and a
nearest prototype classifier. Additionally, they artificially avoided the
possibility of background label conflict between old and new data by excluding
images that contained objects from multiple class groups, which is unreal for
a real-world setting. In a similar strategy, Chen et al. [90] expanded the RPN
for dealing with new classes and used knowledge distillation on the outputs of
a teacher network to allow the model to detect remote sensing objects
incrementally with minimum forgetting using the specific domain datasets
proposed by Li et al. [98] and Xia et al. [99]. Zhou et al. [85] applied
distillation on the detection heads and RPN outputs along with a supplementary
sampling strategy to select proposals that tend to be from the foreground
classes. Ramakrishnan et al. [89] hypothesized that the relationship between
region proposals and the ground truth annotations encoded the detector‘s
knowledge. In this way, the authors introduced a strategy to select proposals
based on their relation and applied distillation on the filtered samples. ul
Haq et al. [94] evaluated distilling knowledge only on the logits for the
YOLO-V3 architecture in a setting with two classes and showed better results
than other CL strategies.

Beyond the basic distillation of the detector outputs, several methods
proposed additionally to distill intermediate features of the base model. Chen
et al. [80] presented the first work that made use of this type of
distillation through what they named a “hint loss”, but they provided limited
results of their approach. Peng et al. [69] made use of the Faster-RCNN and
introduced an additional adaptive distillation step on the features and RPN
outputs. They additionally investigated the negative impact that having old
class objects within the new class images has on the performance of the RPN
and concluded that it was not that significant, which explains why Faster-RCNN
networks generalize better than solutions with external proposals. Peng et al.
[91] presented the use of distillation not only on intermediate features, but
also on the relations (distances) between features of different samples for
anchor-free object detectors. Yang et al. [70] proposed the preservation of
channel-wise, point-wise, and instance-wise correlations between some feature
maps of the teacher and student networks in order to maintain the performance
on the old classes while optimizing for the new ones.

Replay

Hao et al. [82] employed the use of a small buffer of samples along with
logits distillation to perform better than its competitors in the incremental
learning of common objects from vending machines. Shieh et al. [36] proposed
the use of experience replay with different buffer sizes and the YOLO-V3
architecture for the problem of adding multiple classes at once to an object
detector. They evaluated their approach in a common benchmark and on a private
autonomous driving dataset. Acharya et al. [84] suggested the use of product
quantization to compress feature maps without losing their fine-grained
resolution, which allowed for keeping a low-memory profile while performing
well on some incremental benchmarks. Liu et al. [87] presented the use of an
adaptive exemplar sampling for selecting replay instances and proposed
different ways of applying the attention mechanism within the feature
distillation procedure as a strategy to hinder forgetting. They evaluated
their approach on various benchmarks and diverse scenarios in which the
incremental data did not share the same domain as the base classes. Yang et
al. [92] proposed the use of a pre-trained language model to constrain the
topology of the feature space within the model and capture the nuances of
semantic relations associated with each class name. Their solution was meant
to be used for open-world object detection. Still, it can also deal with
incremental detection by using a replay buffer with prototypes for each class
to prevent forgetting old categories.

Parameter Isolation

Li et al. [77] introduced a simple strategy for dealing with forgetting based
on “mining” important network parameters and freezing them. For each task,
they sorted the weight parameters by their magnitude and stored their values
and positions in a memory buffer so that when training for the next task, the
parameters would be reset to their original values. Zhang et al. [95] proposed
a compositional architecture based on the mixture of compact expert detectors.
They trained a YOLO-V3 network using a sparse mechanism for each detection
task and then applied the pruning technique suggested by Liu et al. [100] to
eliminate unimportant channels and residual blocks. For selecting which expert
to forward the inputs, they used a ResNet-50 classifier as the “oracle”. Their
strategy presented interesting results since the final model was able to keep
a low memory footprint and no forgetting of old classes. Yet, their system was
evaluated in a limited scenario with only three incremental tasks, making it
difficult to compare to other techniques.

Pseudo-Labels

Guan et al. [78] showed that when the base classes instances are also present
in the images of the incremental categories, self-labeling using the own model
could be a good enough strategy for dealing with forgetting. Liu et al. [83]
identified that pseudo-labels are an essential step when one wants to
regularize the weight of a network with EWC. Moreover, they also introduced a
novel Huber regularization loss for constraining the gradients of each
parameter based on their relevance to the old classes. Yang et al. [88]
presented the use of pseudo-labels on the new classes images along with the
application of general feature and output distillation and the learning of a
residual model to compensate for the discrepancies between the teacher and
student networks. Joseph et al. [17] suggested the application of self-
labeling to identify potential unknown objects on an image for open-world
object detection. To prevent forgetting, they save a replay buffer with class
prototypes and apply contrastive clustering in the feature space so that new
classes can be added sequentially.

External Data

Li et al. [81] used a one-stage detector (RetinaNet) and not only distilled
the knowledge of outputs and intermediate features but also idealized a way to
automatically collect and annotate new data from search engines such as the
Google Image Search tool to be used during the incremental training and
testing schemes for improved performance. Zhang et al. [86] proposed the
independent training of one-stage networks on the base and new classes and the
further transfer of their specific knowledge to a new separate network via
knowledge distillation using an external unlabeled dataset. Dong et al. [96]
explored the scenario of non co-occurrence of old classes in new classes
images. They proposed a blind sampling strategy to select samples from large
labeled in-the-wild datasets (e.g., COCO). To prevent forgetting, they
designed a distillation strategy based on the remodeled output of the
detection head, RoI masks on the image-level, and heatmaps on the instance-
level.

Meta-Learning

Kj et al. [93] produced a hybrid strategy that relied on knowledge
distillation, replay, and meta-learning to avoid forgetting. Along with the
use of knowledge distillation on the outputs and backbone features, they used
the gradient conditioning technique proposed by Flennerhag et al. [39] to
regularize the weight updates on some layers of the detector RoI head. This
technique granted the ability of fast adaptation by fine-tuning with data from
new classes and a few samples of old ones from a replay buffer.

####  3.2.2 RQ2: What are the main benchmarks?

Every benchmark designed for general object detection can be adjusted for the
class-incremental paradigm. This can be achieved by allowing the model only to
see the instances from the classes of interest, making sure to omit the
annotations related to the categories that are not part of the current
experience. Most of the strategies presented in Table 2 were compared using
the adaptation of the traditional VOC and COCO benchmarks with some caveats to
make them incremental by introducing classes sequentially in single units or
pre-defined groups. For the incremental version of the VOC dataset, classes
were alphabetically ordered and generally split into four different scenarios,
as illustrated by Figure 8.

Figure 8: Description of some of the adopted incremental scenarios for the
Pascal VOC 2007 dataset.

For creating the incremental scheme for the COCO dataset, classes are ordered
following the ID of the original labels and split in half to create a unique
scenario with 40 classes for training the base model and 40 to be added
sequentially as shown in Figure 9.

Figure 9: Description of an incremental scenario with MS COCO 2014 dataset.

