# Few-shot Object Detection in Remote Sensing: Lifting the Curse of
Incompletely Annotated Novel Objects

Fahong Zhang, Yilei Shi, Zhitong Xiong, and Xiao Xiang Zhu The work is jointly
supported by the German Research Foundation (DFG GZ: ZH 498/18-1; Project
number: 519016653), the Helmholtz Association through the Framework of the
Helmholtz Excellent Professorship “Data Science in Earth Observation - Big
Data Fusion for Urban Research”(grant number: W2-W3-100), by the German
Federal Ministry of Education and Research (BMBF) in the framework of the
international future AI lab ”AI4EO – Artificial Intelligence for Earth
Observation: Reasoning, Uncertainties, Ethics and Beyond” (grant number:
01DD20001) and by German Federal Ministry for Economic Affairs and Climate
Action in the framework of the ”national center of excellence ML4Earth” (grant
number: 50EE2201C).(Correspondence: Xiao Xiang Zhu) F. Zhang, Z. Xiong and X.
Zhu are with the Chair of Data Science in Earth Observation, Technical
University of Munich, 80333 Munich, Germany (e-mail: (fahong.zhang,
zhitong.xiong, xiaoxiang.zhu)@tum.de). Y. Shi is with the Chair of Remote
Sensing Technology, Technical University of Munich (TUM), 80333 Munich,
Germany (e-mail<EMAIL_ADDRESS>
###### Abstract

Object detection is an essential and fundamental task in computer vision and
satellite image processing. Existing deep learning methods have achieved
impressive performance thanks to the availability of large-scale annotated
datasets. Yet, in real-world applications the availability of labels is
limited. In this context, few-shot object detection (FSOD) has emerged as a
promising direction, which aims at enabling the model to detect novel objects
with only few of them annotated. However, many existing FSOD algorithms
overlook a critical issue: when an input image contains multiple novel objects
and only a subset of them are annotated, the unlabeled objects will be
considered as background during training. This can cause confusions and
severely impact the model’s ability to recall novel objects. To address this
issue, we propose a self-training-based FSOD (ST-FSOD) approach, which
incorporates the self-training mechanism into the few-shot fine-tuning
process. ST-FSOD aims to enable the discovery of novel objects that are not
annotated, and take them into account during training. On the one hand, we
devise a two-branch region proposal networks (RPN) to separate the proposal
extraction of base and novel objects, On another hand, we incorporate the
student-teacher mechanism into RPN and the region of interest (RoI) head to
include those highly confident yet unlabeled targets as pseudo labels.
Experimental results demonstrate that our proposed method outperforms the
state-of-the-art in various FSOD settings by a large margin. The codes will be
publicly available at https://github.com/zhu-xlab/ST-FSOD.

###### Index Terms:

Object Detection, Few-shot Learning, Self-training, Remote Sensing Image
Processing.

##  I Introduction

Figure 1:  Illustration of the incompletely annotated novel objects (IANO)
issue. In the standard FSOD protocal, only a few bounding boxes for the novel
class object provided for the few-shot fine-tuning stage [1]. Let’s consider a
scenario where our goal is detecting the presence of a ”plane” as the novel
class object, and we are provided with just one bounding box annotation for a
plane. As depicted in the figure, the challenge arises when multiple instances
of planes are present within a single image, In such cases, some of the planes
within the image are left unannotated. This can mislead the detector since RPN
loss and bounding box classification loss are calculated based on these
incomplete annotations.

Object detection (OD) is a critical task in computer vision as well as remote
sensing image processing, which enables the automatic identification and
localization of objects of interest within an image. With the rapid
development of deep learning techniques [2, 3, 4] and the emergence of large-
scale human-annotated data [5, 6], the performance of the state-of-the-art OD
approaches has been pushed to a new stage. These approaches have achieved
remarkable success in detecting objects in various domains, including remote
sensing [7, 8, 9]. However, traditional OD methods rely on a large amount of
labeled data for training, which can be challenging and time-consuming to
obtain in remote sensing image processing, particularly in scenarios where
novel or rare objects are involved.

This results in the need for few-shot learning [10, 11, 12], a paradigm that
aims to overcome the data scarcity issue by enabling models to generalize and
detect objects with only a limited number of labeled examples. Few-shot
learning achieves this by leveraging knowledge acquired from previously seen
categories to adapt and recognize novel objects efficiently.

Concurrently with the achievements in few-shot classification [13] and few-
shot semantic segmentation [14, 15], few-shot object detection (FSOD) [16, 17,
18] has emerged as a compelling research area in recent years. In the
conventional FSOD framework, the model undergoes a two-stage training process:
first, it is trained on a large-scale labeled dataset consisting of base
objects, and subsequently, it is fine-tuned on a fine-tuning set with only a
few labeled novel object instances.

However, when there are multiple novel objects in a single image, it is
possible that only a part of them are provided with labels during the fine-
tuning stage. As a result, these incomplete annotations can negatively impact
the training towards novel classes and hinder the discovery of novel objects.
This issue, illustrated in Fig. 1, can be referred as the incompletely
annotated novel objects (IANO) issue.

While the challenge of IANO has been investigated in the field of FSOD for
natural images [19, 20], it still remains unexplored in remote sensing.
However, objects in the remote sensing images are usually smaller, and scenes
often exhibit higher levels of congestion, particularly in contexts with
vehicles, planes, ships, and so on. This phenomenon can be readily observed
from different OD datasets tailored for remote sensing applications [21]. As a
result, the IANO problem takes on an even more formidable and pressing nature
when considered in the context of remote sensing.

To tackle this issue, it is necessary to establish a mechanism capable of
identifying and subsequently excluding potential unannotated novel objects
during the background sampling process. A promising solution that emerges for
mitigating this concern is self-training, a well-established technique in the
field of domain adaptation [22, 23, 24] and semi-supervised learning [25, 9].
This type of methods first generate pseudo labels on unlabeled data using a
pre-trained model and then fine-tune the model using these pseudo labels. The
underlying philosophy of _generating pseudo labels and leveraging the
unlabeled data_ aligns with our objective of identifying potential unannotated
novel objects, as they are hidden in the background and remain unannotated.
Therefore, we propose using self-training as a feasible approach to tackle
this issue in remote sensing imagery.

We build our self-training-based FSOD (ST-FSOD) method based on a popular
object detection framework: Faster RCNN [2], which consists of two stages. In
the first stage, a region proposal networks (RPN) is used to generate a set of
candidate object proposals, each with a corresponding objectness score and a
bounding box regression score. In the second stage, the candidate object
proposals are refined and classified using a fully-connected layer network,
also known as a Region of Interest (RoI) pooling layer [26].

We incorporate the self-training mechanism into the RPN and the bounding box
head (BBH) of the RoI layer, and devise a self-training RPN (ST-RPN) and a
self-training BBH (ST-BBH) modules accordingly. In these two modules, a
momentum-based teacher-student modeling strategy [27] is used to filter out
highly confident potential novel class objects and refine the loss
calculation. More specifically, the ST-RPN module uses the teacher-student
modeling strategy to filter out highly confident proposals for potential novel
classes. Meanwhile, the ST-BBH module filters out highly confident novel
bounding boxes. Both modules refine the loss calculation to improve the
accuracy of the detection model. Our contributions can be summarized as
follows:

  * •

Our study highlights and examines the challenge of the IANO issue in FSOD for
remote sensing imagery. To the best of our knowledge, this is the first work
that discusses and tackles the issue in the field of remote sensing, which is
neglected in many existing FSOD methods for remote sensing imagery and needs
to be addressed to advance the field.

