Strategic Preys Make Acute Predators: Enhancing Camouflaged Object Detectors by Generating Camouflaged Objects
Authors: Chunming He
Kai Li
Yachao Zhang
Yulun Zhang
Chenyu You
Zhenhua Guo
Xiu Li
Martin Danelljan
Fisher Yu
Venue: ICLR 2024 poster (2024)
Keywords: camouflaged object detection
adversarial training
image segmentation
Primary area: representation learning for computer vision, audio, language, and other modalities

Abstract: Camouflaged object detection (COD) is the challenging task of identifying camouflaged objects visually blended into surroundings. Albeit achieving remarkable success, existing COD detectors still struggle to obtain precise results in some challenging cases. To handle this problem, we draw inspiration from the prey-vs-predator game that leads preys to develop better camouflage and predators to acquire more acute vision systems and develop algorithms from both the prey side and the predator side. On the prey side, we propose an adversarial training framework, Camouflageator, which introduces an auxiliary generator to generate more camouflaged objects that are harder for a COD method to detect. Camouflageator trains the generator and detector in an adversarial way such that the enhanced auxiliary generator helps produce a stronger detector. On the predator side, we introduce a novel COD method, called Internal Coherence and Edge Guidance (ICEG), which introduces a camouflaged feature coherence module to excavate the internal coherence of camouflaged objects, striving to obtain more complete segmentation results. Additionally, ICEG proposes a novel edge-guided separated calibration module to remove false predictions to avoid obtaining ambiguous boundaries. Extensive experiments show that ICEG outperforms existing COD detectors and Camouflageator is flexible to improve various COD detectors, including ICEG, which brings state-of-the-art COD performance.

Published as a conference paper at ICLR 2024
STRATEGIC PREYS MAKE ACUTE PREDATORS: ENHANCING CAMOUFLAGED OBJECT DETECTORS BY GENERATING CAMOUFLAGED OBJECTS
Chunming He* Kai Li Yachao Zhang*, Yulun Zhang
Chenyu You', Zhenhua Guo Xíu Li Martin Danelljan Fisher Yu
"Shenzhen International Graduate School, Tsinghua University, "NEC Laboratories America, *Shanghai Jiao Tong University, Yale University, Tiiany Traffic Technology, *ETH Zürich,
ABSTRACT
Camouflaged object detection (COD) the challenging task of identifying cam- ouflaged objects visually blended into surroundings. Albeit achieving remarkable success, existing COD detectors still struggle to obtain precise results some challenging cases. To handle this problem, we draw inspiration from the prey- vs- vs-predator game that leads preys to develop better camouflage and predators to acquire more acute vision systems and develop algorithms from both the prey side and the predator side. On the prey side, we propose an adversarial training framework, Camouftageator, which introduces more camouflaged objects that are harder for a COD method to detect. Cam- ouflageator trains the generator and detector in an adversarial way such that the enhanced auxiliary generator helps produce stronger detector. On the preda- tor side, we introduce a novel COD method, called Internal Coherence and Edge Guidance (ICEG), which introduces a camouflaged feature coherence module to excavate the internal coherence of camouflaged objects, striving to obtain more complete segmentation results. Additionally, ICEG proposes novel adge-guided separated calibration module to remove false predictions to avoid obtaining am- biguous boundaries. Extensive experiments show that ICEG outperforms existing COD detectors and Camouflageator is flexible to improve various COD detectors, including ICEG, which brings state-of-the-an COD performance. The code will be available at https: / / thub. com/ ChunmingHe/ /ato//almmaaaaaaaa
auxiliary generator generate
INTRODUCTION
The never-ending prey-vs- -sreedatt game drives preys develop various escaping strategies. One of the most effective and ubiquitous strategies is camouflage. Preys use camouflage to blend into the surrounding environment, striving to escape hunting from predators. For survival, predators, on the other hand, must develop acute vision systems to decipher camouflage tricks. Camouflaged object detection (COD) is the task that aims to mimic predators' vision systems and localize fore- ground objects that have subtle differences from the background. The intrinsic similarity between camouflaged objects and the backgrounds renders COD a more challenging task than traditional object detection (Liu et al., 2020), and has attracted increasing research attention for its potential applications in medical image analysis (Tang et al., 2023) and species discovery (He et al., 2023b).