Although not well explored, there were a few datasets designed for evaluating
CIOD solutions. Hao et al. [82] introduced a large-scale dataset of vending
machine products with 38k images and 24 possible categories called Take Goods
from Shelves (TGFS). The benchmark also has three coarse classes that cover
the categories and was meant to instigate class-incremental detection
solutions to retail problems. Wang et al. [97] proposed an egocentric video
dataset that focused on capturing objects and scenes present in the daily life
of a university student. The benchmark is called Objects Around Krishna (OAK)
and was meant to be used for online continual object detection tasks.

####  3.2.3 RQ3: What are the main metrics?

The evaluation for CIOD has followed the same structure of traditional object
detection with the use of<EMAIL_ADDRESS>for nearly all benchmarks and
<EMAIL_ADDRESS>for COCO like datasets. However, some researchers noticed that
directly comparing the $mAP$ performance of techniques on the same benchmark
would not assess their real efficiency since changes in the training regime,
and even framework could cause the same method to present different results.
To comply with that, the difference and the ratio against the upper-bound
(i.e., joint-training with all classes at once) have been commonly used for
comparisons [83, 84] since they represent how the performance would be in case
data could be fully accumulated and create a common ground between techniques
(i.e., how far we are from the ideal response). Yet, the gap against the
joint-training is only meaningful when both methods are implemented within the
same training regime and framework since only in this situation it is possible
to ascertain which single components really contributed to narrowing the gap.
Most researchers do not consider this setting and pick up results from
different papers to compare against their joint-training outcomes, which does
not give more information than checking their single $mAP$ results.

Beyond that, Chen et al. [80] proposed the use of a $F_{map}$ metric, inspired
by the $F_{1}-score$, in which they calculate the harmonic mean between the
$mAP$ values of old and new classes as described by Equation 8.

$F_{map}=\frac{2\ \ mAP_{old}\ \ mAP_{new}}{mAP_{old}+mAP_{new}}$ (8)

Yang et al. [70] introduced a metric called Stability-Plasticity-mAP $SPmAP$
that considers how much the incremental learning process affects the average
stability and plasticity of a detector. Their metric takes into consideration
the mean differences of the incremental model against the upper-bound for the
old and new classes as shown by Equation 9.

$SPmAP=\frac{\frac{Stability+Plasticity}{2}+mAP_{dif}}{2}$ (9)

$Stability=\frac{1}{N_{old\\_classes}}\sum_{i=1}^{N_{old\\_classes}}(mAP_{joint,i}-mAP_{inc,i})$

$Plasticity=\frac{1}{N_{new\\_classes}}\sum_{i=N_{old\\_classes}+1}^{N_{all\\_classes}}(mAP_{joint,i}-mAP_{inc,i})$

$mAP_{dif}=\frac{1}{N_{all\\_classes}}\sum_{i=1}^{N_{all\\_classes}}(mAP_{joint,i}-mAP_{inc,i})$

We also believe that CIOD models can only be compared when the join-training
results of their architecture are available. However, only looking at the
discrepancy between the incremental and joint-training models does not lead to
the understanding of which specific aspects of the strategy are failing. The
aforementioned metrics are helpful, but they lack the specificity for
identifying where the incremental model should pay attention. To circumvent
that, we propose two separate metrics that compare and scale the final
incremental $mAP$ values for each class against the joint-training separately
for the old and new categories. These metrics are defined as the rate of
stability (RSD) and plasticity (RPD) deficits as described in Equations 10 and
11.

$\text{RSD}=\frac{1}{N_{old\\_classes}}\sum_{i=1}^{N_{old\\_classes}}\frac{mAP_{joint,i}-mAP_{inc,i}}{mAP_{joint,i}}\
*100$ (10)

$\text{RPD}=\frac{1}{N_{new\\_classes}}\sum_{i=N_{old\\_classes}+1}^{N_{new\\_classes}}\frac{mAP_{joint,i}-mAP_{inc,i}}{mAP_{joint,i}}\
*100$ (11)

Our metrics allow the direct interpretation of how much an incremental model
compares to the upper-bound in remembering old classes and learning the new
ones (e.g., the model has a 10% worse performance for recognizing previous
classes, but only a 2% deficit for learning new categories when compared to
the upper-bound). Therefore, for this context, a CIOD strategy should aim not
only to reach a decent final $mAP$ value and high upper-bound ratio but also
to keep low and balanced stability and plasticity deficits. Additionally, the
ratios can assume negative values, indicating that the incremental model has
performed better than joint-training for some classes and reinforcing the
relationship with standard CL metrics such as BWT. We applied the rate of
plasticity and stability deficits along with the upper-bound difference during
the performance analysis of the following section.

####  3.2.4 RQ4: What is the current state-of-the-art with respect to
performance?

For performing an investigation on which strategies have worked better for
CIOD, we need to find common ground among them. Yet, there are no standards
for frameworks, architecture backbones, and training regimes regarding paper
reimplementations. Some papers tried to replicate the number of iterations,
learning rates, and procedures used by Shmelkov et al. [7], but there is a
clear difference in the obtained results that can be seen mainly for the joint
training cases that used the same architecture [69, 79]. In this way, it is
difficult to state that some results are better than others because of the
proposed policies and not due to the better selection of hyperparameters,
which has been shown previously to highly influence generalization in the CL
setting [101]. Therefore, using a consistent evaluation procedure is essential
for identifying the most promising directions in the field.

Tables 3, 4 and 5, present the results of each paper that was evaluated on the
PASCAL VOC 2007 and MS COCO 2014 following the main benchmarks described in
Section 3.2.2 for when multiple and singles classes are added sequentially.
The metrics proposed in Section 3.2.3 are used for evaluating the real impact
of each strategy according to their upper-bound. Because our metrics also need
access to the $mAP$ of each class for both the incremental and joint-training
models, some previously discussed works have a $\dagger$ symbol that indicates
that the paper only provided the mean $mAP$ value for the old and new classes
in groups for each setting.

In Table 3, by looking at the final $mAP$ and the values of the upper-bound
ratios for all incremental scenarios, it is possible to conclude that for the
VOC benchmark, as more classes are added at once, the more complex the task
becomes for the detectors. Strategies based on pseudo-labels and replay
demonstrated consistent results. In contrast, pure knowledge distillation
based techniques struggled more and had an average plasticity deficit of more
than 10%, which might be an indication that this type of regularization needs
to be adjusted carefully for not harming the learning of new categories.
Nevertheless, for the settings with 5 and 10 classes added at once, although
the initial baseline from Shmelkov et al. [7] presented a final $mAP$ often
below its competitors, it also offered a good balance of stability and
plasticity according to its upper-bound, which contributes to why this
technique is still relevant for comparisons.