  * •

To handle the IANO issue in object detection, we propose to apply self-
training technique. To this end, ST-RPN and ST-BBH modules are devised to
identify proposals or bounding boxes that are likely to include a novel
object, even in the absence of novel class annotations.

  * •

We conduct extensive experiments on three publicly available datasets, and
evaluate our proposed method under various FSOD settings. Experimental results
demonstrate that our approach outperforms the state-of-the-art methods by a
significant margin.

##  II Related Works

Figure 2:  The overall architecture of the proposed method. The FPN following
the backbone is not illustrated in the figures for the sake of simplicity.

###  II-A Object Detection

Object detection refers to identifying and localizing objects within an image,
which has been one of the main research tasks in computer vision (CV).
Traditional OD techniques are based on, e.g. feature extraction [28], object
recognition, and template matching [29]. State-of-the-art OD techniques are
mostly based on deep learning, owing to its overwhelming performance on large-
scale object detection benchmarks. Deep learning-based OD methods utilize
convolutional neural networks (CNNs) to perform object detection directly from
raw image pixels, without the need for hand-crafted feature engineering. They
can be categorized into two-stage and single-stage detectors. While two-stage
detectors aim to first generate object proposals and then classify them,
single-stage detectors perform both tasks simultaneously. A typical example of
two-stage detectors is Faster R-CNN [2], which generates a set of region
proposals and then extracts features from each proposal using a Region of
Interest (RoI) pooling layer before classifying the object within the
proposal. One well-known single-stage detector is YOLO [3], which applies a
CNN to the entire image to simultaneously predict bounding boxes and class
probabilities for each object without any proposal generation step.

###  II-B Few-shot Object Detection in Computer Vision

Few-shot object detection (FSOD) aims at recognizing novel or unseen object
classes based only on a few examples of them, by fine-tuning on a model
trained on many labeled examples of base classes. FSOD methods can be roughly
categorized into fine-tuning-based methods, meta-learning-based methods, and
metric-learning-based methods.

####  II-B1 Fine-tuning-based Methods

Fine-tuning-based methods are popular for few-shot object detection, which
first train on a large number of base class examples and then perform few-shot
fine-tuning on a smaller support set that includes both base and novel
classes.

One such method, LSTD [16], uses a flexible deep architecture that integrates
the advantages of both SSD and Faster R-CNN in a unified deep framework. It
also introduces transfer knowledge (TK) and background depression (BD)
regularizations to leverage object knowledge from source and target domains
during the fine-tuning stage.

Another fine-tuning-based method, TFA [17], indicates that even a simple fine-
tuning of only the last layer of a Faster R-CNN detector to novel classes can
achieve better performance compared to previous meta-learning based methods.
In addition, TFA replaces the fully-connected classification heads of Faster
R-CNN with cosine similarities. The aim is to reduce intra-class variances and
preserve performance in base classes through this feature normalization
technique.

In [30], the authors introduce a pretrain-transfer framework (PTF) that
utilizes a knowledge inheritance approach to initialize the weights for the
box classifier. Additionally, they develop an adaptive length re-scaling
strategy to ensure consistency of the dimensions of the pretrained weights for
both the base and novel classes. This helps to improve the efficiency and
effectiveness of the fine-tuning process.

####  II-B2 Metric-learning based Methods

Metric-learning based methods aim to reduce the dimensionality of each sample
and learn a feature representation such that similar samples are closer to
each other, while dissimilar ones are easier to discriminate.

RepMet [31] is a metric-learning approach suitable for both few-shot
classification and object detection. It uses a collection of Gaussian mixture
models, each with multiple modes, to describe the base and novel classes.
During base training, an embedding loss is employed to ensure a margin between
the distance of each query feature and its respective class representative, as
well as the distance to the nearest representative of an incorrect class.

In [32], the authors introduce an attention-based Region Proposal Network
(attention-RPN) that uses support features to enhance the proposal generation
process and eliminate non-matching or background proposals. Additionally, the
authors develop a multi-relation detector for feature representation learning
that measures the similarity between the RoI features of query and support
objects.

####  II-B3 Meta-learning based Methods

Meta-learning based methods are designed to learn quickly from a few examples,
using a meta-learner that has been trained on a diverse set of tasks. During
the base training stage, the meta-learner is trained on a meta-dataset
composed of various tasks. Once trained, the meta-learner can quickly adapt to
new tasks or generate a learner that is customized to the target task.

One example of meta-learning based methods is Meta-YOLO [33], which improves
the query feature of a model by using a set of weighting coefficients
generated during the meta-learning phase. These coefficients are based on the
support samples and allow the model to effectively learn the intrinsic
importance of features for detecting objects. This enables the re-weighting
coefficients of novel classes to be learned with only a few support examples.

Another approach is FsDetView [34], where a novel technique for aggregating
query and support features is introduced. Instead of feature re-weighting,
this technique involves performing element-wise multiplication, subtraction,
and concatenation between the two sets of features. This approach has shown
promising results in few-shot object detection tasks.

###  II-C Few-shot Object Detection in Remote Sensing

Remote sensing images have unique characteristics that distinguish them from
natural scene images, such as complex backgrounds, objects with multiple
orientations, and dense and small objects. Thus, designing a few-shot object
detection (FSOD) algorithm that accounts for these distinct features is
crucial.

In SAM&BFS [35], the authors propose a shared attention module that leverages
class-agnostic prior knowledge gained during the base training stage to aid in
detecting novel objects with significant size variations. In DH-FSDet [36],
the authors suggest using a balanced sampling strategy between base and novel
samples, taking all the base samples into consideration. Additionally, they
propose separating the classification and regression heads in the Region of
Interest (RoI) layer according to base and novel classes for better balance in
detection. Zhang et al. [37] introduce the generalized FSOD task to remote
sensing. In one hand, they propose a metric-based discriminative loss to
reduce the intra-class diversity and increase the inter-class separability of
the base and novel objects. In the other hand, they replace the RoI feature
extractor with a representation compensation module to prevent the model from
catastrophic forgetting.

More recently, researchers have explored integrating text data into the visual
learning pipeline to improve FSOD performance. Models such as TEMO [38] and
TSF-RGR [39] leverage text-modal knowledge extractors to provide prior
knowledge on the relationship between base and novel classes, resulting in
improved FSOD performance.

###  II-D Self-training

Self-training [40, 41, 42, 43] is a widely used approach for domain adaptation
[44] and semi-supervised learning [25]. This technique involves generating
pseudo-labels for the target domain or unlabeled data and then using them to
fine-tune the network. Self-training has shown promising results in various
tasks, including semi-supervised classification [45] and semantic segmentation
[46].

In semi-supervised classification, FixMatch [47] is a popular approach for
self-training. It generates pseudo-labels by using a model’s predictions on
weakly-augmented, unlabeled images. These pseudo-labels are then used to
supervise the model’s predictions on a strongly-augmented view of the same
image. This approach has been shown to achieve state-of-the-art performance on
several benchmark datasets.

