Citation: Liang, G.; Xie, F.; Chien, Y.-R.
Class-Aware Self- and Cross-Attention
Network for Few-Shot Semantic
Segmentation of Remote Sensing
Images. Mathematics 2024,12, 2761.
https://doi.org/10.3390/
math12172761
Academic Editors: Volodymyr
Ponomaryov, Vladimir Lukin, Bogdan
Smolka and Beatriz P. García
Salgado
Received: 7 August 2024
Revised: 29 August 2024
Accepted: 5 September 2024
Published: 6 September 2024
Copyright: © 2024 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
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4.0/).
mathematics
Article
Class-Aware Self- and Cross-Attention Network for Few-Shot
Semantic Segmentation of Remote Sensing Images
Guozhen Liang 1,†, Fengxi Xie 1,† and Ying-Ren Chien 2,*
1Department of Electrical Engineering and Computer Science, Technische Universität Berlin,
2Department of Electrical Engineering, National Ilan University, Yilan 260007, Taiwan
*Correspondence: yr[email protected]
†These authors contributed equally to this work.
Abstract: Few-Shot Semantic Segmentation (FSS) has drawn massive attention recently due to its
remarkable ability to segment novel-class objects given only a handful of support samples. However,
current FSS methods mainly focus on natural images and pay little attention to more practical and
challenging scenarios, e.g., remote sensing image segmentation. In the field of remote sensing image
analysis, the characteristics of remote sensing images, like complex backgrounds and tiny foreground
objects, make novel-class segmentation challenging. To cope with these obstacles, we propose a
Class-Aware Self- and Cross-Attention Network (CSCANet) for FSS in remote sensing imagery,
consisting of a lightweight self-attention module and a supervised prior-guided cross-attention
module. Concretely, the self-attention module abstracts robust unseen-class information from support
features, while the cross-attention module generates a superior quality query attention map for
directing the network to focus on novel objects. Experiments demonstrate that our CSCANet achieves
outstanding performance on the standard remote sensing FSS benchmark iSAID-5
i
, surpassing the
existing state-of-the-art FSS models across all combinations of backbone networks and
K
-shot settings.
Keywords: few-shot learning; few-shot semantic segmentation; remote sensing; class-aware self- and
cross-attention
MSC: 68U05; 68U10
1. Introduction
Remote sensing image analysis has greatly contributed to academic research, indus-
trial development, and public affairs management, as remote sensing images are rich in
geographical information [
1
–
3
]. In the context of remote sensing image analysis, semantic
segmentation aims to assign predefined geospatial categories to the images at pixel level [
4
].
The emergence of convolutional neural networks (CNNs) has significantly advanced the
development of semantic segmentation [
5
–
8
]. However, the remarkable performance
of these CNN-based models relies heavily on large datasets. In addition, traditional se-
mantic segmentation models struggle to generalize to classes that are absent from the
training dataset.
To deal with these problems, Few-Shot Semantic Segmentation (FSS) has been devel-
oped. This technique enables the deep models to segment novel-class objects with scarce
support examples, which has been proven effective in low-data scenarios [
9
]. The conceptu-
alization of FSS was first defined by Shaban et al. [
9
]. Afterward, many researchers proposed
their own insights and pushed the performance of FSS to a new limit. Zhang et al. [
10
]
incorporated an attention module and an iterative optimization method into FSS, where
the support information is successfully merged and the segmentation results are improved
recursively. Lang et al. [
11
] proposed a base learner and an ensemble module to suppress
the false-positive prediction caused by the similarities between base classes and novel
Mathematics 2024,12, 2761. https://doi.org/10.3390/math12172761 https://www.mdpi.com/journal/mathematics
Mathematics 2024,12, 2761 2 of 14
classes. Despite impressive results, these methods mainly focused on the segmentation
of natural images, and few works investigated real-world scenarios [
12
–
14
]. The images
of these application scenarios have special properties and pose great challenges to the
segmentation task. For instance, remote sensing images, which are investigated in this pa-
per, have greater foreground–background class similarity and more tiny objects compared
with natural images. It can be observed in the first row of Figure 1, the target class ship,
ground track field and harbor are greatly similar to the background class harbor, grassland
and river bank, respectively. In addition, there is usually more than one target object to
be segmented in an image, and in some circumstances, they are too tiny to identify (as
shown in the second row of Figure 1). These unique characteristics would undoubtedly
lead to unsatisfactory predictions in the existing FSS frameworks (e.g., false activation and
coarse boundaries).
Figure 1. Characteristics of remote sensing images.
Furthermore, prevalent FSS approaches are mostly built on metric learning, which can
be divided into affinity learning [
15
–
17
] and prototype learning [
18
–
21
]. Affinity-learning-
based methods usually establish pixel-level support–query correspondences, which are
then aggregated into query prediction. These methods, however, failed to utilize the
semantic information from the extracted features and resulted in imperfect predictions.
In contrast, prototypical FSS approaches leverage one or two rich semantic class-wise
prototypes to construct prototype–query connections for query segmentation. For instance,
SG-One [
10
] applied masked average pooling (MAP) over support features to generate
the class representative prototype vectors, against which the query feature is matched by
the cosine similarity metric to yield query segmentation. More recently, researchers have
striven to elevate the performance of the prototypical FSS paradigm by obtaining more
guidance from class-wise prototypes such as PPNet [
22
], PFENet [
20
], ASGNet [
21
] and
SD-AANet [
17
]. However, depending solely on scarce compressed prototypes is bound
to incur information loss, making it difficult to deal with challenging scenarios in remote
sensing image segmentation.
To cope with the aforementioned problems, we proposed a Class-Aware Self- and
Cross-Attention Network (CSCANet) for the FSS of remote sensing images. The proposed
CSCANet consists of the self-attention module (SAM) and the prior-guided supervised
cross-attention module (PG-CAM). Firstly, a CBAM [
23
]-like self-attention module is de-
signed to exploit unseen-class information from support images. Specifically, we incorpo-
rate a weighted max pooling branch to extract robust discriminative novel-class features.
Secondly, a prior-guided supervised cross-attention mechanism is proposed to direct our
CSCANet to concentrate on the unseen classes in the query set. In detail, we first generate
the prior similarity mask by measuring the cosine similarity between the intermediate-level
support and query features. The prior similarity mask and support masks, along with
Mathematics 2024,12, 2761 3 of 14
support and query features, are fed into the cross-attention module to yield a high-quality
affinity attention map.