Traditional COD solutions (Hou & Li, 2011; Pan et al., 2011) mainly rely manually designed strategies with fixed extractors and thus are constrained by limited discriminability. Benefiting from the powerful feature extraction capacity of convolutional neural network, series of deep learning- based methods have been proposed and have achieved remarkable success on the COD task (He al., 2023c;d; Zhai al., 2022). However, when facing some extreme camouflage scenarios, those methods still struggle to excavate sufficient discriminative cues crucial to precisely localize objects of interest. For example, as shown in the top row of Fig. 1, the state-of of-the-art art COD method, FEDER
"Corresponding Author, The work mainly done when Yulun Zhang was ETH Zürich.

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Published as a conference paper at ICLR 2024
(a) Origin
(b) GT
(c) FEDER
(d) ICEG
(e) ICEG+
Figure 1: Results of FEDER (He et al., 2023c). ICEG, and ICEG+ ICEG+ indicates to optimize ICEG under the Camouflageator framework. Both ICEG and ICEG+ generate more complete results with clearer edges. ICEG+ also exhibits better localization capacity.
(He al., 2023c), cannot even roughly localize the object and thus produce a completely wrong result. Sometimes, even though rough position can be obtained, FEDER still fails to precisely segment the objects, as shown the two remaining rows of Fig. 1. While FEDER manages find the rough regions for the objects, the results are either incomplete (middle row: some key parts of the dog are missing) or ambiguous (bottom row: the boundaries of the frog are not segmented out).
This paper aims to address these limitations. We are inspired by the prey-vs-predator game, where preys develop more deceptive camouflage skills to escape predators, which, turn, pushes the predators to develop more acute vision systems to discern the camouflage tricks. This game leads to ever-strategic preys and ever-acute predators. With this inspiration, we propose to address COD by developing algorithms on both the prey side that generates more deceptive camouflage objects and the predator side that produces complete and precise detection results.
On the prey side, propose novel adversarial training framework, Camouflageator, which gen- crates more camouflaged objects that make it even harder for existing detectors to detect and thus enhance the generalizability of the detectors. Specifically, shown in Fig. Camouflageator com- prises an auxiliary generator and a detector, which could be any existing detector. We adopt an alternative two-phase training mechanism freeze the detector and train the generator to synthesize camouflaged objects aiming to deceive the detector. In Phase II, freeze the generator and train the detector to accurately segment the syn- thesized camouflaged objects. By iteratively alternating Phases I and II, the generator and detector both evolve, helping to obtain better COD results.
train the generator and the detector. In Phase
On the predator side, we present a novel COD detector, termed Internal Coherence and Edge Guid- ance (ICEG), which particularly aims address the issues of incomplete segmentation and ambigu- ous boundaries of existing COD detectors. For incomplete segmentation, we introduce a camou- flaged feature coherence (CFC) module to excavate the internal coherence of camouflaged objects. We first explore the feature correlations using two feature aggregation components, i.e., the intra- layer feature aggregation and the contextual feature aggregation. Then, we propose a camouflaged consistency loss to constrain the internal consistency of camouflaged objects. To eliminate ambigu- ous boundaries, we propose an -ggeeguided separated calibration (ESC) module. ESC separates foreground and background features using attentive masks to decrease uncertainty boundaries and remove false predictions. Besides, ESC leverages edge features to adaptively guide segmentation and reinforce the feature-level edge information achieve the sharp edge for segmentation results. We integrate the Camouflageator framework with ICEG to get ICEG+, which can exhibit better localization capacity (see Fig. 1). Our contributions are summarized as follows:
We design an adversarial training framework, Camouflageator, for the COD task. Camou- flageator employs an auxiliary generator that generates more camouflaged objects that are harder for COD detectors to detect and hence enhances the generalizability of those detec- tors. Camouflageator is flexible and can be integrated with various existing COD detectors.