For semantic segmentation tasks, SAC [27] is a self-training approach that
employs a momentum network as the teacher network. The teacher network
generates pseudo-labels on the weakly-augmented images, which are used to
supervise the student network that receives the strongly-augmented images. The
momentum network is updated based on the exponential moving average of the
student network. This approach has also shown promising results on several
benchmark datasets.

###  II-E Incompletely Annotated Novel Objects Issue

Li et al. [48] are the first to identify the Icompletely Annotated Novel
Objects (IANO) issue in their work, pointing out that the base set images
might contain unlabeled novel-class objects, leading to false positives. They
address this challenge by introducing a distractor utilization loss. More
specifically, for each annotated bounding box, a cropped image centered at the
object will be fed into a few-shot correction network. This network generate
corresponding pseudo labels, which are subsequently used to identify potential
novel objects and adjust the loss calculation with respect tothe Region of
Interest (RoI) head. On a similar note, Qiao et al. [19] emphasize that the
IANO issue also exists when multiple novel objects were present within a
single image. To resolve this concern, they propose a label calibration
method. This method recalibrated the predicted targets of background objects
based on their predicted confidences. As a result, unannotated novel objects
are assigned with lower weights during the loss calculation, mitigating their
negative impact.

Despite these valuable contributions, it’s worth noting that the
aforementioned approaches primarily addressed the impact of unannotated
objects to bounding box classification, neglecting their influence on the
training of region proposal networks (RPN) for a two-stage object detector.
Specifically, when calculating the RPN loss, the incompleteness of novel
object annotations could cause the RPN to mistakenly predict unannotated novel
objects as background, thereby reducing overall performance. In our method, we
propose a comprehensive approach to mitigate the IANO issue. We apply advanced
self-training techniques not only to the bounding box classification head but
also to the RPN. By doing so, we extend our efforts to tackle this challenge
on a broader scale, addressing the impact of unannotated objects throughout
the entire object detection pipeline.

##  III Methods

In this section, we introduce the proposed ST-FSOD (Self-Training-based Few-
Shot Object Detection) method, which consists of two major components: the
Self-Training Region Proposal Network (ST-RPN) and the Self-Training Bounding
Box Head (ST-BBH). The overall architecture is illustrated in Figure 2. The
ST-RPN module takes the multi-level features extracted from the backbone and
feature pyramid networks (FPN) head [49] as input and generates two sets of
object proposals, namely, the base and novel proposals, corresponding to the
base and novel categories, respectively. These proposals are merged and fed
into the ROI pooling and feature extraction layers to obtain the ROI features.
The ST-BBH module takes the ROI features as input and produces the final
detection results. Specifically, it detects potential unannotated novel class
objects and uses them as pseudo labels to recall more novel class objects,
thereby improving the model’s performance.

###  III-A Problem Formulation

In this section, we present the formulation of the standard FSOD setting. We
assume that we have access to a base set containing base class annotations
denoted as $\mathcal{D}_{base}=\\{(\mathbf{I}_{i},\mathcal{B}_{i}^{base})\\}$
and a novel set containing novel class annotations denoted as
$\mathcal{D}_{nov}=\\{(\mathbf{I}_{i},\mathcal{B}_{i}^{nov})\\}$, where
$\mathbf{I}_{i}$ represents an image and $\mathcal{B}_{i}=\\{(x,y,h,w,c)\\}$
represents a set of object bounding boxes within the image. Here, $x,y,w$ and
$h$ denote the locations of the bounding box, and $c$ denotes the class label.

Furthermore, let $\mathcal{C}_{base}$ and $\mathcal{C}_{nov}$ be the label set
of $\mathcal{B}_{base}$ and $\mathcal{B}_{nov}$, respectively, and they
satisfy the condition $\mathcal{C}_{base}\cap\mathcal{C}_{nov}=\varnothing$,
indicating that there are no common classes between the base and novel
classes.

Our proposed ST-FSOD method is established on the classic two-stage fine-
tuning approach (TFA) [17]. In the first stage of TFA, a base detector is
trained using the base set $\mathcal{D}_{base}$, following the same procedure
as in a regular object detector. In the second stage, a few-shot object
detector is initialized using the weights obtained in the first stage and
fine-tuned on a $K$-shot fine-tuning set denoted by
$\mathcal{D}_{ft}={(\mathbf{I}_{i},\mathcal{B}_{i}^{base},\mathcal{B}_{i}^{nov})}$,
where the number of novel object bounding boxes for each novel class is $K$.
More formally, for each novel class $c_{nov}\in\mathcal{C}_{nov}$, we have:

$\displaystyle\forall c_{nov}\in\mathcal{C}_{nov},$ (1)
$\displaystyle\sum_{\mathbf{I}_{i},\mathcal{B}_{i}^{base},\mathcal{B}_{i}^{nov}\in\mathcal{D}_{ft}}|\\{(x,y,h,w,c)\in\mathcal{B}_{i}^{nov}|\
c=c_{nov}\\}|=K.$

Here $|\cdot|$ denotes the number of elements of the set.

###  III-B Balanced Sampling Strategy

One of the key questions in constructing the few-shot fine-tuning set
$\mathcal{D}_{ft}$ is how many base class objects to sample. In the original
TFA [17], the authors proposed sampling exactly $K$ shots of base objects for
each base class to maintain a better balance between base and novel classes.
However, recent studies have shown that this strategy may not be the optimal
for remote sensing images [36, 35]. For example, Wolf et al. [36] found that
using more base objects and oversampling the few-shot novel objects can
improve overall performance.

Inspired by these findings, we propose a balanced sampling strategy (BSS) as
follows: First, we randomly sample the $K$-shot novel objects as usual. Next,
we include all base class images and annotations in $\mathcal{D}_{base}$ into
the fine-tuning set $\mathcal{D}_{ft}$. Finally, when sampling images for
fine-tuning, we increase the probability of sampling images $\mathbf{I}_{i}$
with a corresponding non-empty $\mathcal{B}_{i}^{nov}$ to ensure they are
sampled with the same probability as images that do not contain any novel
annotations. By adopting the BSS, we achieve a more balanced fine-tuning set
between base and novel classes, while also making full use of all the
available base annotations.

###  III-C Self-training-based Region Proposal Networks

In the Faster R-CNN network architecture, the Region Proposal Network (RPN) is
utilized to generate a set of proposals
$\mathcal{P}=\\{p=(t_{x},t_{y},t_{w},t_{h},o)\\}$ for each input image $I$
using multi-scale features extracted from $I$. The parameters $t_{x}$,
$t_{y}$, $t_{w}$, and $t_{h}$ represent the coordinates of each proposal, and
$o$ is an objectness score that indicates the probability of the proposal
containing an object. Fig. 2 illustrates the architecture of ST-RPN, which
consists of three sub-modules: base RPN, teacher RPN, and student RPN. All of
these sub-modules follow the original RPN architecture. ST-RPN generates two
sets of proposals, $\mathcal{P}^{base}$ and $\mathcal{P}^{nov}$, which are
obtained as follows:

  * •

The base RPN is responsible for extracting base proposals $\mathcal{P}^{base}$
from the input image $\mathbf{I}$. For fine-tuning the base RPN module, only
base annotations $\mathcal{B}^{base}$ are used to calculate the RPN loss.