In summary, the contributions of our work include the following:
•
We devise an efficient self-attention module, which makes use of support features and
the corresponding ground-truth mask to mine the unseen-class information distinct
from the background classes.
•
We propose a prior-guided supervised cross-attention module to generate a high-
quality query attention map. The query attention map can outline the tiny objects in
images, which enhances the network’s ability to segment tiny targets.
•
The CSCANet outperforms the existing FSS methods across almost all the combina-
tions of backbone networks (VGG-16, ResNet-50) and few-shot settings (one-shot and
five-shot) on the standard remote sensing benchmark iSAID-5i.
2. Related Work
2.1. Semantic Segmentation
Semantic segmentation stands as a foundational computer vision task with the pri-
mary goal of accomplishing pixel-level classification in images, categorizing each pixel into
annotated semantic categories. Benefiting from the emergence of fully convolutional net-
works (FCNs) [
5
], significant advancements in this field have been achieved. For example,
Unet [
24
] adopted an encoder–decoder-like architecture to generate the predicted mask in
a symmetric manner. Later on, PSPNet [
25
] incorporated a pyramid pooling module to
enhance the robustness of image features. In addition, an attention mechanism was also
employed to direct the network to focus on the foreground regions [
26
]. Although tradi-
tional segmentation models have achieved impressive performance, they face a challenge in
effectively adapting to novel-class objects as they heavily depend on a substantial number
of annotated samples, hindering their practical applications to some extent.
2.2. Few-Shot Learning
Few-shot learning (FSL) aims to train models with scarce labeled examples, promoting the
generalization ability of deep networks in scenarios with limited data. Most of the prevalent
FSL approaches are implemented within the meta-learning paradigm [
27
], which has three sub-
divisions: metric-based [
28
–
30
], optimization-based [
31
–
33
] and augmentation-based [
34
]. Our
work is built upon the metric-based approaches, where distance metrics (e.g., cosine distance,
Euclidean distance) are leveraged to measure the support–query similarities.
2.3. Few-Shot Semantic Segmentation
Few-Shot Semantic Segmentation (FSS) has gained massive attention as an extension
of FSL. FSS aims to adapt deep networks to predict pixel-to-pixel correspondence between
support–query image pairs. This technique facilitates unseen-class segmentation, making it
a promising solution for challenges in low-data regimes. The problem of FSS was initially
formulated by Shaban et al. [
9
]. They proposed OSLSM to make query predictions using
a classifier trained on the support branch. After that, Zhang et al. [
10
] proposed the first
end-to-end prototypical FSS framework, which has become the paradigm in the field
of FSS. ASGNet [
21
] adaptively extracted multiple prototypes according to the feature
similarity and allocated them in the prototype–query matching based on an attention-
like algorithm. Lang et al. [
11
] proposed a novel FSS paradigm where an auxiliary base
learner was leveraged to explicitly identify confusing target regions that are similar to the
base-class objects.
However, existing prevalent methods are mainly designed for natural image segmenta-
tion, which fails to consider the tricky properties of remote sensing images. Wang et al. [
14
]
proposed a metametric-based FSS framework for few-shot geographical image segmenta-
tion, where the feature comparison sub-branch and affinity-based feature aggregation were
introduced to improve the predictions. Lang et al. [
35
] designed a few-shot remote sensing
image segmentation framework, in which the proposed global rectification and decouple
Mathematics 2024,12, 2761 4 of 14
registration mechanism address the inter-class similarity and intra-class diversity to some
extent. Nevertheless, these approaches did not thoroughly solve the aforementioned com-
plicated cases in remote sensing image segmentation. Therefore, we propose a lightweight
self-attention module and a supervised cross-attention module to solve these problems and
push the performance to a new level.
3. Methodology
In this section, we first introduce the problem setting in Section 3.1. The overall
architecture of our CSCANet is mentioned in Section 3.2. Then, in Sections 3.3 and 3.4, we
describe our lightweight self-attention block and prior-guided supervised cross-attention
block in detail, respectively. Section 3.5 is about the ASPP module and classifier. Finally,
we briefly introduce the K-shot setting of our proposed method in Section 3.6.
3.1. Problem Definition
The goal of Few-Shot Semantic Segmentation is to segment novel-class targets with
merely a few annotated exemplars. The training process of FSS models is usually performed
within the meta-learning paradigm, also known as episodic training [
36
]. To ensure a reli-
able generalization ability, the model training and testing phases are separately performed
on two subsets
Dtrain
(sufficient base classes) and
Dtest
(scarce unseen classes) with no
overlapped classes. Both image sets contain a series of episodes. Each episode includes a
small number of support sets
S=Ii
s,Mi
sK
i=1
and a query set
Q=Iq,Mq
, where
I∗
denotes a raw image and
M∗
the corresponding ground-truth mask. In each episode
of training, a support set
S
and a query image
Iq
are input to the model, with each query
prediction supervised by its corresponding ground-truth mask. During each episode of the
testing stage, the model is tested on Dtest to assess the performance.
3.2. Overall Framework
Figure 2depicts the overall architecture of our CSCANet under a 1-shot setting.
Initially, a pre-trained backbone network is utilized to extract support and query features
from input image sets. The support features
F2
s
of block2 and
F3
s
of block3 are concatenated
and then processed by a 1
×
1 convolution to generate the intermediate-level support
feature F23
s:
F23
s=Conv1×1{F2
sc
F3
s}, (1)
where
c
represents the concatenate operation. Thereafter, the support prototypes
Vs
can
be calculated as follows:
F23
masked =F23
s⊙ζ(Ms), (2)
Vs=Favg_pool(F23
masked), (3)
Here,
⊙
denotes element-wise multiplication,
ζ
is the bi-linear interpolation function such
that
RH×W→Rc×h×w
.
Favg_pool
represents the average pooling operation. In the self-
attention module, the support feature
F23
s
and its corresponding support mask are utilized
to yield the support attention feature map
As
. Thereafter, the support and query features,
as well as the prototype vector, are fed into the cross-attention module to yield a query
attention map. Subsequently, the support attention feature map, query attention map
and prototype vector, along with the query feature, are input to a dilated ASPP module
for feature refinement. The enriched feature is processed by the classifier, where 3
×
3 and
1×1 convolution are applied to generate the query prediction.