Table 3: VOC 2007 results for one or multiple classes added at once

|  | VOC 2007 Incremental (1-19 + 20)
---|---|---
Paper |  | Final mAP | $\Omega_{all}\uparrow$ | RSD (%) $\downarrow$ | RPD (%) $\downarrow$
|  Shmelkov et al. [7] (ILOD)
---
| 68.40 | 0.980 | 1.90 | 21.11
Li et al. [77] (MMN) |  | 77.50 | 0.991 | 1.09 | -4.24
Li et al. [81] (RILOD) |  | 65.00 | 0.870 | 10.93 | 48.67
|  Peng et al. [69] (Faster ILOD)$\dagger$
---
| 68.56 | 0.972 | 0.60 | 44.27
Zhou et al. [85]$\dagger$ |  | 69.60 | 0.991 | -0.45 | 24.82
Zhang et al. [86] (DMC) |  | 70.80 | 0.948 | 4.80 | 12.33
Yang et al. [88] |  | 72.13 | 0.977 | 0.93 | 29.14
Shieh et al. [36] |  | 68.90 | 0.941 | 3.41 | 53.56
|  Ramakrishnan et al. [89] (RKT)
---
| 67.20 | 0.984 | 1.00 | 14.29
Peng et al. [91] (SID)$\dagger$ |  | 68.30 | 0.954 | 4.61 | 4.61
Joseph et al. [17] (ORE) |  | 68.89 | 0.977 | 1.66 | 14.51
Yang et al. [92]$\dagger$ |  | 69.82 | 0.990 | 0.41 | 11.64
Yang et al. [70] |  | 69.70 | 0.973 | 2.11 | 12.17
Kj et al. [93] (Meta-ILOD) |  | 70.20 | 0.934 | 5.82 | 21.74
Dong et al. [96]$\dagger$ |  | 72.20 | 0.999 | -1.38 | 29.88
|  | VOC 2007 Incremental (1-15 + 16-20)
Paper |  | Final mAP | $\Omega_{all}\uparrow$ | RSD (%) $\downarrow$ | RPD (%) $\downarrow$
|  Shmelkov et al. [7] (ILOD)
---
| 65.90 | 0.944 | 3.60 | 12.33
Liu et al. [83] (IncDet)$\dagger$ |  | 70.40 | 0.954 | 0.44 | 12.12
|  Peng et al. [69] (Faster ILOD)$\dagger$
---
| 67.94 | 0.963 | -3.60 | 25.44
Yang et al. [88] |  | 69.71 | 0.944 | 1.92 | 17.75
Peng et al. [91] (SID)$\dagger$ |  | 62.20 | 0.869 | 13.13 | 13.13
Joseph et al. [17] (ORE) |  | 68.51 | 0.972 | 0.44 | 10.71
Yang et al. [92]$\dagger$ |  | 69.93 | 0.992 | -3.55 | 13.93
Yang et al. [70] |  | 66.50 | 0.929 | 5.56 | 11.92
Kj et al. [93] (Meta-ILOD) |  | 67.80 | 0.902 | 6.58 | 20.62
Dong et al. [96]$\dagger$ |  | 65.30 | 0.903 | 2.49 | 31.67
|  | VOC 2007 Incremental (1-10 + 11-20)
Paper |  | Final mAP | $\Omega_{all}\uparrow$ | RSD (%) $\downarrow$ | RPD (%) $\downarrow$
|  Shmelkov et al. [7] (ILOD)
---
| 63.10 | 0.904 | 7.66 | 11.42
Guan et al. [78] |  | 68.80 | 0.922 | 11.06 | 4.68
Chen et al. [80]$\dagger$ |  | 33.50 | 0.474 | 47.05 | 69.02
Li et al. [81] (RILOD) |  | 67.90 | 0.909 | 10.42 | 7.67
Liu et al. [83] (IncDet)$\dagger$ |  | 70.80 | 0.959 | 4.52 | 1.18
|  Peng et al. [69] (Faster ILOD)$\dagger$
---
| 62.16 | 0.881 | -4.79 | 28.50
Zhou et al. [85]$\dagger$ |  | 61.80 | 0.880 | 9.16 | 14.89
Zhang et al. [86] (DMC) |  | 68.30 | 0.914 | 7.63 | 11.29
Yang et al. [88] |  | 66.21 | 0.897 | 5.98 | 14.74
Shieh et al. [36] |  | 65.50 | 0.895 | 8.78 | 14.04
|  Ramakrishnan et al. [89] (RKT)
---
| 63.10 | 0.924 | 1.25 | 13.85
Peng et al. [91] (SID)$\dagger$ |  | 59.80 | 0.835 | 16.48 | 16.48
Joseph et al. [17] (ORE) |  | 64.58 | 0.916 | 14.76 | 2.01
Yang et al. [92]$\dagger$ |  | 64.96 | 0.921 | 14.86 | 0.89
Yang et al. [70] |  | 66.10 | 0.923 | 7.29 | 8.02
Kj et al. [93] (Meta-ILOD) |  | 66.30 | 0.882 | 8.51 | 15.07
Dong et al. [96]$\dagger$ |  | 59.90 | 0.828 | 20.33 | 13.97

Probably due to the increased complexity when working with several online
updates, there were not many solutions to the sequential setting compared to
its counterpart scenario, as shown in Table 4. For when only five classes were
being added sequentially, the parameter isolation strategy of Li et al. [77]
demonstrated outstanding performance in the final $mAP$ and stability-
plasticity metrics. Also, in their unique participation, the RODEO method from
Acharya et al. [84] confirmed that replay is a suitable tool for dealing with
consecutive one-class updates. Interestingly, the IncDet strategy from Liu et
al. [83] performed well on the setup of multiple groups being added
sequentially but seemed to fail when learning single classes alone. This may
be related to how strong the regularization penalty was adjusted to prevent
the parameters from deviating much from the previously known distribution. In
general, although having the same number of classes, the final $mAP$ was
clearly lower in this setting when compared to adding the multiple categories
at once. This corroborates that the “tug-of-war” on the parameters is
happening actively in each new class network update.

Table 4: VOC 2007 results for one or a group of classes added sequentially

|  | VOC 2007 Incremental (1-15 + 16 + … + 20)
---|---|---
Paper |  | Final mAP | $\Omega_{all}\uparrow$ | RSD (%) $\downarrow$ | RPD (%) $\downarrow$
|  Shmelkov et al. [7] (ILOD)
---
| 62.40 | 0.894 | 6.89 | 22.56
Li et al. [77] (MMN) |  | 76.00 | 0.972 | 2.21 | 4.48
Liu et al. [83] (IncDet)$\dagger$ |  | 67.60 | 0.916 | 1.12 | 35.87
Yang et al. [88] |  | 59.62 | 0.807 | 7.98 | 56.45
Peng et al. [91] (SID)$\dagger$ |  | 48.90 | 0.683 | 31.70 | 31.70
Kj et al. [93] (Meta-ILOD) |  | 65.70 | 0.874 | 8.77 | 25.08
|  | VOC 2007 Incremental (1-10 + 11 + … + 20)
Paper |  | Final mAP | $\Omega_{all}\uparrow$ | RSD (%) $\downarrow$ | RPD (%) $\downarrow$
Chen et al. [80]$\dagger$ |  | 33.50 | 0.474 | 47.05 | 69.02
|  Acharya et al. [84] (RODEO)$\dagger$
---
| 63.72 | 0.887 | 13.78 | 8.95
Zhou et al. [85]$\dagger$ |  | 46.20 | 0.658 | 22.46 | 45.82
|  | VOC 2007 Incremental (1-5 + 6-10 + 11-15 + 16-20)
Paper |  | Final mAP | $\Omega_{all}\uparrow$ | RSD (%) $\downarrow$ | RPD (%) $\downarrow$
Hao et al. [79] (CIFRCN) |  | 48.50 | 0.694 | 36.62 | 12.09
Liu et al. [83] (IncDet)$\dagger$ |  | 62.60 | 0.848 | 11.58 | 21.67
Yang et al. [88] |  | 49.05 | 0.664 | 38.50 | 3.00
|  Ramakrishnan et al. [89] (RKT)
---
| 52.90 | 0.775 | 20.56 | 29.23
Peng et al. [91] (SID)$\dagger$ |  | 36.20 | 0.506 | 49.44 | 49.44
Yang et al. [70] |  | 27.66 | 0.386 | 66.30 | 44.77

Considering the results for the COCO incremental benchmark exhibited in Table
5, techniques based on Faster-RCNN and feature distillation presented decent
results. Even though the number of classes is higher than in the VOC
benchmark, the upper-bound ratio shows that as the network is updated all at
once, the forgetting condition is not as strong as in the sequential update
example. Beyond that, methods based on self-labeling demonstrated results that
justify their effectiveness for dealing with scenarios where a substantial
number of classes was already introduced.