$\mathcal{L}_{rpn}^{base}(\mathcal{P}^{base},\mathcal{B}^{base})=\sum_{p_{i}\in\mathcal{P}^{base}}L(p_{i},p_{i}^{*}),$
(2)

where $p_{i}^{*}$ is the regression and classification targets achieved by
matching $\mathcal{P}^{base}$ with $\mathcal{B}^{base}$ according to the
intersection over union (IoU) between each pair of the proposal and the ground
truth bounding box [2].

  * •

The teacher RPN generates a set of ignored proposals $\mathcal{P}^{ign}$,
which includes output proposals from this module that have an objectness score
$o$ greater than a given threshold $\tau_{rpn}$. Please refer to IV-B for the
selection of $\tau_{\text{rpn}}$.

  * •

The student RPN receives the extracted features from the backbone and the FPN
head [49] with dropout [50] applied and outputs a set of novel proposals
$\mathcal{P}^{nov}$. This module is trainable and is supervised only by the
few-shot novel annotations $\mathcal{B}^{nov}$. During the calculation of the
regression and classification target of each output proposal, those with a
high overlap with the ignored proposals in $\mathcal{B}^{ign}$ are excluded
from the loss calculation. The loss function for the student RPN can be
formulated as follows:

$\mathcal{L}_{rpn}^{nov}(\mathcal{P}^{nov},\mathcal{P}^{ign},\mathcal{B}^{nov})=\sum_{p_{i}\in\mathcal{P}^{nov}}w_{i}L(p_{i},p_{i}^{*}).$
(3)

Here $p_{i}^{*}$ is the box targets achieved by assigning $\mathcal{P}^{nov}$
to $\mathcal{B}^{nov}$. $w_{i}$ is a weighting coefficient that is used to
ignore the highly confident proposals that might contain novel objects.

$\displaystyle w_{i}=$ $\displaystyle\begin{cases}0,&\text{if }\exists\
p_{j}\in\mathcal{P}^{ign},\text{IoU}(p_{j},p_{i})>0.7,\\\
1,&\text{otherwise}.\end{cases}$ (4)

Here $\text{IoU}(\cdot,\cdot)$ denotes the IoU value of the two proposals.

The separation of base and novel proposals has two benefits. First, during
fine-tuning, the pre-trained weights obtained from the base training stage
could be negatively affected, which can impact the quality of the extracted
base proposals. Separating proposal extraction prevents the fine-tuning
towards novel objects from biasing the extraction of base proposals. Second,
previous FSOD works in remote sensing [35] have found that the RPN module
achieved from the base training stage often fails to recall novel objects
successfully. Thus, training an additional RPN module from scratch can lead to
a better network state in terms of novel object detection.

During the extraction of novel proposals $\mathcal{P}^{nov}$, using a student-
teacher-based self-training mechanism and introducing ignored proposals into
the loss calculation can prevent potential unannotated novel objects (UNO)
from being misclassified as background. This helps the RPN module to recall
more novel objects, and improves detection performance.

###  III-D Self-training-based Bounding Box Head

In the proposed framework, the extracted base and novel proposals are merged
and passed through a RoI pooling layer and a RoI feature extraction layer to
obtain the corresponding RoI features. These features are then forwarded to
the self-training bounding box head (ST-BBH) for achieving the final detection
results. The ST-BBH consists of a teacher and a student bounding box heads
(BBH). Each BBH contains a bounding box classifier and a regressor, following
the same architecture as the one proposed in Faster R-CNN [26]. While
processing each RoI, the feature is input to both the teacher and student
classifier heads. In order to improve the robustness, the student head
receives the feature with dropout [50] applied as the input. Let
$\mathbf{u}^{stu}$ and $\mathbf{u}^{tch}$ be the output probability of the
student and teacher BBH, and $v$ be the corresponding ground truth label. The
student classifier’s classification loss is calculated using the following
equation:

$\displaystyle\mathcal{L}_{bbh}(\mathbf{u}^{\text{stu}},\mathbf{u}^{\text{tch}},v)=$
(5) $\displaystyle\quad\
\begin{cases}L_{\text{cls}}(\mathbf{u}^{\text{stu}},\hat{u}^{\text{tch}}),&\text{if
}v=0\text{ and }\max(\mathbf{u}^{\text{tch}})>\tau_{\text{bbox}},\\\
L_{\text{cls}}(\mathbf{u}^{\text{stu}},v),&\text{otherwise}.\end{cases}$

Here, $\hat{u}^{\text{tch}}$ represents the class index with the highest value
in $\mathbf{u}^{\text{tch}}$. The assigned ground truth class label of the RoI
is denoted by $v$, with $v=0$ indicating that the RoI is considered as
background. The threshold $\tau_{\text{bbox}}$ is used to determine when to
use the prediction from the teacher bounding box head as the pseudo label.
Please refer to IV-B for the selection of $\tau_{\text{bbox}}$.

Overall, ST-RPN and ST-BBH share the same self-training philosophy with a
momentum network, represented in our context as the teacher module. This
teacher module maintains a slowly updated copy of the original module,
ensuring stable yet recent targets (or pseudo-labels) for model updates, as
discussed in [27]. However, it’s important to note that ST-RPN and ST-BBH are
technically distinct: ST-RPN is employed to extract class-agnostic proposals,
while ST-BBH is specifically designed for the classification and regression of
class-specific bounding boxes. Furthermore, we emphasize that in ST-RPN, we
explicitly separate the extraction of base and novel proposals, whereas in ST-
BBH, the classification and regression heads for both base and novel classes
share the same ROI features.

###  III-E Weights Initialization & Update

As depicted in Fig. 2, different trainable and fixed modules have been
highlighted in different colors. Among these modules, the backbone, FPN head,
base RPN, and RoI feature extraction layer will be initialized by the pre-
trained weights obtained from the base training stage.  For the classifier and
regressor of both student and teacher BBH, the entries of their weight
matrices correspond to the base classes will be initialized based on the pre-
trained weights, while entries for the novel classes will be randomly
initialized. The student and teacher RPN module of the ST-RPN will be randomly
initialized.

It is worth noting that the weights for the teacher RPN and teacher BBH are
not trainable. Instead, they will be updated by the corresponding student
module’s weights using exponential moving average [27].

$\theta_{\mathcal{T}}^{(t)}=\alpha\theta_{\mathcal{T}}^{(t-1)}+(1-\alpha)\theta_{\mathcal{S}}^{(t)},$
(6)

Here $\theta_{\mathcal{T}}^{(t)}$ and $\theta_{\mathcal{S}}^{(t)}$ denote the
weights of the teacher networks and their corresponding student networks at
time step $t$ during the training stage, respectively. $\alpha$ is a decay
weight that is set to $0.999$ following [27].

##  IV Experiments

In this section, we will present the experimental results of our proposed
method on various benchmarks for FSOD in remote sensing.

###  IV-A Experimental Settings

We evaluate the proposed method on three large-scale public object detection
datasets in remote sensing, including NWPU-VHR10 v2 [51], DIOR [52] and iSAID
[21].  NWPU-VHR10 v2 dataset comprises a total of $1,172$ images, each with
dimension $400\times 400$, and are divided into $10$ categories. Following the
previous works [53], $3$ categories “airplane”, “baseball dimond”, and “tennis
court” are adopted as novel classes, while the others are as the base classes.
In line with previous researches, we employ the training and validation sets
to fine-tune the model, and report the performance on the test set, which
contains $293$ images.