Mathematics 2024,12, 2761 5 of 14
Figure 2. Meta learner of our proposed CSCANet.
3.3. Self-Attention Module
In the context of limited cues provided by the support prototypes, we proposed an
efficient self-attention module to exploit novel-class cues from the scarce support images,
which guides the network to concentrate on the unseen-class objects and avoid false
activation. As shown in Figure 3, we first generate the pooling vector as follows:
Vpool =Favg_pool(F23
masked)⊕α∗ Fmax_pool(F23
masked), (4)
Here,
Fmax_pool
denotes the max pooling operation, and
⊕
represents the element-wise
addition. The average pooling operation is employed to extract the global general features
of the novel-class objects, while the max pooling operation is applied to abstract the local
discriminative unseen-class features. However, we notice that directly incorporating the
max pooling branch will result in a non-uniform feature representation of the novel classes.
Therefore, we adopt a learnable parameter
α
to weight the max pooling branch and mitigate
this side effect. We set the initial value of
α
to 1. Subsequently, the attention vector can be
derived as follows:
Va=σ(ConvN(Vpool)), (5)
where
ConvN
refers to a series of convolutional layers, and
σ
denotes the activation function
Sigmoid, respectively.
Finally, a foreground-focused support attention map is generated as follows:
As=F23
s⊙Va, (6)
Figure 3. Architecture of the proposed SAM in 1-shot setting.
Mathematics 2024,12, 2761 6 of 14
3.4. Prior-Guided Supervised Cross-Attention Module
A high-quality query attention map is an important hint for accurate novel-class seg-
mentation. We proposed a prior-guided supervised cross-attention block to generate such
an attention map, which is capable of accurately capturing the query targets regardless
of their sizes. PFENet [
20
] introduced a similar attention mechanism, where the cosine
similarity between the deepest support and query features is calculated to generate a query
attention map. However, the backbone network adopted to extract the image features is
pre-trained on ImageNet [
37
] for classification tasks, which would be ineffective for FSS. In
contrast, we treat the cosine similarity map as a prior and adopt the pyramid pooling mod-
ule (PPM) [
25
] as the feature extractor, which is trained in a standard supervised manner.
The architecture of the proposed PG-CAM is visualized in Figure 4.
Figure 4. Architecture of the proposed PG-CAM in 1-shot setting.
In detail, the cosine similarity between query feature
F3
q
and support prototype
Vs
is
calculated to generate the prior similarity mask
Mcrs
, which serves as an important clue to
locating the target regions:
Mcrs(x,y) = arg max
k
exp(γϕ(F3
q(x,y),Vk
s))
∑Vk
s∈Vall
sexp(γϕ(F3
q(x,y),Vk
s)), (7)
where x∈ {1, ..., h},y∈ {1, ..., w},k∈ {1, ..., N}, and we set γto 10 in all experiments.
For the support branch, we first concatenate the support prototype, the support feature
F23
s
and the prior similarity mask
Mcrs
and pass them through PPM. Subsequently, a 1
×
1
convolution is used to generate support prediction Pswith two output channels:
Ps=Conv1×1DeF23
sc
Vsc
Mcrs, (8)
Thereafter, the ground-truth support mask is applied to supervise the training of the
proposed cross-attention module:
Lce,s=−
h
∑
x=1
w
∑
y=1
(Ms(x,y)·log(Ps(x,y))), (9)
where
Lce,s
represents the cross-entropy loss for the support prediction.
Ms(x
,
y)
and
Ps(x
,
y)
denote the (x,y)location of support ground truth and support prediction, respectively.
The same operation as in the support branch is applied for the affinity attention map
prediction, except that the output of the 1×1 convolution is a binary mask:
Mattn =Conv1×1DeF23
qc
Vsc
Mcrs, (10)
Mathematics 2024,12, 2761 7 of 14
3.5. Classifier
The obtained support attention feature map
As
and the query affinity attention map
Mattn
are concatenated along with the support prototype
Vs
and the query feature
F23
q
.
A dilated version of the ASPP module is introduced to merge and enrich these concatenated
features. Finally, we obtain the mask prediction P∈R2×h×wthrough
F23
q=Conv1×1{F2
qc
F3
q}, (11)
Fmerged =FguidanceMattn,As,Vs,F23
q, (12)
P=So f tmaxDmFmerged, (13)
where
Fguidance
denotes the combination of concatenate and expand operations.
Dm
consists
of the ASPP module, convolutional operation and classifier.
Finally, binary cross-entropy (BCE) loss between
Mq(j)
and
P(j)
is employed to
supervise the training of the meta learner:
Lm=1
nep
nep
∑
j=1
BCE(Mq(j),P(j)), (14)
where nep represents the number of training episodes in each batch.
3.6. K-Shot Setting
In
K
-shot (
K
> 1) segmentation, there are
K
support sets available. For the self-attention
mechanism, we directly take the average of
K
and generate support attention maps. For the
query affinity attention map prediction,
K
support features are fed into the cross-attention
module separately, with each prediction supervised by its own label. Then, we average the
Klosses as follows:
Lce,s=
K
∑
i=1
Li
ce,s, (15)
where Li
ce,sdenotes the cross-entropy loss of the i-th support image.
Finally, the
K
-times generated support attention feature map
As
and support proto-
types Vsare averaged. Then, the averaged Asand Vsconcatenated with F23
qand Mattn are
passed through the ASPP module to obtain the predictions.
4. Experiments
4.1. Experimental Setup
Dataset. We assess the effectiveness of our approach on the standard remote sensing
benchmark dataset iSAID-5
i
[
38
], which is generated from 2806 high-resolution images. This
publicly available aerial image dataset includes 655,451 object instances from 15 geospatial
categories. We employ a cross-validation strategy for our experiments, dividing the dataset
into three evenly distributed folds, where one fold is used for meta testing and the remaining
folds are adopted for meta training. We randomly select 1000 support–query image pairs
for validation in each training episode. As shown in Table 1, we select the unseen classes in
each fold following the experimental settings of [
13
,
35
], in which the determination of the
categories is based on the original sequence of the label dictionary [38].
Table 1. Selection of novel classes for each fold of iSAID-5idataset.