Table 5: MS COCO 2014 results for multiple classes being added at once

|  | MS COCO Incremental (1-40 + 41-80)
---|---|---
Paper |  |<EMAIL_ADDRESS>|<EMAIL_ADDRESS>.95] |  $\Omega_{all}\<EMAIL_ADDRESS>
|  Shmelkov et al. [7] (ILOD)
---
| 37.40 | 21.30 | 0.982
Liu et al. [83] (IncDet)$\dagger$ |  | 49.30 | 29.70 | 0.978
|  Peng et al. [69] (Faster ILOD)$\dagger$
---
| 40.10 | 20.64 | 0.939
Zhou et al. [85]$\dagger$ |  | 36.80 | 22.70 | 0.868
Yang et al. [88] |  | 43.75 | 24.23 | 0.882
Peng et al. [91] (SID)$\dagger$ |  | 41.60 | 25.20 | 0.885
Yang et al. [70] |  | 44.62 | - | 0.854
Kj et al. [93] (Meta-ILOD) |  | 40.50 | 23.80 | 0.794
Dong et al. [96]$\dagger$ |  | 40.90 | 22.50 | 0.893

Overall, it is clear that all methods suffered from some aspect of forgetting
and were limited to the joint-training baseline in all benchmarks. It is
important to mention that this might not always be the case and that some CL
strategies may surpass this baseline, which has been the case for some based
on parameter freezing mechanisms [27]. The RKT strategy proposed by [89] was
the best knowledge distillation method on the benchmarks it participated;
however, most of the pure distillation-based techniques presented low
plasticity probably due to the constraints imposed on the original weights.
The IncDet model, which involved EWC regularization and pseudo-labeling,
showed the most consistent results in all evaluated benchmarks. Yet, some
strategies based on parameter isolation and replay that did well in individual
benchmarks, such as MMN and ORE, demonstrated that there is still room for
exploring alternatives and possibly combining them.

This review did not consider other desired characteristics for the CL
desiderata, such as the low memory footprint, which usually tends to overthrow
parameter isolation strategies and fast adaptability to new categories. The
meta-learning hybrid method from Kj et al. [93] presented results slightly
superior to other knowledge distillation techniques. Regardless, the method
quickly adapted to new tasks using only 10 replay samples for each category
during fine-tuning. In this way, considering the CL desiderata for object
detection discussed in Section 2.1, meta-learning hybrid methods can play an
interesting role in class-incremental scenarios and should be more
investigated.

##  4 Trends and Research Directions

Considering the main takeaways of the previous systematic review, in this
section, we briefly discuss some of the observed trends and possible research
directions in the CIOD field.

Hybrid methods prevent more forgetting: The best performing solutions to the
class-incremental problem in object detection involved a combination of
techniques to avoid catastrophic forgetting. This outcome agrees with the
findings from other computer vision tasks [102] and corroborates with the fact
that even the brain has multiple ways to prevent subtle task interference
[103]. One key point common to most hybrid methods was the fine-tuning on new
classes given the representation of old categories using pseudo-labels or
replay samples. This fine-tuning resulted in better results but can require a
large buffer of samples and, similarly, an extensive hyperparameter search
which might prevent its application in the real-world.

Knowledge Distillation: a strong baseline with caveats: It is easy to notice
from Table 2 that most proposed strategies in the CIOD field use knowledge
distillation as their primary mechanism to mitigate the effects of
catastrophic forgetting. Comparing the results of the selected papers on the
PASCAL VOC 2007 and MS COCO incremental benchmarks considering the metrics
that assess the stability-plasticity of solutions, the differences between a
recently proposed distillation technique such as Peng et al. [91] and the
first work of Shmelkov et al. [7] are subtle. This either means that
researchers might have been overfitting their solutions to the benchmarks or
that simple logits and bounding box distillation are a strong baseline. We
believe the latter to be a more reasonable explanation.

Working towards the CL Desiderata for object detection: The majority of the
currently published CL research is done focusing on improving the last 0.01%
of performance, sometimes considering unrealistic scenarios (e.g., use of task
labels at test time). However, for real-world focused applications, strategies
should also contemplate practical implementation aspects such as the
computational burden and frequency of updates for the model. For class-
incremental detectors, the desiderata described in Section 2.1 give an
intuition of the main aspects that future research could focus on in order to
increase practical adoption for researchers in academia and industry.

Working towards the standardization of implementations: Research in CIOD
suffers from poor standardization and has not fully adopted the advent already
developed by the CL community for reproducibility, such as the Avalanche and
Continuum libraries [104, 105]. Besides that, there is no standard
implementation for most of the discussed solutions to leverage fair
comparisons. Although some available implementations are provided using the
Detectron2 framework or its old form [69, 17, 93], the interpretation of the
changes to the original framework that are needed to reach the same results is
often difficult due to the abstract structure of its repository. One step
towards improving on this issue is the open-sourcing of the code for the
regular baselines evaluated when proposing a new benchmark. Ideally, the
implementation should envision using a well-established framework specific to
the field (e.g., Avalanche), where a better description of the differences and
human-readable code can be maintained. Nevertheless, the metrics proposed in
this paper are also available as a tool for performing honest comparisons
between solutions for the same benchmark.

Overcoming the overestimation of results: As also found in the recent survey
for few-shot object detection Huang et al. [43], most works evaluated on the
VOC and COCO datasets are using their training and validation splits for
fitting the models and the testing set for selecting hyperparameters. This can
lead to an overestimation of results and generalization problems when
selecting techniques for good performance in the real world. A straightforward
fix would be to use the original train/val/test splits as indicated by the
datasets organizers and not perform contradictory actions to favor the
proposed methods. Yet, as most researchers are using this setup to report
their performance on the current benchmarks, it is sadly expected that the
follow-up papers still keep the same choice of splits. We believe researchers
should be more careful when proposing and evaluating their strategies for new
incremental benchmarks to ensure a not biased outcome.

##  5 Related fields

Some related computer vision tasks already involve components that deal with
incremental object detection in their pipeline. This section discusses a few
of them shortly to make it possible for the readers to connect with other
fields that can inspire and contribute to the current research of continual
object detectors.