The DIOR dataset contains $23,463$ images and over $190,000$ instances, with
an image size of $800\times 800$. All objects are categorized into 20 classes.
In previous literature, two commonly used settings are adopted. The first
setting, proposed by Li et al. [20], uses $5$ categories (i.e., “plane”,
“baseball field”, “tennis court”, “train station”, and “wind mill”) as the
novel categories and the remaining as the base categories. In this setting,
the training set is used for base training and few-shot fine-tuning, while the
validation set is used for evaluation. The second setting, proposed in [54],
includes a total of $4$ base-novel class splits, each containing $5$ novel
categories and $15$ base categories. In this setting, both the training and
validation sets are used for base training and fine-tuning, and the test set
is used for evaluation.

iSAID is a large-scale instance segmentation dataset for remote sensing. It is
built on the same image set as DOTA [55], but provide the instance-level mask
annotations, and also finer bounding box annotations. iSAID contains 2806
images, whose sizes range from $800\times 800$ to $20,000\times 20,000$. In
total, there are $655,451$ annotated objects, which are classified into $15$
categories. We follow the official data pre-processing pipeline to crop the
images into $800\times 800$ patches, with a overlap of $25\%$. We follow the
FSOD setting of [36], which uses $3$ different base-novel class splits, and
sets the number of shots for each split to $10$, $50$, and $100$.

To make a fair comparison with the previous works, we adopt the mean average
precision (mAP) with a IoU threshold at $0.5$ as the evaluation metric
following the common practices.

###  IV-B Implementation Details

The proposed method uses the Faster R-CNN architecture [2] with a ResNet101
[56] backbone that is pre-trained on the ImageNet dataset. A Feature Pyramid
Networks (FPN) [49] is used to generate multi-scale features. The Adamw
optimizer [57] with a weight decay of 0.01 and a learning rate of $1e-4$ is
used to train the model on all settings. The base training stage of NWPU-VHR10
v2, DIOR and iSAID datasets lasts for $10,000$, $40,000$ and $80,000$
iterations, respectively. Learning rate decay with a factor of 0.1 is applied
at $5,000$ and $8,000$ for NWPU-VHR10 v2 dataset, $24,000$ and $32,000$
iterations for DIOR dataset, and $40,000$ and $60,000$ for iSAID dataset. The
few-shot fine-tuning lasts for $2,000$ iterations for NWPU-VHR10 v2 dataset
and $10,000$ iterations for the other settings.

For data pre-processing and augmentation, image patches are randomly cropped
to sizes of 400 $\times$ 400 for NWPU-VHR10 v2 dataset and 608 $\times$ 608
for the others. Multi-scale training with a range from 0.5 to 2.0, random
flipping, and random rotation with degrees of 90, 180, and 270 are applied.
The batch size is set to 16 for base training and 8 for fine-tuning.

The momentum parameter is set to $\alpha=0.999$ when updating the networks’
weights by exponential moving average [27]. The thresholds $\tau_{\text{rpn}}$
and $\tau_{\text{bbh}}$ used in ST-ROI and ST-BBH are set to 0.8. In Sec.
IV-F, sensitivity analyses to these hyperparameters are provided. Our codes
are based on PyTorch, EarthNets [58], and MMDetection [59] platform. For more
information, please refer to our published codes.