# Fold Novel Classes
0 Ship (C1) Storage tank (C2) Baseball diamond (C3) Tennis court (C4) Basketball court (C5)
1 Ground track field (C6) Bridge (C7) Large vehicle (C8) Small vehicle (C9) Helicopter (C10)
2 Swimming pool (C11) Roundabout (C12) Soccer ball field (C13) Plane (C14) Harbor (C15)
Mathematics 2024,12, 2761 8 of 14
Evaluation Metrics. Consistent with previous studies [
11
,
22
,
39
], we employ the mean
intersection over union (MIoU) for performance assessment. In addition, foreground–
background IoU (FB-IoU) is also adopted as the evaluation metric.
Implementation Details. In order to enhance the network’s generalization ability,
most of the existing FSS approaches use a backbone network pre-trained on the large natural
image dataset ImageNet [
37
], the parameters of which are frozen in the meta training phase.
This backbone network cannot perfectly adapt to remote sensing image segmentation due
to the unignorable domain shift. Hence, we train a more suitable backbone network on
iSAID-5
i
from scratch within the standard supervised learning paradigm. The backbone
network is initialized with the parameters pre-trained on ImageNet [
37
]. We set the learning
rate, training epoch and batch size to 1.25 ×10−3, 50 and 16, respectively.
For the meta training, we adopt the episodic training strategy [
11
,
36
]. Specifically, we
train the CSCANet using SGD optimizer for 12 epochs, with learning rate and batch size
configured as 5
×
10
−2
and 8, respectively. We adopt a similar data augmentation strategy
to [35]. All experiments are conducted in PyTorch [40] on 4 NVIDIA Tesla T4s.
For a fair comparison, we run the source codes of the selected prevalent FSS ap-
proaches, except that we adopt the same retrained backbone network for training. Addi-
tionally, we use the same hyper-parameters for training as in our CSCANet.
4.2. Visualization Analysis
Visualization of segmentation results. We visualize some representative predicted
masks generated by our CSCANet in Figure 5. The first two rows depict examples of
support images (blue) and query images (green). The last two rows show the samples
of baseline predictions and the results of CSCANet, respectively. It can be seen in all the
examples that the proposed CSCANet is able to effectively reduce false activation. The last
five columns show that the proposed method is capable of segmenting the multiple tiny
query targets more precisely and completely than the baseline. The predicted masks are
almost identical to the corresponding labels.
Figure 5. Qualitative examples of 1-shot prediction on the iSAID-5i.
Visualization of query affinity attention map. To investigate the quality of query
attention maps generated by PG-CAM, we plot some representative attention maps in
Figure 6. Given the supported image(s) (the 1st row) and query image (the 2nd row),
the cross-attention module is able to effectively capture the query targets regardless of their
sizes and quantities.
Mathematics 2024,12, 2761 9 of 14
Figure 6. Visualization of the cross-attention maps generated by PG-CAM on the iSAID-5
i
in the
1-shot setting.
4.3. Comparison with State of the Art
We compare the performance of CSCANet against other state-of-the-art FSS ap-
proaches. Table 2demonstrates the performance of different approaches on iSAID-5
i
in terms of MIoU and FB-IoU. The results indicate that our CSCANet outperforms all SOTA
methods across almost all combinations of backbone network (VGG-16 and ResNet-50) and
few-shot settings (1-shot and 5-shot), except in the case of backbone VGG-16 under the
1-shot setting. For backbone ResNet-50, we achieve 1.61%mIoU (1-shot) and 2.04%mIoU (5-
shot) performance improvements over the best competitor R2Net. Remarkably, CSCANet
significantly surpasses the second-best approach under a 5-shot setting by 2.12%mIoU on
average for both backbones. Additionally, we also list the model complexity and inference
speed in Table 3. It can be observed that our proposed method reaches a superior balance
between performance and efficiency.
Table 2. Comparison of the CSCANet with other FSS networks on iSAID-5
i
under 1-shot and 5-shot
settings. The results that are
underlined
denote the second-best performance, while the results that
are bold show the best performance (the same applies to all the following tables).
Backbone Method 1-Shot 5-Shot
Fold-0 Fold-1 Fold-2 MIoU% FB-IoU% Fold-0 Fold-1 Fold-2 MIoU% FB-IoU%
VGG-16
PANet(ICCV-19) [18] 26.86 14.56 20.69 20.70 52.69 30.89 16.63 24.05 23.86 54.75
CANet (CVPR-19) [19] 13.91 12.94 13.67 13.51 53.98 17.32 15.07 18.23 16.87 56.86
SCL (CVPR-21) [41] 25.75 18.57 22.24 22.19 58.96 35.77 24.92 32.70 31.13 61.56
PFENet (TPAMI-22) [20] 28.52 17.05 18.94 21.50 57.79 37.59 23.22 30.45 30.42 60.84
NERTNet (CVPR-22) [42] 25.78 20.01 19.88 21.89 56.34 38.43 24.21 28.99 30.54 61.97
DCP (arXiv-22) [43] 28.17 16.52 22.49 22.39 59.55 39.65 22.68 29.93 30.75 60.78
BAM (CVPR-22) [11] 33.93 16.88 21.47 24.09 59.20 38.46 22.76 28.81 30.01 62.26
DMML (TGRS-21) [14] 24.41 18.58 19.46 20.82 54.21 28.97 21.02 22.78 24.26 54.89
SDM (TGRS-22) [13] 24.52 16.31 21.01 20.61 56.39 26.73 19.97 26.10 24.27 56.65
DML (GRSL-22) [44] 30.99 14.60 19.05 21.55 55.98 34.03 16.38 26.32 25.48 56.26
TBPN (IJON-23) [45] 27.86 12.32 18.16 19.45 54.26 32.79 16.28 24.27 24.45 55.79
R2Net (TGRS-23) [35]35.27 19.93 24.63 26.61 61.71 42.06 23.52 30.06 31.88 63.55
CSCANet (Ours) 33.26 20.44 25.98 26.56 61.45 40.08 24.15 38.00 34.08 63.74
ResNet-50
PANet(ICCV-19) [18] 27.56 17.23 24.60 23.13 56.56 36.54 16.05 26.22 26.27 57.37
CANet (CVPR-19) [19] 25.51 13.50 24.45 21.15 56.64 29.32 21.85 26.91 26.03 59.46
SCL (CVPR-21) [41] 34.78 22.77 31.20 29.58 61.30 41.29 25.73 37.70 34.91 64.13
PFENet (TPAMI-22) [20] 35.84 23.35 27.20 28.80 60.09 42.42 25.34 33.00 33.59 63.25
NERTNet (CVPR-22) [42] 34.93 23.95 28.56 29.15 59.97 44.83 26.73 37.19 36.25 64.45
DCP (arXiv-22) [43] 37.83 22.86 28.92 29.87 62.36 41.52 28.18 33.43 34.38 63.37
BAM (CVPR-22) [11] 39.43 21.69 28.64 29.92 62.04 43.29 27.92 38.62 36.61 65.00
DMML (TGRS-21) [14] 28.45 21.02 23.46 24.31 57.78 30.61 23.85 24.08 26.18 58.26
SDM (TGRS-22) [13] 27.96 21.99 27.82 25.92 59.58 28.50 25.23 31.07 28.27 59.90
DML (GRSL-22) [44] 32.96 18.98 26.27 26.07 58.93 33.58 22.05 29.77 28.47 59.23
TBPN (IJON-23) [45] 29.33 16.84 25.47 23.88 57.34 30.98 20.42 28.07 26.49 58.63
R2Net (TGRS-23) [35] 41.22 21.64 35.28 32.71 63.82 46.45 25.80 39.84 37.36 66.18
CSCANet (Ours) 42.30 24.17 36.50 34.32 63.56 47.85 30.04 40.32 39.40 66.32
Mathematics 2024,12, 2761 10 of 14
Table 3. Model complexity and average speed (FPS) comparisons between our approach (ResNet-50,
1-shot) and previous state-of-the-art methods.