###  5.1 Open-World Object Detection

The set of possible objects that a detector can encounter at test time in the
wild is limitless. For dealing with the unknown and adapting to it, the field
of open-world object detection has emerged as a possible solution to unify the
paradigms of open-set and open-world recognition to class-incremental learning
with object detectors [15, 106]. The solutions in this category are usually
the combination of a structure to detect out-of-distribution samples (i.e.,
unknown objects) and a specific module to allow learning from them in an
incremental manner. By modeling the unknown, researchers believe it is
possible to reduce the label conflict and therefore enable more autonomous
detection pipelines Joseph et al. [17].

###  5.2 Incremental Few-shot object detection

When learning incrementally in robotics applications, models can be required
to learn from data streams with only a few batches and several unseen classes.
This scenario makes it difficult to apply the traditional batch learning used
with neural networks and therefore needs a particular solution. The field of
Incremental Few-Shot Object Detection (iFSD) looks for fast adaption of a
trained network in situations of a low-data regime for learning novel classes
[107]. This scenario is naturally more difficult than the plain CIOD paradigm
since it assumes no large dataset is provided. Arguably, the current results
on their benchmarks show a trend to focus more on the adaption to new classes
than on avoiding the forgetting of old ones [108]. This might indicate that
research should be directed first at how to solve a less complicated problem
(i.e., class-incremental object detection with large batches), which can give
hints on how to move forward to more complex scenarios.

###  5.3 Continual Semantic Segmentation

The field of Continual Semantic Segmentation deals with the same difficulties
of continual object detection (e.g., background label conflict) but at the
pixel level. Most of the current solutions that have excelled in the field
involve the techniques described in this review, such as pseudo-labeling [109]
and knowledge distillation [110, 111]. Its application has a direct impact on
real-world robotics navigation and should always be looked at closely by CIOD
researchers for insights.

###  5.4 Zero-shot Object Detection

The Zero-Shot Object Detection paradigm consists of learning to detect new
categories that are not present in the training set by using non-visual
features that describe them [112]. Specifically, a pre-trained language model
was originally used to model the semantics associated with the class labels.
These relations were then used to guide the learning and inference of new
unseen classes by a detector. The method proposed by Yang et al. [92], which
was described in Section 3.2.1, combined a zero-shot strategy with exemplar
replay and showed decent results not only for open-world recognition (their
primary goal) but also for some CIOD benchmarks. This might be an indication
that innovations can be appropriately adapted and shared among these fields.

##  6 Conclusions

This short systematic review investigated how continual learning solutions
have been applied to object detection tasks covering the topic‘s technical
background up to the most explored benchmarks, metrics, and strategies.

For the literature review, we analyzed the reported performance of the leading
papers in a popular benchmark for the class-incremental scenario with the lens
of a new metric explicitly proposed to look at how well a detector adapts and
maintains its internal knowledge. We found out that even though most of the
current research appeals to the single use of regularization-based techniques,
specifically knowledge distillation, the methods that presented the best
overall results on the evaluated benchmarks usually combine such techniques
with replay, self-labeling, and meta-learning.

Finally, we discussed some of the main trends in the field, pitfalls and how
researchers may avoid them, and a few related tasks that can inspire the
proposal of new methods and possible future research intersections.

## Acknowledgments

This study was funded in part by the Coordenação de Aperfeiçoamento de Pessoal
de Nível Superior - Brasil (CAPES) - Finance Code 001. The authors also would
like to thank the Eldorado Research Institute for supporting this research.