TABLE I:  Average Precision (AP) (in $\%$) at a IoU threshold of $0.5$ of different methods on NWPU-VHR v2 dataset, where the base-novel class split follows [53]. Averaged results and the standard deviations of $3$ different runs are reported for the proposed methods. Method / Shots | Novel Classes | Base Classes
---|---|---
|  3 |  5 |  10 |  20 |  3 |  5 |  10 |  20
OFA [53] |  43.2 |  60.4 |  66.7 |  - |  - |  - |  - |  -
FSODM [20] |  32 |  53 |  65 |  - |  - |  - |  - |  -
SAM&BFS [35] |  47.0 |  61.6 |  74.9 |  - |  - |  - |  - |  -
PAMS-Det [60] |  37 |  55 |  66 |  - |  - |  - |  - |  -
CIR-FSD [61] |  54 |  64 |  70 |  - |  - |  - |  - |  -
TFACSC [62] |  47 |  67 |  72 |  - |  - |  - |  - |  -
SAGS&TFS [63] |  51 |  66 |  72 |  - |  - |  - |  - |  -
TSF-RGR [39] |  57 |  66 |  77 |  - |  - |  - |  - |  -
G-FSOD [37] |  50.1 |  58.8 |  67.0 |  75.9 |  90.0 |  90.5 |  89.2 |  90.6
Ours |  60.7 $\pm$ 2.1 |  67.2 $\pm$ 1.2 |  77.2 $\pm$ 2.5 |  83.3 $\pm$ 1.5 |  89.7 $\pm$ 0.4 |  89.1 $\pm$ 0.5 |  89.3 $\pm$ 1.1 |  89.9 $\pm$ 0.5
TABLE II:  Average Precision (AP) (in $\%$) at a IoU threshold of $0.5$ of different methods on DIOR dataset, where the base-novel class split follows [20]. Averaged results and the standard deviations of $3$ different runs are reported for the proposed methods.  Method / Shots | Novel Classes | Base Classes
---|---|---
|  3 |  5 |  10 |  20 |  3 |  5 |  10 |  20
OFA [53] |  32.8 |  37.9 |  40.7 |  - |  - |  - |  - |  -
FSODM [20] |  - |  25 |  32 |  36 |  - |  - |  - |  -
SAM&BFS [35] |  - |  38.3 |  47.3 |  50.9 |  - |  - |  - |  -
PAMS-Det [60] |  28 |  33 |  38 |  - |  - |  - |  - |  -
CIR-FSD [61] |  - |  33 |  38 |  43 |  - |  - |  - |  -
TFACSC [62] |  38 |  42 |  47 |  - |  - |  - |  - |  -
SAGS&TFS [63] |  - |  34 |  37 |  42 |  - |  - |  - |  -
TSF-RGR [39] |  - |  42 |  49 |  54 |  - |  - |  - |  -
Ours |  43.5 $\pm$ 5.5 |  48.3 $\pm$ 1.3 |  55.8 $\pm$ 1.5 |  61.3 $\pm$ 2.0 |  75.0 $\pm$ 0.3 |  75.6 $\pm$ 0.5 |  74.4 $\pm$ 0.3 |  73.2 $\pm$ 2.1
TABLE III:  Average Precision (AP) (in $\%$) at a IoU threshold of $0.5$ of different methods on DIOR dataset, where the 4 base-novel class splits follow [54]. Averaged results and the standard deviations of $3$ different runs are reported for the proposed methods.  Shots |  Methods | Novel Classes | Base Classes
---|---|---|---
|  |  split1 |  split2 |  split3 |  split4 |  split1 |  split2 |  split3 |  split4
3 |  P-CNN [54] |  18.0 |  14.5 |  16.5 |  15.2 |  47.0 |  48.9 |  49.5 |  49.8
|  SAGS&TFS [63] |  29.3 |  12.6 |  20.9 |  17.5 |  - |  - |  - |  -
|  G-FSOD [37] |  27.6 |  14.1 |  16.0 |  16.7 |  68.9 |  69.2 |  71.1 |  69.0
|  Ours |  41.9 $\pm$ 0.6 |  17.7 $\pm$ 2.0 |  20.9 $\pm$ 0.4 |  20.4 $\pm$ 3.6 |  73.5 $\pm$ 0.5 |  72.5 $\pm$ 0.5 |  75.2 $\pm$ 0.4 |  73.3 $\pm$ 0.6
5 |  P-CNN [54] |  22.8 |  14.9 |  18.8 |  17.5 |  48.4 |  49.1 |  49.9 |  49.9
|  SAGS&TFS [63] |  31.6 |  15.5 |  24.8 |  19.7 |  - |  - |  - |  -
|  G-FSOD [37] |  30.5 |  15.8 |  23.3 |  21.0 |  69.5 |  69.3 |  70.2 |  68.0
|  Ours |  45.7 $\pm$ 1.6 |  20.7 $\pm$ 2.8 |  26.0 $\pm$ 2.5 |  25.2 $\pm$ 4.5 |  73.3 $\pm$ 0.4 |  72.7 $\pm$ 0.4 |  75.6 $\pm$ 0.6 |  73.5 $\pm$ 0.4
10 |  P-CNN [54] |  27.6 |  18.9 |  23.3 |  18.9 |  50.9 |  52.5 |  52.1 |  51.7
|  SAGS&TFS [63] |  31.6 |  15.5 |  24.8 |  19.7 |  - |  - |  - |  -
|  G-FSOD [37] |  37.5 |  20.7 |  26.2 |  25.8 |  69.0 |  68.7 |  71.1 |  68.6
|  Ours |  50.0 $\pm$ 1.5 |  27.3 $\pm$ 1.1 |  31.3 $\pm$ 0.3 |  33.4 $\pm$ 1.1 |  72.6 $\pm$ 0.3 |  72.3 $\pm$ 0.5 |  75.7 $\pm$ 0.4 |  73.9 $\pm$ 0.2
20 |  P-CNN [54] |  29.6 |  22.8 |  28.8 |  25.7 |  52.2 |  51.6 |  53.1 |  52.3
|  SAGS&TFS [63] |  40.2 |  23.8 |  36.1 |  27.7 |  - |  - |  - |  -
|  G-FSOD [37] |  39.8 |  22.7 |  32.1 |  31.8 |  69.8 |  68.2 |  71.3 |  67.7
|  Ours |  53.7 $\pm$ 1.1 |  33.4 $\pm$ 0.4 |  34.6 $\pm$ 1.9 |  38.2 $\pm$ 2.0 |  73.3 $\pm$ 0.5 |  73.3 $\pm$ 0.5 |  75.5 $\pm$ 0.2 |  73.8 $\pm$ 0.2
TABLE IV:  Average Precision (AP) (in $\%$) at a IoU threshold of $0.5$ of different methods on iSAID dataset, where the 3 base-novel class splits follows [36]. Averaged results and the standard deviations of $3$ different runs are reported for the proposed methods. Results of FSDetView and TFA are cited from [36].  Shots |  Methods | Novel Classes | Base Classes
---|---|---|---
|  |  split1 |  split2 |  split3 |  split1 |  split2 |  split3
10 |  FSDetView [34] |  1.3 $\pm$ 0.3 |  8.7 $\pm$ 2.1 |  4.6 $\pm$ 1.2 |  33.8 $\pm$ 0.5 |  29.8 $\pm$ 1.6 |  32.9 $\pm$ 3.4
|  TFA [17] |  3.3 $\pm$ 0.8 |  9.0 $\pm$ 2.6 |  3.8 $\pm$ 1.1 |  58.6 $\pm$ 0.3 |  56.5 $\pm$ 0.8 |  59.0 $\pm$ 1.5
|  DH-FSDet [36] |  5.2 $\pm$ 0.8 |  14.5 $\pm$ 1.7 |  9.7 $\pm$ 2.2 |  65.0 $\pm$ 0.2 |  64.5 $\pm$ 0.1 |  67.8 $\pm$ 0.1
|  Ours |  10.2 $\pm$ 3.3 |  17.7 $\pm$ 3.8 |  14.0 $\pm$ 2.1 |  63.7 $\pm$ 0.4 |  62.4 $\pm$ 0.4 |  66.1 $\pm$ 0.7
50 |  FSDetView [34] |  7.2 $\pm$ 2.3 |  26.8 $\pm$ 2.8 |  17.1 $\pm$ 1.1 |  35.3 $\pm$ 0.5 |  30.0 $\pm$ 1.1 |  34.6 $\pm$ 1.1
|  TFA [17] |  4.7 $\pm$ 0.0 |  12.1 $\pm$ 1.9 |  5.6 $\pm$ 1.4 |  60.7 $\pm$ 0.5 |  58.5 $\pm$ 0.8 |  60.9 $\pm$ 0.3
|  DH-FSDet [36] |  12.8 $\pm$ 0.8 |  28.9 $\pm$ 3.4 |  19.6 $\pm$ 2.4 |  65.1 $\pm$ 0.1 |  64.7 $\pm$ 0.1 |  68.0 $\pm$ 0.1
|  Ours |  24.8 $\pm$ 2.1 |  39.3 $\pm$ 2.1 |  31.1 $\pm$ 1.2 |  62.8 $\pm$ 0.4 |  62.6 $\pm$ 0.3 |  65.8 $\pm$ 0.1
100 |  FSDetView [34] |  10.2 $\pm$ 1.2 |  32.8 $\pm$ 2.0 |  24.1 $\pm$ 1.1 |  36.4 $\pm$ 0.6 |  30.4 $\pm$ 0.4 |  34.5 $\pm$ 1.3
|  TFA [17] |  5.0 $\pm$ 0.3 |  14.4 $\pm$ 1.5 |  5.4 $\pm$ 1.1 |  61.4 $\pm$ 0.3 |  59.2 $\pm$ 0.2 |  61.6 $\pm$ 0.4
|  DH-FSDet [36] |  16.7 $\pm$ 1.7 |  36.0 $\pm$ 1.7 |  23.1 $\pm$ 0.9 |  65.2 $\pm$ 0.1 |  64.8 $\pm$ 0.1 |  68.1 $\pm$ 0.1
|  Ours |  34.3 $\pm$ 1.9 |  45.0 $\pm$ 1.0 |  33.0 $\pm$ 1.3 |  63.3 $\pm$ 0.3 |  62.9 $\pm$ 0.2 |  65.6 $\pm$ 0.1
Figure 3:  Visualized FSOD results of the proposed methods under $K=20$ shots
setting on NWPU-VHR10 v2 dataset. The base-novel class split follows [53].
Figure 4:  Visualized FSOD results of the proposed methods under $K=20$ shots
setting on DIOR dataset. The base-novel class split follows the split 2 in
[54].  Figure 5:  Visualized FSOD results of the proposed methods under
$K=100$ shots setting on iSAID dataset. The base-novel class split follows the
split 2 in [36]. Figure 6:  Visualized FSOD results of the proposed methods
under different ablations on iSAID dataset. The number of novel shots is set
to $K=100$. The base-novel class split is the split 1 in [36].

###  IV-C Quantitative Results

The quantitative results for the NWPU-VHR10 v2, DIOR, and iSAID datasets are
presented in Table I, Table II and III, and Table IV, respectively. Overall,
our proposed method achieves superior or comparable performance across all
datasets and various settings related to novel objects. Specifically:

On the NWPU-VHR10 v2 dataset, our method attains state-of-the-art performance
in both 3 and 20-shot settings, surpassing the second-best results by a
notable margin ranging from 3% to 7%. Additionally, it performs comparably to
state-of-the-art models in the 5 and 10-shot settings.