We propose a new COD detector, ICEG, to address the issues of incomplete segmentation and ambiguous boundaries that existing detectors face. ICEG introduces novel CFC mod-
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Published as a conference paper at ICLR 2024
ule to excavate the internal coherence of camouflaged objects to obtain complete segmen- tation results, and an ESC module to leverage edge information to get precise boundaries.
Experiments on four datasets verify that Camouflageator can promote the performance of various existing COD detectors, ICEG significantly outperforms existing COD detectors, and integrating Camouflageator with ICEG reaches even better results.
2 RELATED WORK
2.1 CAMOUFLAGED OBJECT DETECTION
Traditional methods rely on hand-crafted operators with limited feature discriminability (He al., 2019), failing to handle complex scenarios. A Bayesian-base method (Zhang et al., 2016) was pro- posed to separate the foreground and background regions through camouflage modeling. Learning- based approaches have become mainstream in COD with three main categories: (i) Multi-stage framework: SegMaR (Jia et al., 2022) was the first plug-and-play method to integrate segment, magnify, and reiterate under a multi-stage framework. However, SegMaR has limitations in flexi- bility due to not being end-to-end trainable. (ii) Multi-scale feature aggregation: PreyNet (Zhang et al., 2022) proposed bidirectional bridging interaction module to aggregate cross-layer features with attentive guidance. UGTR (Yang et al., 2021) proposed probabilistic representational model combined with transformers to explicitly address uncertainties. DTAF (Ren et al., 2021) devel- oped multiple texture-aware refinement modules to learn the texture-aware features. Similarly, FGANet (Zhai et al., 2022) designed collaborative local information interaction module to ag- gregate structure context features. (ii) Joint training stralegy: MGL. (Zhai et al., 2021) designed the mutual graph reasoning to model the correlations between the segmentation map and the edge map. BGNet (Sun et al., 2022) presented joint framework for COD to detect the camouflaged candidate and its edge using a cooperative strategy. Analogously, FEDER (He al., 2023c) jointly trained the edge reconstruction task with the COD task and guided the segmentation with the predicted edge.
We improve existing methods three aspects: (i) Camouflageator is the first end-to-end trainable plug-and-play framework for COD, thus ensuring flexibility. (ii) ICEG is the first COD detector to alleviate incomplete segmentation by excavating the internal coherence of camouflaged objects. (iii) Unlike existing edge-based detectors (Sun al., 2022; He et al., 2023c; Xiao et al., 2023), ICEG employs edge information to guide segmentation adaptively under the separated attentive framework.
2.2 ADVERSARIAL TRAINING
Adversarial training is a widely-used solution with many applications, including adversarial at- tack (Zhang al., 2021) and generative adversarial network (GAN) (Deng et al., 2022; Li et al., 2020). Recently, several GAN-based methods have been proposed for the COD task. JCOD (Li al., 2021) introduced GAN-based framework measure the prediction uncertainty. ADENet (Xiang et al., 2021) employed GAN to weigh the contribution of depth for COD. Distinct from those GAN-based methods, our Camouftageator enhances the generalizability of existing COD detectors by generating more camouflaged objects that are harder to detect.
3 METHODOLOGY
When preys develop more deceptive camouflaged skills to escape predators, the predators respond by evolving more acute vision systems discem the camouflage tricks. Drawing inspiration from this prey-vs-predator game, we propose to address COD by developing the Camouflageator and ICEG techniques that mimic preys and predators, respectively, to generate more camouflaged objects and to more accurately detect the camouflaged objects, improving the generalizability of the detector.
3.1 CAMOUFLAGEATOR
Camouflageator is an adversarial training framework that employs an auxiliary generator Ge to synthesize more camouflaged objects that make it even harder for existing detectors D, to detect and thus enhance the generalizability of the detectors. We train G, and D, alternatively in a two- phase adversarial training scheme. Fig. shows the framework.
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Phase I
Altemation
X
X,
D2(x)
Phase II
Figure 2: Architecture of Camouflageator In Phase we fix detector Ds and update generator Ge to synthesize more camouflaged objects to deceive D.. In Phase II, we fix Ge and train the detector D₂ segment the synthesized image.