## References

  * Hadsell et al. [2020] R. Hadsell, D. Rao, A. A. Rusu, R. Pascanu,  Embracing change: Continual learning in deep neural networks,  Trends in cognitive sciences (2020).
  * Robins [1995] A. Robins,  Catastrophic forgetting, rehearsal and pseudorehearsal,  Connection Science 7 (1995) 123–146.
  * Parisi et al. [2019] G. I. Parisi, R. Kemker, J. L. Part, C. Kanan, S. Wermter,  Continual lifelong learning with neural networks: A review,  Neural Networks 113 (2019) 54–71.
  * Díaz-Rodríguez et al. [2018] N. Díaz-Rodríguez, V. Lomonaco, D. Filliat, D. Maltoni,  Don’t forget, there is more than forgetting: new metrics for continual learning,  arXiv preprint arXiv:1810.13166 (2018).
  * Ross et al. [2008] D. A. Ross, J. Lim, R.-S. Lin, M.-H. Yang,  Incremental learning for robust visual tracking,  International journal of computer vision 77 (2008) 125–141.
  * Shaheen et al. [2021] K. Shaheen, M. A. Hanif, O. Hasan, M. Shafique,  Continual learning for real-world autonomous systems: Algorithms, challenges and frameworks,  arXiv preprint arXiv:2105.12374 (2021).
  * Shmelkov et al. [2017] K. Shmelkov, C. Schmid, K. Alahari,  Incremental learning of object detectors without catastrophic forgetting,  in: Proceedings of the IEEE international conference on computer vision, 2017, pp. 3400–3409.
  * Thrun [1995] S. Thrun, Lifelong Learning: A Case Study., Technical Report, Carnegie-Mellon Univ Pittsburgh pa Dept of Computer Science, 1995.
  * Silver [2011] D. L. Silver,  Machine lifelong learning: challenges and benefits for artificial general intelligence,  in: International conference on artificial general intelligence, Springer, 2011, pp. 370–375.
  * Clune [2019] J. Clune,  Ai-gas: Ai-generating algorithms, an alternate paradigm for producing general artificial intelligence,  arXiv preprint arXiv:1905.10985 (2019).
  * Rebuffi et al. [2017] S.-A. Rebuffi, A. Kolesnikov, G. Sperl, C. H. Lampert,  icarl: Incremental classifier and representation learning,  in: Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, 2017, pp. 2001–2010.
  * Ren et al. [2015] S. Ren, K. He, R. Girshick, J. Sun,  Faster r-cnn: Towards real-time object detection with region proposal networks,  Advances in neural information processing systems 28 (2015).
  * Devlin et al. [2018] J. Devlin, M.-W. Chang, K. Lee, K. Toutanova,  Bert: Pre-training of deep bidirectional transformers for language understanding,  arXiv preprint arXiv:1810.04805 (2018).
  * Aljundi [2019] R. Aljundi,  Continual learning in neural networks,  arXiv preprint arXiv:1910.02718 (2019).
  * Mundt et al. [2020] M. Mundt, Y. W. Hong, I. Pliushch, V. Ramesh,  A wholistic view of continual learning with deep neural networks: Forgotten lessons and the bridge to active and open world learning,  arXiv preprint arXiv:2009.01797 (2020).
  * Ahn et al. [2019] H. Ahn, S. Cha, D. Lee, T. Moon,  Uncertainty-based continual learning with adaptive regularization,  Advances in Neural Information Processing Systems 32 (2019).
  * Joseph et al. [2021] K. Joseph, S. Khan, F. S. Khan, V. N. Balasubramanian,  Towards open world object detection,  in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 5830–5840.
  * Mundt et al. [2021] M. Mundt, S. Lang, Q. Delfosse, K. Kersting,  Cleva-compass: A continual learning evaluation assessment compass to promote research transparency and comparability,  arXiv preprint arXiv:2110.03331 (2021).
  * Lomonaco and Maltoni [2017] V. Lomonaco, D. Maltoni,  Core50: a new dataset and benchmark for continuous object recognition,  in: Conference on Robot Learning, PMLR, 2017, pp. 17–26.
  * Delange et al. [2021] M. Delange, R. Aljundi, M. Masana, S. Parisot, X. Jia, A. Leonardis, G. Slabaugh, T. Tuytelaars,  A continual learning survey: Defying forgetting in classification tasks,  IEEE Transactions on Pattern Analysis and Machine Intelligence (2021).
  * Van de Ven and Tolias [2019] G. M. Van de Ven, A. S. Tolias,  Three scenarios for continual learning,  arXiv preprint arXiv:1904.07734 (2019).
  * Aljundi et al. [2019] R. Aljundi, K. Kelchtermans, T. Tuytelaars,  Task-free continual learning,  in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019, pp. 11254–11263.
  * Normandin et al. [2021] F. Normandin, F. Golemo, O. Ostapenko, P. Rodriguez, M. D. Riemer, J. Hurtado, K. Khetarpal, D. Zhao, R. Lindeborg, T. Lesort, et al.,  Sequoia: A software framework to unify continual learning research,  arXiv preprint arXiv:2108.01005 (2021).
  * Lopez-Paz and Ranzato [2017] D. Lopez-Paz, M. Ranzato,  Gradient episodic memory for continual learning,  in: Advances in Neural Information Processing Systems, 2017, pp. 6467–6476.
  * Hayes et al. [2018] T. L. Hayes, R. Kemker, N. D. Cahill, C. Kanan,  New metrics and experimental paradigms for continual learning,  in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2018, pp. 2031–2034.
  * Rumelhart [1992] D. E. Rumelhart,  Reducing interference in distributed memories through episodic gating,  From learning theory to connectionist theory 1 (1992) 227.
  * Yoon et al. [2017] J. Yoon, E. Yang, J. Lee, S. J. Hwang,  Lifelong learning with dynamically expandable networks,  arXiv preprint arXiv:1708.01547 (2017).
  * Mallya et al. [2018] A. Mallya, D. Davis, S. Lazebnik,  Piggyback: Adapting a single network to multiple tasks by learning to mask weights,  in: Proceedings of the European Conference on Computer Vision (ECCV), 2018, pp. 67–82.
  * Rusu et al. [2016] A. A. Rusu, N. C. Rabinowitz, G. Desjardins, H. Soyer, J. Kirkpatrick, K. Kavukcuoglu, R. Pascanu, R. Hadsell,  Progressive neural networks,  arXiv preprint arXiv:1606.04671 (2016).
  * Hung et al. [2019] C.-Y. Hung, C.-H. Tu, C.-E. Wu, C.-H. Chen, Y.-M. Chan, C.-S. Chen,  Compacting, picking and growing for unforgetting continual learning,  Advances in Neural Information Processing Systems 32 (2019).
  * Kirkpatrick et al. [2017] J. Kirkpatrick, R. Pascanu, N. Rabinowitz, J. Veness, G. Desjardins, A. A. Rusu, K. Milan, J. Quan, T. Ramalho, A. Grabska-Barwinska, et al.,  Overcoming catastrophic forgetting in neural networks,  Proceedings of the national academy of sciences 114 (2017) 3521–3526.
  * Li and Hoiem [2017] Z. Li, D. Hoiem,  Learning without forgetting,  IEEE transactions on pattern analysis and machine intelligence 40 (2017) 2935–2947.
  * Pellegrini et al. [2019] L. Pellegrini, G. Graffieti, V. Lomonaco, D. Maltoni,  Latent replay for real-time continual learning,  arXiv preprint arXiv:1912.01100 (2019).
  * Beaulieu et al. [2020] S. Beaulieu, L. Frati, T. Miconi, J. Lehman, K. O. Stanley, J. Clune, N. Cheney,  Learning to continually learn,  arXiv preprint arXiv:2002.09571 (2020).
  * Shin et al. [2017] H. Shin, J. K. Lee, J. Kim, J. Kim,  Continual learning with deep generative replay,  Advances in neural information processing systems 30 (2017).
  * Shieh et al. [2020] J.-L. Shieh, M. A. Haq, S. Karam, P. Chondro, D.-Q. Gao, S.-J. Ruan, et al.,  Continual learning strategy in one-stage object detection framework based on experience replay for autonomous driving vehicle,  Sensors 20 (2020) 6777.
  * Caccia and Pineau [2021] L. Caccia, J. Pineau,  Special: Self-supervised pretraining for continual learning,  arXiv preprint arXiv:2106.09065 (2021).
  * Hospedales et al. [2020] T. Hospedales, A. Antoniou, P. Micaelli, A. Storkey,  Meta-learning in neural networks: A survey,  arXiv preprint arXiv:2004.05439 (2020).