Regarding the DIOR dataset, as shown in Table II, our method consistently
outperforms the second-best approach by a substantial margin of 5% to 7%. In
Table III, our method excels in all settings except for the 20-shot setting of
split 3. Remarkably, our results in split 1 demonstrate improvements exceeding
10%.

On the iSAID dataset, our proposed method delivers significant enhancements,
ranging from 5% to over 10%. These improvements remain consistent across
various splits and different numbers of shots.

These results not only demonstrate the effectiveness of our proposed method
but also underscore the significance of addressing the issue of incompletely
annotated novel objects. It is worth noting that performance variance tends to
be higher in cases with fewer shots, suggesting that FSOD performance is
somewhat sensitive to the sampling of annotated novel objects, especially when
the available shots are limited.

###  IV-D Qualitative Results

We present the visualization results on NWPU-VHR10 v2, DIOR, and iSAID
datasets in Fig. 3, 4, and 5. Some observations and conclusions can be made
from the results:

  * •

The proposed method plays a crucial role in alleviating the challenge posed by
incompletely annotated novel objects (IANO). This issue becomes particularly
pronounced in scenarios featuring multiple small objects within a single
image. For instance, in the first column of Fig. 4, ships can be easily
mistaken as part of the background by a few-shot object detector. This occurs
because, during the few-shot fine-tuning stage, a significant number of ship
annotations will be missing, as depicted in Fig. 1. In contrast to this, our
detection results show demonstrate that our method excels in recalling many
ship objects. This verify the effectiveness of employing self-training
technique to solve the IANO issue.

  * •

By separating the proposals for base and novel class objects, our approach
effectively preserves the performance of the few-shot detector on the base
class. As elaborated in Section III-C, this separation of region proposals
serves to prevent the few-shot fine-tuning process from negatively influence
the detection of base class objects. As evidenced by the results depicted in
the 5th column of Fig. 4 and the 3rd column of Fig. 5, our method is able to
detect challenging, small base class objects such as storage tanks and
vehicles.

TABLE V:  Ablation study results on split1 of the iSAID benchmark. The same seed for image sampling and other randomized process is used to make a fair comparison. “w/ FT” denotes whether the few-shot fine-tuning approach proposed in [17] is applied. “BSS” denotes the balanced sampling strategy as described in Sec. III-B.  w/ FT |  BSS |  ST-RPN |  ST-BBH | Novel Classes | Base Classes
---|---|---|---|---|---
|  |  |  |  10 |  50 |  100 |  10 |  50 |  100
|  |  |  |  - |  - |  - |  65.8 |  65.8 |  65.8
✓ |  |  |  |  3.7 |  13.1 |  17.0 |  39.6 |  54.2 |  57.8
✓ |  ✓ |  |  |  4.0 |  12.3 |  16.2 |  65.9 |  65.9 |  66.0
✓ |  ✓ |  ✓ |  |  5.2 |  16.3 |  22.3 |  65.5 |  66.0 |  66.4
✓ |  ✓ |  |  ✓ |  6.5 |  17.8 |  27.0 |  65.1 |  64.9 |  65.0
✓ |  ✓ |  ✓ |  ✓ |  10.3 |  23.4 |  35.5 |  63.0 |  62.3 |  63.0
TABLE VI:  Sensitivity analyses of the hyper-parameters on split1 of the iSAID benchmark.  $\tau_{\text{rpn}}$ |  $\tau_{\text{bbh}}$ |  $\alpha$ | Novel Classes | Base Classes
---|---|---|---|---
|  |  |  10 |  50 |  100 |  10 |  50 |  100
0.8 |  0.8 |  0.999 |  10.3 |  23.4 |  35.5 |  63.0 |  62.3 |  63.0
0.8 |  0.6 |  0.999 |  10.1 |  25.1 |  34.4 |  63.8 |  63.2 |  62.9
0.8 |  0.9 |  0.999 |  10.0 |  22.8 |  36.5 |  64.0 |  62.4 |  62.9
0.6 |  0.8 |  0.999 |  10.5 |  22.4 |  35.0 |  63.4 |  62.4 |  62.5
0.9 |  0.8 |  0.999 |  10.5 |  23.7 |  36.4 |  63.9 |  61.9 |  63.3
0.8 |  0.8 |  0.99 |  10.4 |  23.9 |  35.3 |  63.5 |  62.7 |  63.3
TABLE VII:  Training times (seconds per iteration) and inference frames per second (FPS) of the proposed method evaluated on iSAID dataset. mAP (%) is also reported for comparison. ST-RPN |  ST-BBH |  mAP |  Training Times |  Inference FPS
---|---|---|---|---
|  |  16.2 |  0.258 |  18.8
✓ |  |  22.3 |  0.458 |  16.0
|  ✓ |  27.0 |  0.260 |  18.7
✓ |  ✓ |  35.5 |  0.479 |  15.9

###  IV-E Ablation Studies

We conduct ablation studies on the first split of iSAID dataset to better
understand the effect of all the components used in the proposed method. The
results are presented in Table V and Fig. 6 for quantitative and qualitative
analysis, respectively. The following observations can be made from the
results:

  * •

The naive fine-tuning strategy introduced in [17] has a detrimental effect on
the detection of base class objects. A noticeable performance decline becomes
evident when comparing the results in the first row to those in the second
row, according to Table V. This decline in performance stems from the fact
that this particular strategy employs only a limited subset of base class
annotations for model fine-tuning, leading to incomplete annotations for the
base class.

  * •

BSS proves to be highly effective in preserving the base class performance
after the few-shot fine-tuning process, at the cost of a slight trade-off in
novel class performance. This highlights the critical importance of fine-
tuning with a complete base annotation set for the maintenance of base class
performance in satellite image-based FSOD.

  * •

Applying the proposed ST-RPN module does not influence the base class
performance, owing to the separation of the extraction of base and novel
proposals. In addition, applying the proposed ST-RPN module is beneficial to
improving the performance on novel class.

  * •

After applying the proposed ST-BBH module, there is a slight performance decay
on the base classes. One possible reason is that since the base and novel
classes share the same bounding box head (BBH), the fine-tuning on novel
objects affects the general feature extraction within BBH and further affects
the detection of base classes. However, there is a consistent improvement on
the novel classes after applying ST-BBH, ranging from 3% to even 10%, at
different number of shots.

  * •

By combining all the modules, the highest novel class accuracy is achieved,
with a margin of 6% to nearly 20% compared to the naive fine-tuning strategy.
This verifies the effectiveness of the proposed ST-RPN and ST-BBH modules in
solving the IANO issue.

Fig. 6 shows visualized results for different ablated models. The second row
shows that using the ST-RPN module helps to recall more large vehicle objects.
However, using ST-RPN alone without ST-BBH may lead to a higher false positive
rate, as can be seen from the first row. By combining the ST-RPN and ST-BBH,
the best visualization quality is achieved. The third row demonstrates that
this combination can help to detect small ship objects with high accuracy.
These results further demonstrate the significance of incorporating the self-
training mechanism to solve the unlabeled novel object issue.

Figure 7:  Visualized failure cases of the proposed method on NWPU-VHR10 v2,
DIOR and iSAID datasets. Base-novel class splits are the same as the splits in
Fig. 3, 4, and 5.