Ours PANet [18]
CANet [
19
]
SCL [41]
PFENet [
20
] DCP [
43
]
#Params. 5.2M 23.6M 22.3M 11.9M 10.8M
11.3M
FPS 40.36 58.1 32.7 39.2 45.7
37.9
BAM [11]
DMML [
14
]
SDM [13] DML [44] TBPN [45]
R
2Net
[
35
]
#Params 4.9M 23.6M 29.3M 23.6M 23.6M 5.0M
FPS 44.4 47.4 52.9 59.5 56.5
41.5
In addition, we also list the class-wise results in Table 4. It is noteworthy that our
proposed CSCANet surpasses other prevalent FSS methods with the backbone ResNet-
50 in class C12 (Roundabout) and C14 (Plane) by 13.32%mIoU and 4.73%, separately.
The proposed method also obtained the second-best performances in class C1 (Ship), C2
(Storage tank), C3 (Baseball diamond) and C4 (Tennis court). The sizes of these categories
are usually tiny and densely arranged in an image, indicating our proposed method is
capable of accurately segmenting multiple tiny target objects.
Table 4. Class-wise comparison of CSCANet with other FSS networks on iSAID-5iunder 1-shot setting.
Method C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12 C13 C14 C15 MIoU%
VGG-16
PANet(ICCV-19) [18] 20.05 37.71 21.18 41.22 14.15 12.17 13.82 21.05 7.89 17.88 4.36 31.68 27.55 26.88 12.97 20.70
CANet (CVPR-19) [19] 24.13 6.73 13.83 16.32 8.54 14.12 3.24 21.04 3.35 22.96 9.57 14.91 17.83 16.11 9.92 13.51
SCL (CVPR-21) [41] 28.50 32.93 19.68 29.60 18.05 22.48 7.92 31.46 8.99 22.02 14.17 16.53 19.72 39.40 21.37 22.19
PFENet (TPAMI-22) [20] 34.32 31.81 24.20 35.43 16.86 13.98 6.01 31.68 6.76 26.85 8.15 17.75 20.56 33.34 14.87 21.50
NERTNet (CVPR-22) [42] 12.66 23.11 26.90 50.47 15.77 23.14 8.48 31.73 11.75 24.94 14.63 20.45 29.03 28.06 7.24 21.89
DCP (arXiv-22) [43] 27.69 38.45 25.92 33.20 15.57 17.62 12.36 26.79 8.05 17.80 22.45 18.29 18.03 37.57 16.10 22.39
BAM (CVPR-22) [11] 27.66 43.90 31.48 43.96 22.66 13.57 8.91 31.76 9.26 20.91 17.05 26.27 30.68 25.27 8.07 24.09
DMML (TGRS-21) [14] 34.75 37.36 15.15 22.85 11.94 21.41 13.85 23.92 10.24 23.50 8.17 16.32 21.08 29.63 22.09 20.82
SDM (TGRS-22) [13] 33.76 23.88 17.80 27.76 19.38 18.36 9.63 25.24 8.63 19.69 10.56 15.36 24.76 32.30 22.06 20.61
DML (GRSL-22) [44] 27.30 42.63 19.25 50.63 15.13 14.16 15.94 22.40 7.74 12.74 3.79 23.73 23.47 27.40 16.88 21.55
TBPN (IJON-23) [45] 22.03 39.75 20.80 42.80 13.94 10.41 6.87 16.54 4.38 23.41 5.68 23.66 22.13 24.63 14.72 19.45
R2Net (TGRS-23) [35]37.82 45.16 26.27 45.30 21.81 24.11 14.38 30.92 12.21 18.03 18.66 25.02 29.64 31.95 17.87 26.61
CSCANet (Ours) 36.21 43.88 26.01 43.39 16.81 21.80 15.84 26.65 10.58 27.33 9.05 41.67 32.19 31.01 15.97 26.56
ResNet-50
PANet (ICCV-19) [18] 21.81 36.31 23.01 42.06 14.59 12.11 17.44 22.70 12.27 21.60 30.29 24.62 26.79 25.54 15.79 23.13
CANet (CVPR-19) [19] 39.57 18.54 18.46 33.63 17.34 9.78 5.49 22.15 5.17 24.89 9.96 36.50 19.12 38.82 17.85 21.15
SCL (CVPR-21) [41] 37.61 33.63 26.68 54.75 21.22 22.60 24.40 30.22 6.71 29.93 33.00 44.68 18.25 44.63 15.46 29.58
PFENet (TPAMI-22) [20] 39.02 45.63 20.86 49.96 23.72 21.00 24.76 31.59 6.98 32.42 13.34 47.64 30.65 32.82 11.54 28.80
NERTNet (CVPR-22) [42] 33.59 42.83 22.30 49.35 21.91 21.62 28.82 25.64 9.35 34.30 23.91 38.67 25.63 40.84 13.74 28.83
DCP (arXiv-22) [43] 37.42 42.44 35.16 56.55 17.58 21.66 19.57 32.97 10.60 29.50 24.02 35.34 28.44 39.80 17.02 29.87
BAM (CVPR-22) [11] 36.34 39.76 38.23 58.13 24.71 18.25 12.68 35.91 11.42 30.21 28.98 40.74 29.43 33.25 10.79 29.92
DMML (TGRS-21) [14] 40.14 40.18 21.31 27.02 13.60 15.56 15.19 26.05 13.84 34.44 11.26 17.57 23.27 39.11 26.12 24.31
SDM (TGRS-22) [13] 41.77 35.50 21.41 20.81 20.29 15.60 25.60 28.66 13.29 26.79 13.61 32.35 24.59 42.79 25.75 25.92
DML (GRSL-22) [44] 35.13 42.10 30.49 41.79 15.31 13.25 16.87 24.70 14.62 25.45 10.24 35.49 25.35 41.69 18.57 26.07
TBPN (IJON-23) [45] 25.36 41.28 30.67 32.88 16.48 13.48 9.74 27.88 12.52 20.56 11.12 34.31 23.57 40.36 17.98 23.88
R2Net (TGRS-23) [35]46.87 49.06 30.70 52.86 26.62 24.31 17.25 31.25 13.67 21.73 24.88 46.07 42.29 42.07 21.08 32.71
CSCANet (Ours) 45.96 47.83 36.62 57.99 23.10 21.27 23.45 29.87 11.98 34.28 18.69 59.39 37.45 46.80 20.17 34.32
4.4. Limitation Analysis
We observe that the proposed method has a poor performance in C9 (Small vehicle)
with both backbone networks. We assume that this is due to the class similarity between
C9 (Small vehicle) and other classes like C1 (Ship), C7 (Bridge), and C8 (Large vehicle) in
the top-view conditions.