  * Flennerhag et al. [2019] S. Flennerhag, A. A. Rusu, R. Pascanu, F. Visin, H. Yin, R. Hadsell,  Meta-learning with warped gradient descent,  arXiv preprint arXiv:1909.00025 (2019).
  * Caccia et al. [2020] M. Caccia, P. Rodriguez, O. Ostapenko, F. Normandin, M. Lin, L. Page-Caccia, I. H. Laradji, I. Rish, A. Lacoste, D. Vázquez, et al.,  Online fast adaptation and knowledge accumulation (osaka): a new approach to continual learning,  Advances in Neural Information Processing Systems 33 (2020) 16532–16545.
  * Rajasegaran et al. [2020] J. Rajasegaran, S. Khan, M. Hayat, F. S. Khan, M. Shah,  itaml: An incremental task-agnostic meta-learning approach,  in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 13588–13597.
  * Javed and White [2019] K. Javed, M. White,  Meta-learning representations for continual learning,  Advances in Neural Information Processing Systems 32 (2019).
  * Huang et al. [2021] G. Huang, I. Laradji, D. Vazquez, S. Lacoste-Julien, P. Rodriguez,  A survey of self-supervised and few-shot object detection,  arXiv preprint arXiv:2110.14711 (2021).
  * Jing and Tian [2020] L. Jing, Y. Tian,  Self-supervised visual feature learning with deep neural networks: A survey,  IEEE transactions on pattern analysis and machine intelligence 43 (2020) 4037–4058.
  * Caron et al. [2020] M. Caron, I. Misra, J. Mairal, P. Goyal, P. Bojanowski, A. Joulin,  Unsupervised learning of visual features by contrasting cluster assignments,  Advances in Neural Information Processing Systems 33 (2020) 9912–9924.
  * Bar et al. [2021] A. Bar, X. Wang, V. Kantorov, C. J. Reed, R. Herzig, G. Chechik, A. Rohrbach, T. Darrell, A. Globerson,  Detreg: Unsupervised pretraining with region priors for object detection,  arXiv preprint arXiv:2106.04550 (2021).
  * Gallardo et al. [2021] J. Gallardo, T. L. Hayes, C. Kanan,  Self-supervised training enhances online continual learning,  arXiv preprint arXiv:2103.14010 (2021).
  * Pham et al. [2021] Q. Pham, C. Liu, S. Hoi,  Dualnet: Continual learning, fast and slow,  Advances in Neural Information Processing Systems 34 (2021).
  * Viola and Jones [2001] P. Viola, M. Jones,  Rapid object detection using a boosted cascade of simple features,  in: Proceedings of the 2001 IEEE computer society conference on computer vision and pattern recognition. CVPR 2001, volume 1, Ieee, 2001, pp. I–I.
  * Lowe [2004] D. G. Lowe,  Distinctive image features from scale-invariant keypoints,  International journal of computer vision 60 (2004) 91–110.
  * Girshick et al. [2014] R. Girshick, J. Donahue, T. Darrell, J. Malik,  Rich feature hierarchies for accurate object detection and semantic segmentation,  in: Proceedings of the IEEE conference on computer vision and pattern recognition, 2014, pp. 580–587.
  * Girshick [2015] R. Girshick,  Fast r-cnn,  in: Proceedings of the IEEE international conference on computer vision, 2015, pp. 1440–1448.
  * Wu et al. [2020] X. Wu, D. Sahoo, S. C. Hoi,  Recent advances in deep learning for object detection,  Neurocomputing 396 (2020) 39–64.
  * Lin et al. [2017] T.-Y. Lin, P. Dollár, R. Girshick, K. He, B. Hariharan, S. Belongie,  Feature pyramid networks for object detection,  in: Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 2117–2125.
  * Deng et al. [2009] J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, L. Fei-Fei,  Imagenet: A large-scale hierarchical image database,  in: 2009 IEEE conference on computer vision and pattern recognition, Ieee, 2009, pp. 248–255.
  * Uijlings et al. [2013] J. R. Uijlings, K. E. Van De Sande, T. Gevers, A. W. Smeulders,  Selective search for object recognition,  International journal of computer vision 104 (2013) 154–171.
  * Redmon et al. [2016] J. Redmon, S. Divvala, R. Girshick, A. Farhadi,  You only look once: Unified, real-time object detection,  in: Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 779–788.
  * Liu et al. [2016] W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, A. C. Berg,  Ssd: Single shot multibox detector,  in: European conference on computer vision, Springer, 2016, pp. 21–37.
  * Lin et al. [2017] T.-Y. Lin, P. Goyal, R. Girshick, K. He, P. Dollár,  Focal loss for dense object detection,  in: Proceedings of the IEEE international conference on computer vision, 2017, pp. 2980–2988.
  * Ge et al. [2021] Z. Ge, S. Liu, F. Wang, Z. Li, J. Sun,  Yolox: Exceeding yolo series in 2021,  arXiv preprint arXiv:2107.08430 (2021).
  * Duan et al. [2019] K. Duan, S. Bai, L. Xie, H. Qi, Q. Huang, Q. Tian,  Centernet: Keypoint triplets for object detection,  in: Proceedings of the IEEE/CVF international conference on computer vision, 2019, pp. 6569–6578.
  * Tian et al. [2020] Z. Tian, C. Shen, H. Chen, T. He,  Fcos: A simple and strong anchor-free object detector,  IEEE Transactions on Pattern Analysis and Machine Intelligence (2020).
  * LeCun et al. [2015] Y. LeCun, Y. Bengio, G. Hinton,  Deep learning,  nature 521 (2015) 436.
  * Everingham et al. [2010] M. Everingham, L. Van Gool, C. K. Williams, J. Winn, A. Zisserman,  The pascal visual object classes (voc) challenge,  International journal of computer vision 88 (2010) 303–338.
  * Lin et al. [2014] T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, C. L. Zitnick,  Microsoft coco: Common objects in context,  in: European conference on computer vision, Springer, 2014, pp. 740–755.
  * Zou et al. [2019] Z. Zou, Z. Shi, Y. Guo, J. Ye,  Object detection in 20 years: A survey,  arXiv preprint arXiv:1905.05055 (2019).
  * Gupta et al. [2019] A. Gupta, P. Dollar, R. Girshick,  Lvis: A dataset for large vocabulary instance segmentation,  in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2019, pp. 5356–5364.
  * Padilla et al. [2020] R. Padilla, S. L. Netto, E. A. Da Silva,  A survey on performance metrics for object-detection algorithms,  in: 2020 international conference on systems, signals and image processing (IWSSIP), IEEE, 2020, pp. 237–242.
  * Peng et al. [2020] C. Peng, K. Zhao, B. C. Lovell,  Faster ilod: Incremental learning for object detectors based on faster rcnn,  Pattern Recognition Letters 140 (2020) 109–115.
  * Yang et al. [2021] D. Yang, Y. Zhou, W. Wang,  Multi-view correlation distillation for incremental object detection,  arXiv preprint arXiv:2107.01787 (2021).
  * Kundu et al. [2020] J. N. Kundu, R. M. Venkatesh, N. Venkat, A. Revanur, R. V. Babu,  Class-incremental domain adaptation,  in: European Conference on Computer Vision, Springer, 2020, pp. 53–69.
  * ssl [2021] Iccv sslad competition, Available at: https://sslad2021.github.io/, 2021.
  * Li et al. [2022] D. Li, G. Cao, Y. Xu, Z. Cheng, Y. Niu,  Technical report for iccv 2021 challenge sslad-track3b: Transformers are better continual learners,  arXiv preprint arXiv:2201.04924 (2022).
  * Acharya and Kanan [2021] M. Acharya, C. Kanan,  2nd place solution for soda10m challenge 2021–continual detection track,  arXiv preprint arXiv:2110.13064 (2021).
  * Zhai and Liu [2021] J. Zhai, X. Liu, Technical report for domain incremental object detection, Available at: https://sslad2021.github.io/, 2021\.
  * Wohlin [2014] C. Wohlin,  Guidelines for snowballing in systematic literature studies and a replication in software engineering,  in: Proceedings of the 18th international conference on evaluation and assessment in software engineering, 2014, pp. 1–10.