Training the generator. We fix the detector D, and train the generator Ge generate more decep- tive objects that fail the detector. Given camouflaged image generate
Xg Gx(x),
and expect that xg is more deceptive D2 than x. To achieve this, Xg should be visually consistent (similar in global appearance) with x but simultaneously have those discriminative features crucial for detection hidden reduced.
To encourage visual consistency, we propose to optimize the fidelity loss represented as follows:
L, = (1-))xx, (1 (1-y)0x|²²
where y is the ground truth binary mask and @ denotes element-wise multiplication. Since (1 y) denotes the background mask, this term in essence encourage X, to be similar with x for the background region. We encourage fidelity by preserving only the background rather than the whole image because otherwise, it hinders the generation of camouflaged objects in the foreground.
To hide discriminative features, we optimize the following concealment loss to imitate the bio- camouflage strategies, i.e., internal similarity and edge disruption (Price et al., 2019), as
La = lly XXg- Po 2 + llye @x,- P! l|2,
(3)
where ye is the weighted edge mask dilated by Gaussian function (Jia et al., 2022) to capture richer edge information. P/ is the image-level object prototype which is average of foreground pixels. P. is the image-level edge prototype which is an average of edge pixels specified by Ye- Note that Yer P!. and P/ are all derived from the provided ground truth y and help train the model. This term encourages individual pixels of the foreground region and the edge region of x, to be similar the average values, which has smooth effect and thus hides discriminative features.
Apart from the above concealment loss, we further employ the detector D3 to reinforce the con- cealment effect. The idea is that if xy perfectly deceptive, De tends to detect nothing as the foreground. To this end, we optimize
= LBCCE (D. (Xg) yz)++ (D, (x₂) yz),
(4)
where y. 0 is all-zero mask. ( and I. denote the weighted binary cross-entropy loss (Jadon, 2020) and the weighted cntrrsectioo-vvr-uniiii loss (Rahman & Wang, 2016).
By introducing trade-off parameter X, our overall learning objective to train Ge is follows,
L Cam = L + Ls + Xbel
(5)
Training the detector. In Phase II, we fix the generator Ge and train the detector D, to accurately segment the synthesized camouflaged objects. This the standard COD task and various existing COD detectors can be employed, for example, the simple one we used above,
Cam LECE (D, (xg) y) LYoU (Da (xg)
3.2 ICEG
We further propose ICEG to alleviate incomplete segmentation and eliminate ambiguous boundaries. Given x of size W H. start by using basic encoder F extract set of deep features (fx Y with the resolution of W H
पनन ăテकт and employ ResNet50 (He et al., 2016) the default architecture.
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X
PL
R B
P
Channel-wise Concatenation
P
Mutipliation Summation
Convoution
Fo
Camouflaged Feature Coherence
ESD
CRB
IFA
CFA
ER
RCAB
RCAB
ESC
CFA
Da
PC
ER
ESC
input
CFA
D3
ER
CRB
ESC
ESC
IFA
ASPP
P
output AN
(a) The framework the proposed ICEG.
(b) Details D2 in ESD.
Figure 3: Framework of our ICEG. CRB the Conv-Rel.U-BN structure. We omit the Sigmoid operator in (b) for clarity.
As shown in Fig. 3, we then feed these features, i.c., {fx) to the camouflaged feature coherence (CFC) module and the edge-guided segmentation decoder (ESD) for further processing. Morcover, the last feature map fa. which has rich semantic cues, is fed into atrous spatial pyramid pooling (ASPP) module Ag (Yang et al., 2018) and a 3 3 convolution come3 to generate a coarse result pf: P cone3 con (f4))))). where p shares the same spatial resolution with f4.
3.2. CAMOUFLAGED FEATURE COHERENCE MODULE
To alleviate incomplete segmentation, propose the camouflaged feature coherence (CFC) module to excavate the internal coherence of camouflaged objects. CFC consists of two feature aggregation components, i.e., the intra-layer feature aggregation (IFA) and the contextual feature aggregation (CFA), to explore feature correlations. Besides, CFC intro- duces a camouflaged consistency loss to constrain the internal consistency of camouflaged objects.