We also visualize some representative failure cases of our proposed method in
Figure 7.
Failure cases happen mainly due to different resolutions (row 1) and intra-class discrepancy
(row 2 and row 3). These are also the major challenges faced by the current Few-Shot
Mathematics 2024,12, 2761 11 of 14
Semantic Segmentation methods for remote sensing images. In the case of limited rep-
resentativeness, our attention mechanism may concentrate on unrepresentative target
information, leading to performance degradation.
Figure 7. Visualization of the failure cases of the proposed CSCANet on iSAID-5
i
(ResNet50, 1-shot setting).
4.5. Ablation Studies
The ablation study aims to examine the importance of each component of our CSCANet.
We conducted a variety of ablation experiments on iSAID-5
i
under a 1-shot setting, with ResNet-
50 selected as the backbone network. The results are presented in Table 5.
Table 5. Ablation study of our CSCANet at module level. The first row represents the result of the baseline.
Self Attention Cross
Attention Alpha Prior MIoU% FB-IoU%
- - - - 32.85 61.75
!- - - 33.01 61.81
!-!- 33.18 62.13
-!- - 33.61 62.50
-!-!34.08 62.92
! ! ! ! 34.32 63.56
4.5.1. Effect of Self-Attention Module
Compared with the performance of the complete pipeline of CSCANet, the model
without a self-attention module reduces it to 0.24% in terms of mIoU. Furthermore, the first
two rows of Table 5show that introducing the learnable parameter
α
in the SAM brings a
further improvement of 0.17% mIoU, implying that
α
is important for abstracting a robust
feature representation of novel classes. These results demonstrate our SAM can effectively
extract robust class-relevant information and direct the model to concentrate on the novel
class targets.
4.5.2. Effect of Cross-Attention Module
A high-quality query affinity attention map has a significant impact on the final pre-
diction. Therefore, we conducted relevant ablation tests on PG-CAM, which is the core
component of CSCANet. As shown in the second and fifth rows of Table 5, the model with-
out PG-CAM decreases the performance to 1.14%. In particular, we also investigated the
impact of the prior map on the proposed PG-CAM. Referring to the third and fourth rows,
incorporating the prior similarity map achieved a 0.47% mIoU improvement, indicating
that the prior information plays a crucial role in guiding the cross-attention module to
focus on the unseen-class objects.
5. Conclusions
In this paper, we introduced a few-shot remote sensing image segmentation frame-
work named CSCANet to address the problems of foreground–background similarity and
Mathematics 2024,12, 2761 12 of 14
multiple tiny objects. The proposed CSCANet includes a simple yet effective self-attention
module and a prior-guided cross-attention module. Specifically, the first module is able
to extract robust unseen-class information from the support set and avoid undesired ac-
tivation. The second module generates a high-quality query attention map, which can
guide the network to concentrate on the tiny target regions. The proposed method demon-
strates an outstanding ability to adapt to unseen classes, achieving state-of-the-art (SOTA)
performance in both one-shot and five-shot settings.
The major factors in failure cases are different resolutions between support and query
sets and the intra-class discrepancy. To address these issues, we will adopt stronger
backbones (e.g., ResNet101, Swin-B) and incorporate transformer architecture to enhance
the model’s feature extraction ability in the future. Furthermore, we will validate the
proposed method on more remote sensing benchmark datasets and try to create a new
few-shot remote sensing image dataset. We will also explore the potential of extending the
proposed framework to the zero-shot remote sensing image segmentation task.
Author Contributions: Conceptualization, G.L., F.X. and
Y.-R.C.
; Methodology, G.L., F.X. and
Y.-R.C.
;
Experiments, G.L. and F.X.; Validation, G.L. and F.X.; Formal analysis, G.L., F.X. and Y.-R.C.; Investi-
gation, G.L.; Data curation, F.X.; Writing—original draft, G.L., F.X. and Y.-R.C.; Writing—review and
editing, Y.-R.C.; Visualization, G.L.; Project administration, Y.-R.C.; Funding acquisition, Y.-R.C. All
authors have read and agreed to the published version of the manuscript.