  * Li et al. [2018] W. Li, Q. Wu, L. Xu, C. Shang,  Incremental learning of single-stage detectors with mining memory neurons,  in: 2018 IEEE 4th International Conference on Computer and Communications (ICCC), IEEE, 2018, pp. 1981–1985.
  * Guan et al. [2018] L. Guan, Y. Wu, J. Zhao, C. Ye,  Learn to detect objects incrementally,  in: 2018 IEEE Intelligent Vehicles Symposium (IV), IEEE, 2018, pp. 403–408.
  * Hao et al. [2019] Y. Hao, Y. Fu, Y.-G. Jiang, Q. Tian,  An end-to-end architecture for class-incremental object detection with knowledge distillation,  in: 2019 IEEE International Conference on Multimedia and Expo (ICME), IEEE, 2019, pp. 1–6.
  * Chen et al. [2019] L. Chen, C. Yu, L. Chen,  A new knowledge distillation for incremental object detection,  in: 2019 International Joint Conference on Neural Networks (IJCNN), IEEE, 2019, pp. 1–7.
  * Li et al. [2019] D. Li, S. Tasci, S. Ghosh, J. Zhu, J. Zhang, L. Heck,  Rilod: Near real-time incremental learning for object detection at the edge,  in: Proceedings of the 4th ACM/IEEE Symposium on Edge Computing, 2019, pp. 113–126.
  * Hao et al. [2019] Y. Hao, Y. Fu, Y.-G. Jiang,  Take goods from shelves: A dataset for class-incremental object detection,  in: Proceedings of the 2019 on International Conference on Multimedia Retrieval, 2019, pp. 271–278.
  * Liu et al. [2020] L. Liu, Z. Kuang, Y. Chen, J.-H. Xue, W. Yang, W. Zhang,  Incdet: In defense of elastic weight consolidation for incremental object detection,  IEEE transactions on neural networks and learning systems (2020).
  * Acharya et al. [2020] M. Acharya, T. L. Hayes, C. Kanan,  Rodeo: Replay for online object detection,  arXiv preprint arXiv:2008.06439 (2020).
  * Zhou et al. [2020] W. Zhou, S. Chang, N. Sosa, H. Hamann, D. Cox,  Lifelong object detection,  arXiv preprint arXiv:2009.01129 (2020).
  * Zhang et al. [2020] J. Zhang, J. Zhang, S. Ghosh, D. Li, S. Tasci, L. Heck, H. Zhang, C.-C. J. Kuo,  Class-incremental learning via deep model consolidation,  in: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2020, pp. 1131–1140.
  * Liu et al. [2020] X. Liu, H. Yang, A. Ravichandran, R. Bhotika, S. Soatto,  Multi-task incremental learning for object detection,  arXiv preprint arXiv:2002.05347 (2020).
  * Yang et al. [2020] D. Yang, Y. Zhou, D. Wu, C. Ma, F. Yang, W. Wang,  Two-level residual distillation based triple network for incremental object detection,  arXiv preprint arXiv:2007.13428 (2020).
  * Ramakrishnan et al. [2020] K. Ramakrishnan, R. Panda, Q. Fan, J. Henning, A. Oliva, R. Feris,  Relationship matters: Relation guided knowledge transfer for incremental learning of object detectors,  in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2020, pp. 250–251.
  * Chen et al. [2020] J. Chen, S. Wang, L. Chen, H. Cai, Y. Qian,  Incremental detection of remote sensing objects with feature pyramid and knowledge distillation,  IEEE Transactions on Geoscience and Remote Sensing (2020).
  * Peng et al. [2021] C. Peng, K. Zhao, S. Maksoud, M. Li, B. C. Lovell,  Sid: Incremental learning for anchor-free object detection via selective and inter-related distillation,  Computer Vision and Image Understanding (2021) 103229.
  * Yang et al. [2021] S. Yang, P. Sun, Y. Jiang, X. Xia, R. Zhang, Z. Yuan, C. Wang, P. Luo, M. Xu,  Objects in semantic topology,  arXiv preprint arXiv:2110.02687 (2021).
  * Kj et al. [2021] J. Kj, J. Rajasegaran, S. Khan, F. S. Khan, V. N. Balasubramanian,  Incremental object detection via meta-learning,  IEEE Transactions on Pattern Analysis and Machine Intelligence (2021).
  * ul Haq et al. [2021] Q. M. ul Haq, S.-J. Ruan, M. A. Haq, S. Karam, J. L. Shieh, P. Chondro, D.-Q. Gao,  An incremental learning of yolov3 without catastrophic forgetting for smart city applications,  IEEE Consumer Electronics Magazine (2021).
  * Zhang et al. [2021] N. Zhang, Z. Sun, K. Zhang, L. Xiao,  Incremental learning of object detection with output merging of compact expert detectors,  in: 2021 4th International Conference on Intelligent Autonomous Systems (ICoIAS), IEEE, 2021, pp. 1–7.
  * Dong et al. [2021] N. Dong, Y. Zhang, M. Ding, G. H. Lee,  Bridging non co-occurrence with unlabeled in-the-wild data for incremental object detection,  Advances in Neural Information Processing Systems 34 (2021).
  * Wang et al. [2021] J. Wang, X. Wang, Y. Shang-Guan, A. Gupta,  Wanderlust: Online continual object detection in the real world,  in: Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021, pp. 10829–10838.
  * Li et al. [2020] K. Li, G. Wan, G. Cheng, L. Meng, J. Han,  Object detection in optical remote sensing images: A survey and a new benchmark,  ISPRS Journal of Photogrammetry and Remote Sensing 159 (2020) 296–307.
  * Xia et al. [2018] G.-S. Xia, X. Bai, J. Ding, Z. Zhu, S. Belongie, J. Luo, M. Datcu, M. Pelillo, L. Zhang,  Dota: A large-scale dataset for object detection in aerial images,  in: Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 3974–3983.
  * Liu et al. [2017] Z. Liu, J. Li, Z. Shen, G. Huang, S. Yan, C. Zhang,  Learning efficient convolutional networks through network slimming,  in: Proceedings of the IEEE international conference on computer vision, 2017, pp. 2736–2744.
  * Mirzadeh et al. [2020] S. I. Mirzadeh, M. Farajtabar, R. Pascanu, H. Ghasemzadeh,  Understanding the role of training regimes in continual learning,  arXiv preprint arXiv:2006.06958 (2020).
  * Qu et al. [2021] H. Qu, H. Rahmani, L. Xu, B. Williams, J. Liu,  Recent advances of continual learning in computer vision: An overview,  arXiv preprint arXiv:2109.11369 (2021).
  * Hassabis et al. [2017] D. Hassabis, D. Kumaran, C. Summerfield, M. Botvinick,  Neuroscience-inspired artificial intelligence,  Neuron 95 (2017) 245–258.
  * Lomonaco et al. [2021] V. Lomonaco, L. Pellegrini, A. Cossu, A. Carta, G. Graffieti, T. L. Hayes, M. De Lange, M. Masana, J. Pomponi, G. M. van de Ven, et al.,  Avalanche: an end-to-end library for continual learning,  in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 3600–3610.
  * Douillard and Lesort [2021] A. Douillard, T. Lesort,  Continuum: Simple management of complex continual learning scenarios,  arXiv preprint arXiv:2102.06253 (2021).
  * Bendale and Boult [2015] A. Bendale, T. Boult,  Towards open world recognition,  in: Proceedings of the IEEE conference on computer vision and pattern recognition, 2015, pp. 1893–1902.
  * Perez-Rua et al. [2020] J.-M. Perez-Rua, X. Zhu, T. M. Hospedales, T. Xiang,  Incremental few-shot object detection,  in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 13846–13855.
  * Li et al. [2021] P. Li, Y. Li, H. Cui, D. Wang,  Class-incremental few-shot object detection,  arXiv preprint arXiv:2105.07637 (2021).
  * Douillard et al. [2021] A. Douillard, Y. Chen, A. Dapogny, M. Cord,  Plop: Learning without forgetting for continual semantic segmentation,  in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 4040–4050.
  * Michieli and Zanuttigh [2019] U. Michieli, P. Zanuttigh,  Incremental learning techniques for semantic segmentation,  in: Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, 2019, pp. 0–0.
  * Cermelli et al. [2020] F. Cermelli, M. Mancini, S. R. Bulo, E. Ricci, B. Caputo,  Modeling the background for incremental learning in semantic segmentation,  in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 9233–9242.
  * Bansal et al. [2018] A. Bansal, K. Sikka, G. Sharma, R. Chellappa, A. Divakaran,  Zero-shot object detection,  in: Proceedings of the European Conference on Computer Vision (ECCV), 2018, pp. 384–400.