1xl Convolation
CRB
C
5x5 Convoluton
Concotenation
Muliglication
CA
SA
Summation
CFA
Up-samplnng
Figure Details of IFA and CFA.
Intra-layer feature aggregation. Fig. 4, IFA seeks the feature correlations by integrating the multi-scale features with different reception fields in single layer, assuring that the aggregated features can capture scale-inven inverianva information. Given fe. a 1 1 convolution convl first applied for channel reduction, followed by two parallel convolutions with different kernel sizes. This process produces the features f and f with varying receptive fields:
f8 conv3(conet (fx))) ff cono5(comel(ft))
where conv5 convolutions, and multiply the outputs to excavate the icale-invariant information:
convolution. Then we combine f and f process them with two parallel
k conv3( conca f.: ; f5)) conc5 (conca(fE: (R))
where conca(.) denote concatenation. We then integrate the three features and process them with CRB block CRB(+), ie.. 3 convolution, ReLU, and batch normalization. By summing with the channel-wise down-sampled feature, the aggregated features {fr fk=1 are formulated as follows:
M convl (fk) + CRB (conca (
Contextual feature aggregation. CFA explores the inter-layer feature correlations by selectively interacting cross-level information with channel attention and spatial attention (Woo et al., 2018),
which ensures the retention of significant coherence. The aggregated feature { is
f SA (CA (cono3 (conca (up 1 (R))))
(10)
where up(-) is up-sampling operation. C' (-) and SA(.) (-) are channel attention and spatial attention. fi = ff. Given 3 { the integrated features { conveyed to the decoder are
S conv1 (concate (Je. f().
(11)
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We employ conv1 for channel integration and fi f7.
Camouflaged consistency loss. To enforce the internal consistency of the camouflaged object, we propose camouflaged consistency loss to enable more compact internal features. To achieve this, one intuitive idea is to decrease the variance of the camouflaged internal features. However, such a constraint can lead to feature collapse, L.e., all extracted features too clustered to be separated, thus diminishing the segmentation capacity. Therefore, apart from the above constraint, we propose an extra requirement to keep the internal and external features as far away as possible. We apply the feature-level consistency loss to the deepest feature f for its abundant semantic information:
Lee = ly @ f4 1- lly @ PP,
(12)
where ya is the down-sampled ground truth mask. Pdd and Py denote the feature-level prototypes of the camouflaged object and the background, respectively.
Discussions. Apart from focusing on feature correlations as in existing detectors (Zhang et al., 2022: He et al., 2023c), we design novel camouflaged consistency loss enhance the internal consistency of camouflaged objects, facilitating complete segmentation.
3.2.2 EDGE-GUIDED SEGMENTATION DECODER
As depicted Fig. 3, edge-guided segmentation decoder (ESD) {D&} comprises an edge recon- struction (ER) module and an edge-guided separated calibration (ESC) module to generate the edge predictions {pi]t=1 and the segmentation results {PE respectively.
Edge reconstruction module. We introduce an ER module to reconstruct the object boundary. Assisted by the edge map pk+1 and the segmentation feature fi++1 from the former decoder, the edge feature fū is presented follows:
f CRB(concaafFk & Pk++ + fhe fi-1))
(13)
where fz (f4) and f cone3( f5 and p are set as zero for initialization. We repeat Ph+1 as a 64-dimension tensor to ensure channel consistency with f in Eq. (13).
Edge-guided separated calibration module. Ambiguous boundary, a common problem in COD, manifests as two phenomena: (1) high degree of uncertainty in the fringes, and (2) the unclear edge of the segmented object. We have observed that the high degree of uncertainty is mainly due to the intrinsic similarity between the camouflaged object and the background. To address this issue, we separate the features from the foreground and the background by introducing the corresponding attentive masks, and design a two-branch network to process the attentive features. This approach helps decrease uncertainty fringes and remove false predictions, including false-positive and false- negative errors. Given the prediction map pf++1 the network is defined as follows:
S = conca( (af _.E"), = cone3(
(14)
where / and J&h are the foreground and the background attentive features, which are formulated
J RCAB ( @ S (vi+1) + Mt)
(15a)
pab RCAB (f% S (R (pi+1)) + +1))
(15b)
where S(.) and R(()) are Sigmoid and reverse operators, iee, element-wise subtraction with 1. RCAB(+) is the residual channel attention block (Zhang al., 2018), which used emphasize those informative channels and high-frequency information.