###  IV-F Sensitivity Analyses of Hyperparameters

We conducted a sensitivity analysis to evaluate the impact of hyper-parameter
selection on the proposed method. The hyper-parameters we tested include the
two self-training thresholds $\tau_{\text{rpn}}$ and $\tau_{\text{bbh}}$ used
in ST-RPN and ST-BBH, and the momentum $\alpha$ used when updating the teacher
networks via EMA [27]. The results are presented in Tab. VI. We observed
slight performance fluctuations (generally less than $2\%$) with different
hyper-parameter values. However, compared to the variances caused by different
sampling seeds as shown in Tab. II, III and IV, such fluctuations are not
significant. Therefore, we can conclude that the proposed method is not highly
sensitive to the values of the aforementioned hyper-parameters.

###  IV-G Computational Efficiency

To assess the incremental computational demands introduced by our two proposed
modules, ST-RPN and ST-BBH, we conducted an evaluation based on training times
(measured in seconds per iteration) during the fine-tuning stage and inference
frames per second (FPS) when these modules were integrated. The outcomes, as
illustrated in Table VII, indicate that the inclusion of ST-BBH results in a
negligible computational overhead, both during training and inference phases.
This outcome aligns with our expectations, as ST-BBH simply introduces a pair
of additional bounding box regressor and classifier layers on top of the
Region of Interest (RoI) features.

In contrast, the integration of ST-RPN introduces a more substantial
computational load during the training phase. However, it is crucial to note
that the fine-tuning phase in FSOD algorithms typically involves significantly
fewer iterations compared to the base training phase (see our settings in
Section IV-B). As such, the additional training costs remain within acceptable
bounds. Furthermore, while ST-RPN does marginally reduce inference speed, it
is essential to consider this cost in light of the performance gains it
offers. This expense is manageable given the corresponding boost in detection
performance. Additionally, upon closer investigation, we found that a
substantial portion of the computational cost introduced by ST-RPN is
attributed to the additional bounding box operations, such as non-maximum
suppression (NMS). These options can be optimized and accelerated through the
utilization of CUDA implementations.

###  IV-H Failure Cases

To gain a better understanding of the limitations of the proposed FSOD method,
we visualize some failure cases in different FSOD settings, as shown in Fig.
7. Based on the figure, we can observe that the majority of the missed
detections are primarily due to the small size of the objects (“ships” in the
3rd column), or objects with large size variance compared to the training data
(“ships” in the second column). Additionally, there may be duplicated detected
boxes for objects that lack clear boundaries, such as the “tennis court” in
the 1st column or “soccerball field” in the 4th column. While these issues are
prevalent in general object detection [64], improved techniques related to
addressing them can also be utilized to enhance the performance of FSOD.

##  V Conclusion

In this paper, we analyzed the current FSOD setting for remote sensing and
identified the issue of incompletely annotated novel objects that can
negatively impact the performance of FSOD methods. To address this issue, we
propose to incorporate the self-training mechanism into the classical two-
stage fine-tuning based FSOD pipeline. Our approach includes a ST-RPN module,
which generates a set of novel proposals by excluding some proposals from the
loss calculation that are likely to be novel objects but cannot be assigned to
an existing few-shot annotation. Additionally, we designed a ST-BBH module
that leverages the pseudo labels generated from a teacher BBH to filter out
potential novel bounding boxes that are unlabeled and use them to supervise
the student BBH to recall more novel objects.

While our proposed method significantly improved the novel class FSOD
performance in remote sensing, the base class performance may slightly
decrease compared to the base model. Future works could focus on designing a
generalized FSOD method that prevents the model from forgetting the previously
learned base knowledge while improving the performance in detecting novel
classes.

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|  Fahong Zhang received the B.E. degree in software engineering from
Northwestern Polytechnical University, Xi’an, China, in 2017, the M.S. degree
in computer science with the Center for OPTical IMagery Analysis and Learning,
Northwestern Polytechnical University, Xi’an, China. He is now pursuing the
Ph.D. degree with the department of Aerospace and Geodesy, Data Science in
Earth Observation, Technical University of Munich. His reserach interests
include computer vision and satellite image processing.
---|---
|  Yilei Shi (Member, IEEE) received the Dipl.-Ing. degree in mechanical
engineering and the Dr.-Ing. degree in signal processing from the Technical
University of Munich (TUM), Munich, Germany, in 2010 and 2019, respectively.
He is a Senior Scientist with the Chair of Remote Sensing Technology, TUM. His
research interests include fast solver and parallel computing for large-scale
problems, high-performance computing and computational intelligence, advanced
methods on synthetic aperture radar (SAR) and InSAR processing, machine
learning, and deep learning for variety of data sources, such as SAR, optical
images, and medical images, and partial differential equation (PDE)-related
numerical modeling and computing.
---|---
|  Zhitong Xiong (Member, IEEE) received the Ph.D. degree in computer science
and technology from Northwestern Polytechnical University, Xi’an, China, in
2021. He is currently a research scientist and leads the ML4Earth working
group with the Data Science in Earth Observation, Technical University of
Munich (TUM), Germany. His research interests include computer vision, machine
learning, Earth observation, and Earth system modeling.
---|---
|  Xiao Xiang Zhu (S’10–M’12–SM’14–F’21) received the Master (M.Sc.) degree,
her doctor of engineering (Dr.-Ing.) degree and her “Habilitation” in the
field of signal processing from Technical University of Munich (TUM), Munich,
Germany, in 2008, 2011 and 2013, respectively. She is the Chair Professor for
Data Science in Earth Observation at Technical University of Munich (TUM) and
was the Founding Head of the Department “EO Data Science” at the Remote
Sensing Technology Institute, German Aerospace Center (DLR). Since 2019, Zhu
is a co-coordinator of the Munich Data Science Research School (www.mu-ds.de).
Since 2019 She also heads the Helmholtz Artificial Intelligence – Research
Field “Aeronautics, Space and Transport”. Since May 2020, she is the PI and
director of the international future AI lab ”AI4EO – Artificial Intelligence
for Earth Observation: Reasoning, Uncertainties, Ethics and Beyond”, Munich,
Germany. Since October 2020, she also serves as a Director of the Munich Data
Science Institute (MDSI), TUM. Prof. Zhu was a guest scientist or visiting
professor at the Italian National Research Council (CNR-IREA), Naples, Italy,
Fudan University, Shanghai, China, the University of Tokyo, Tokyo, Japan and
University of California, Los Angeles, United States in 2009, 2014, 2015 and
2016, respectively. She is currently a visiting AI professor at ESA’s Phi-lab.
Her main research interests are remote sensing and Earth observation, signal
processing, machine learning and data science, with their applications in
tackling societal grand challenges, e.g. Global Urbanization, UN’s SDGs and
Climate Change. Dr. Zhu is a member of young academy (Junge Akademie/Junges
Kolleg) at the Berlin-Brandenburg Academy of Sciences and Humanities and the
German National Academy of Sciences Leopoldina and the Bavarian Academy of
Sciences and Humanities. She serves in the scientific advisory board in
several research organizations, among others the German Research Center for
Geosciences (GFZ) and Potsdam Institute for Climate Impact Research (PIK). She
is an associate Editor of IEEE Transactions on Geoscience and Remote Sensing
and serves as the area editor responsible for special issues of IEEE Signal
Processing Magazine. She is a Fellow of IEEE.
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