Funding: This work was supported in part by the National Science and Technology Council, Taiwan
(NSTC) under Grant 112-2221-E-197-022.
Data Availability Statement: The original data presented in the study are openly available in iSAID
at https://captain-whu.github.io/iSAID/ (accessed on 23 May 2024).
Conflicts of Interest: The authors declare no conflict of interest.
Abbreviations
The following abbreviations are used in this manuscript:
FSS Few-Shot Semantic Segmentation
FSL Few-Shot Learning
CNN Convolutional Neural Network
FCN Fully Convolutional Network
ASPP Atrous Spatial Pyramid Pooling
PPM Pyramid Pooling Module
MAP Masked Average Pooling
SAM Self Attention Module
PG-CAM Prior-Guided Supervised Cross-Attention Module
BCE Binary Cross Entropy
MIoU Mean Intersection Over Union
FB-IoU Foreground–Background Intersection Over-Union
References
1.
Sun, W.; Du, Q. Graph-regularized fast and robust principal component analysis for hyperspectral band selection. IEEE Trans.
Geosci. Remote Sens. 2018,56, 3185–3195. [CrossRef]
2.
Peng, J.; Sun, W.; Ma, L.; Du, Q. Discriminative transfer joint matching for domain adaptation in hyperspectral image classification.
IEEE Geosci. Remote Sens. Lett. 2019,16, 972–976. [CrossRef]
3.
Sun, X.; Yin, D.; Qin, F.; Yu, H.; Lu, W.; Yao, F.; He, Q.; Huang, X.; Yan, Z.; Wang, P.; et al. Revealing influencing factors on global
waste distribution via deep-learning based dumpsite detection from satellite imagery. Nat. Commun. 2023,14, 1444. [CrossRef]
[PubMed]
4.
Shelhamer, E.; Long, J.; Darrell, T. Fully convolutional networks for semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell.
2017,39, 640–651. [CrossRef] [PubMed]
5.
Long, J.; Shelhamer, E.; Darrell, T. Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE Conference
on Computer Vision and Pattern Recognition, Boston, MA, USA, 7–12 June 2015; pp. 3431–3440.
Mathematics 2024,12, 2761 13 of 14
6.
Lin, D.; Dai, J.; Jia, J.; He, K.; Sun, J. Scribblesup: Scribble-supervised convolutional networks for semantic segmentation.
In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 27–30 June 2016;
pp. 3159–3167.
7.
Zhang, H.; Dana, K.; Shi, J.; Zhang, Z.; Wang, X.; Tyagi, A.; Agrawal, A. Context encoding for semantic segmentation. In
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 18–23 June 2018;
pp. 7151–7160.
8.
Strudel, R.; Garcia, R.; Laptev, I.; Schmid, C. Segmenter: Transformer for semantic segmentation. In Proceedings of the IEEE/CVF
International Conference on Computer Vision, Montreal, QC, Canada, 10–17 October 2021; pp. 7262–7272.
9. Shaban, A.; Bansal, S.; Liu, Z.; Essa, I.; Boots, B. One-shot learning for semantic segmentation. arXiv 2017, arXiv:1709.03410.
10.
Zhang, X.; Wei, Y.; Yang, Y.; Huang, T.S. Sg-one: Similarity guidance network for one-shot semantic segmentation. IEEE Trans.
Cybern. 2020,50, 3855–3865. [CrossRef] [PubMed]
11.
Lang, C.; Cheng, G.; Tu, B.; Han, J. Learning what not to segment: A new perspective on few-shot segmentation. In Proceedings of
the IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA, 18–24 June 2022; pp. 8057–8067.
12.
Ouyang, C.; Biffi, C.; Chen, C.; Kart, T.; Qiu, H.; Rueckert, D. Self-supervision with superpixels: Training few-shot medical image
segmentation without annotation. In Proceedings of the Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK,
23–28 August 2020; Proceedings, Part XXIX 16; Springer: Cham, Switzerland, 2020; pp. 762–780.
13.
Yao, X.; Cao, Q.; Feng, X.; Cheng, G.; Han, J. Scale-aware detailed matching for few-shot aerial image semantic segmentation.
IEEE Trans. Geosci. Remote Sens. 2021,60, 5611711. [CrossRef]
14.
Wang, B.; Wang, Z.; Sun, X.; Wang, H.; Fu, K. Dmml-net: Deep metametric learning for few-shot geographic object segmentation
in remote sensing imagery. IEEE Trans. Geosci. Remote Sens. 2021,60, 5611118. [CrossRef]
15.
Zhang, C.; Lin, G.; Liu, F.; Guo, J.; Wu, Q.; Yao, R. Pyramid graph networks with connection attentions for region-based one-shot
semantic segmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, Seoul, Republic of Korea,
27 October–2 November 2019; pp. 9587–9595.
16.
Wang, H.; Zhang, X.; Hu, Y.; Yang, Y.; Cao, X.; Zhen, X. Few-shot semantic segmentation with democratic attention networks. In
Proceedings of the Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, 23–28 August 2020; Proceedings, Part
XIII 16; Springer: Cham, Switzerland, 2020; pp. 730–746.
17.
Zhao, Q.; Liu, B.; Lyu, S.; Chen, H. A self-distillation embedded supervised affinity attention model for few-shot segmentation.
IEEE Trans. Cogn. Dev. Syst. 2023,16, 177–189. [CrossRef]
18.
Wang, K.; Liew, J.H.; Zou, Y.; Zhou, D.; Feng, J. Panet: Few-shot image semantic segmentation with prototype alignment. In
Proceedings of the IEEE/CVF International Conference on Computer Vision, Seoul, Republic of Korea, 27 October–2 November
2019; pp. 9197–9206.
19.
Zhang, C.; Lin, G.; Liu, F.; Yao, R.; Shen, C. Canet: Class-agnostic segmentation networks with iterative refinement and attentive
few-shot learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA,
USA, 15–20 June 2019; pp. 5217–5226.
20.
Tian, Z.; Zhao, H.; Shu, M.; Yang, Z.; Li, R.; Jia, J. Prior guided feature enrichment network for few-shot segmentation. IEEE Trans.
Pattern Anal. Mach. Intell. 2020,44, 1050–1065. [CrossRef] [PubMed]
21.