The second phenomenon, unclear edge, is due to the extracted features giving insufficient impor- tance to edge information. In this case, we explicitly incorporate edge features to guide the seg- mentation process and promote edge prominence. Instead of simply superimposing, we design an adaptive normalization (AN) strategy with edge features to guide the segmentation in a variational manner, which reinforces the feature-level edge information and thus ensures the sharp edge of the segmented object. Given the edge feature fi+1, the attentive features can be acquired by:
fel of ( f = & (RCAB @(((())))))))))))))))))))))))))) +
(16a)
(16b)
where (0/2 m and (o 1 are variational parameters. In AN, {ok, } can be calculated by: )) t4 (CRB,
0 =onv3, (CRB, (f -
(17)

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Published as a conference paper at ICLR 2024
Camouflaged Object Size
(a) Origin
(6) GT
(c) +
(d) ICEG
(6) FGANet
M9 PreyNet
0S Former
(h) SegMaR
Figure 5: Qualitative analysis of ICEG and other four cutting-edge methods. ICEG generates complete results with clearer edges. We also provide results of ICEG+, which is optimized under Camouflageator.
Discussions. Unlike existing adge-guided methods (Sun et al., 2022; He et 2023c) that focus only on edge guidance, we combine edge guidance with foreground/hackgroomn splitting using attentive masks. This integration enables us to decrease uncertainty fringes and remove false predictions along edges, thus achieving the sharp edge for segmentation results.
3.2.3 LOSS FUNCTIONS OF ICEG
Apart from the camouflaged consistency loss Lee- our ICEG is also constraint with the segmentation loss Lg and the edge loss Le to supervise the segmentation results {pt 15 and the reconstructed edge results {pr P Following (Fan et al., 2021), we define Lg as
1
L
2 (LECE ()., +.To ()i+
(18)
For edge supervision, we employ dice loss Laice (Milletari al., 2016) to overcome the extreme imbalance edge maps:
Le Σ
2k-1 I
Liiice (ph, ye).
(19)
kmm1
Therefore, with the assistance of a trade-off parameter B, the total loss is presented as follows:
I T.x + The + BTe..
(20)
3.2.4 ICEG+
To promote the adoption of our Camouflageator, we provide use case and utilize ICEG+ to denote the algorithm that integrates our Camouflageator framework with ICEG. The integration is straight- forward; we only need to replace the detector supervision from Eq. (6) with Eq. (20). In addition, we pre-train ICEG with L, (Eq, (20)) to ensure the training stability. See Sec. 4.1 for more details.
EXPERIMENTS
4.1 EXPERIMENTAL SETUP
Implementation details. All experiments are implemented on PyTroch on two RTX3090 GPUs. For Camouflageator, the generator adopts ResUNet as its backbone. As for ICEG, pre- treitrained ResNet50 (He al., 2016) on ImageNet (Krizhevsky et al., 2017) is employed as the default encoder. We also report the COD results with other encoders, including Res2Net50 (Gao et al., 2019) and Swin Transformer (Liu et al., 2021). Following (Fan et al., 2020), we resize the input image as 352 352 and pre-train ICEG by Adam with momentum terms (0.9, 0.999) for 100 epochs. The batch size is set as 36 and the learning rate is initialized as 0.0001, decreased by 0.1 every 50 epochs. Then the same batch size to further optimize ICEG under the Camouflageator framework for 30 epochs and get ICEG+, where the optimizer Adam with parameters (0.5, 0.99) and the initial learning rate 0.0001, dividing by 10 every 15 epochs. À and B are 0.1.
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Methods
Backhones
CHAMELEON
Fly 1
Eg
So
CAMO
5.