Li, G.; Jampani, V.; Sevilla-Lara, L.; Sun, D.; Kim, J.; Kim, J. Adaptive prototype learning and allocation for few-shot segmentation.
In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA, 20–25 June 2021;
pp. 8334–8343.
22.
Liu, Y.; Zhang, X.; Zhang, S.; He, X. Part-aware prototype network for few-shot semantic segmentation. In Proceedings of the
Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, 23–28 August 2020; Proceedings, Part IX 16; Springer:
Cham, Switzerland, 2020; pp. 142–158.
23.
Woo, S.; Park, J.; Lee, J.-Y.; Kweon, I.S. Cbam: Convolutional block attention module. In Proceedings of the European Conference
on Computer Vision (ECCV), Munich, Germany, 8–14 September 2018; pp. 3–19.
24.
Ronneberger, O.; Fischer, P.; Brox, T. U-net: Convolutional networks for biomedical image segmentation. In Medical Image
Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, 5–9 October 2015,
Proceedings, Part III 18; Springer: Cham, Switzerland, 2015; pp. 234–241.
25.
Zhao, H.; Shi, J.; Qi, X.; Wang, X.; Jia, J. Pyramid scene parsing network. In Proceedings of the IEEE Conference on Computer
Vision and Pattern Recognition, Honolulu, HI, USA, 21–26 July 2017; pp. 2881–2890.
26.
Huang, Z.; Wang, X.; Huang, L.; Huang, C.; Wei, Y.; Liu, W. Ccnet: Criss-cross attention for semantic segmentation. In
Proceedings of the IEEE/CVF International Conference on Computer Vision, Seoul, Republic of Korea, 27 October–November
2019; pp. 603–612.
27.
Jindal, S.; Manduchi, R. Contrastive representation learning for gaze estimation. In Proceedings of the Annual Conference on
Neural Information Processing Systems, PMLR, New Orleans, LA, USA, 10–16 December 2023; pp. 37–49.
28.
Koch, G.; Zemel, R.; Salakhutdinov, R. Siamese neural networks for one-shot image recognition. In Proceedings of the ICML
Deep Learning Workshop, Lille, France, 6–11 July 2015; volume 2.
29. Snell, J.; Swersky, K.; Zemel, R. Prototypical networks for few-shot learning. Adv. Neural Inf. Process. Syst. 2017,30, 1–11.
Mathematics 2024,12, 2761 14 of 14
30.
Li, H.; Eigen, D.; Dodge, S.; Zeiler, M.; Wang, X. Finding task-relevant features for few-shot learning by category traversal. In
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA, 15–20 June 2019;
pp. 1–10.
31.
Finn, C.; Abbeel, P.; Levine, S. Model-agnostic meta-learning for fast adaptation of deep networks. In Proceedings of the
International Conference on Machine Learning, PMLR, Sydney, Australia, 6–11 August 2017; pp. 1126–1135.
32.
Jamal, M.A.; Qi, G.-J. Task agnostic meta-learning for few-shot learning. In Proceedings of the IEEE/CVF Conference on Computer
Vision and Pattern Recognition, Long Beach, CA, USA, 15–20 June 2019; pp. 11719–11727.
33.
Ravi, S.; Larochelle, H. Optimization as a model for few-shot learning. In Proceedings of the International Conference on Learning
Representations, San Juan, Puerto Rico, 2–4 May 2016.
34.
Chen, Z.; Fu, Y.; Chen, K.; Jiang, Y.-G. Image block augmentation for one-shot learning. AAAI Conf. Artif. Intell. 2019,33, 3379–3386.
[CrossRef]
35.
Lang, C.; Cheng, G.; Tu, B.; Han, J. Global rectification and decoupled registration for few-shot segmentation in remote sensing
imagery. IEEE Trans. Geosci. Remote Sens. 2023,61, 5617211. [CrossRef]
36.
Vinyals, O.; Blundell, C.; Lillicrap, T.; Wierstra, D. Matching networks for one shot learning. Adv. Neural Inf. Process. Syst. 2016,
29, 1–9.
37.
Deng, J.; Dong, W.; Socher, R.; Li, L.; Li, K.; Li, F.-F. Imagenet: A large-scale hierarchical image database. In Proceedings of the
2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA, 20–25 June 2009; pp. 248–255.
38.
Zamir, S.W.; Arora, A.; Gupta, A.; Khan, S.; Sun, G.; Khan, F.S.; Zhu, F.; Shao, L.; Xia, G.-S.; Bai, X. Isaid: A large-scale dataset for
instance segmentation in aerial images. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
Workshops, Long Beach, CA, USA, 16–17 June 2019; pp. 28–37.
39.
Yang, B.; Liu, C.; Li, B.; Jiao, J.; Ye, Q. Prototype mixture models for few-shot semantic segmentation. In Proceedings of the
Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, 23–28 August 2020; Proceedings, Part VIII 16; Springer:
Cham, Switzerland, 2020; pp. 763–778.
40.
Paszke, A.; Gross, S.; Massa, F.; Lerer, A.; Bradbury, J.P.; Chanan, G.; Killeen, T.; Lin, Z.; Gimelshein, N.; Antiga, L.; et al. An
imperative style, high-performance deep learning library. Adv. Neural Inf. Process. Syst 1912,32, 8026.
41.
Zhang, B.; Xiao, J.; Qin, T. Self-guided and cross-guided learning for few-shot segmentation. In Proceedings of the IEEE/CVF
Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA, 20–25 June 2021; pp. 8312–8321.
42.
Liu, Y.; Liu, N.; Cao, Q.; Yao, X.; Han, J.; Shao, L. Learning non-target knowledge for few-shot semantic segmentation. In
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA, 18–24 June 2022;
pp. 11573–11582.
43.
Lang, C.; Tu, B.; Cheng, G.; Han, J. Beyond the prototype: Divide-and-conquer proxies for few-shot segmentation. arXiv 2022,
arXiv:2204.09903.
44.
Jiang, X.; Zhou, N.; Li, X. Few-shot segmentation of remote sensing images using deep metric learning. IEEE Geosci. Remote Sens.
Lett. 2022,19, 6507405. [CrossRef]
45.
Puthumanaillam, G.; Verma, U. Texture based prototypical network for few-shot semantic segmentation of forest cover: General-
izing for different geographical regions. Neurocomputing 2023,538, 126201. [CrossRef]
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