CODTOK
Ei
NGAK
Common Setting:
Single
Iaput
Scale and
Single Stage
SegMak- 1 Uta et al,, 2022)
ResNec50
0.028
0.828
0.944
0.892
0.072
0.772
0.861
0.805
0.035
0.094
0.890
0.813
0.062
0.767
0.885
0.835
PrayNet (2hung ot ol, 2022)
ResNa150
0.127
0.844
0.948
01.855
0.077
0.763
0.854
0.750
0.034
0.715
11.994
0.813
0.477
0.758
0.887
0.838
RGANNe (Zhai ot ol. 2022)
ResNet50
0,030
0.838
01544
0188%
0.070
0.709
0,865
0.800
0.032
0.71%
112594
0,801
01447
0,800
0.891
0.837
FEDER (Ife 2023)
ResNer50
0.028
0.850
0.944
0.897
0.070
0.775
0.870
0.802
0.032
0.715
0.802
0.810
0.946
0.888
0.900
0.542
KCBG (Ouns)
ResNec50
0.027
0.888
0.950
0.899
0.088
0.789
0.879
0.510
0.030
0.747
0,906
0.826
0,044
0.814
0.905
0.549
PtayNet-
ResNe150
0.027
0.544
0.778
0.869
0.308
0108
0.633
0144
PGANece (Ouss
ResNec50
0.029
0.847
0,99
0.09
0.781
0877
0.814
0.030
1211
0:823
01814
0,905
FRDER- (Durs)
ResNat00
0.027
0.85
0.947
0.95
0.088
0.793
0.883
0.200
0.030
0805
0:831
000
0.820
0,910
0.545
ICBG+ (Ouns)
ResNoc50
0.025
0.863
0.952
0,90
0.006
0,805
0.891
0.829
0.028
0.763
0.920
0.843
0.011
0.835
0.922
0.369
SINct V2 (Pan et 2021)
Res2Ne50
0.030
0.816
0.942
0.883
0.070
0.779
0.882
0.822
0.037
0.682
0.587
0.815
0,018
0.792
0.903
0.347
BGNNet (Sin ot oll, 2022)
Ris2Ne501
0.029
0.835
0:944
11895
0.073
0.744
0.870
0.812
0.833
0.714
11001
0.851
0044
0.756
0.907
0.551
ICEG (Ouns)
Re:NNe50
0,025
0.869
0,958
0,908
0.066
0,805
0,903
0.838
0.028
0.752
0.914
0,845
0.042
0,825
0.917
0.867
KCFC+ (Curr)
Re22Na130
0.023
01875
0.500
0,910
07764
0,825
0.912
0,345
0.0025
01770
0.915
0,831
07440
0,844
0,9.5
01378
ICON (Zhuge et til,, 2022)
Swin
0.029
0.848
0.940
0.898
0.058
0.794
0.907
0.
0.840
0,033
0.720
0.588
0.818
0.044
0.817
0.915
0.858
KBG (0uns)
Swin
0.025
0.800
0,959
0.905
0.855
0.926
0.867
0.024
0.930
0.857
0.034
0.855
0.932
KDG (Ouns)
Swin
0.022
0.042
0.861
0.931
0.924
0.862
0.003
0.861
Other Setting: Muttiple Iuput Scales (MIS)
ZecenNet (Pang ot, 2002)
ResNat00
(0.024
0,88
0.943
0,907
0.0666
0.793 0.877 0.830
0.020
0.740
0.888
0.838
10:033
0.814
0.896
ICBG (Ouns)
ResNec50
0.023
0.864
0.957 0.905 0.063
0.802 0.889 0.833 0.028
0.751
0.913
0.840
0.042
0.827
0.911
0.573
Other Setting: Multiple Stuges (MS)
SegMaR-4 Uia et 2022)
ResNa50
0.025
0,855
0.955 0.906
0.071 0.770 0.865 0.815
0.033
0.737 0.806 0.853
0.047
0.793
0.892
0.845
REG 4 (Ours)
RosNec50
0,024
0.870
0 0,907 0.067 0,802 0.884 0.823 0.025
0.755 0,920 0.843 0.043
0,824
0.915
0.500
Table 1: Quantitative comparisons of ICEG and other 13 SOTAS on four benchmarks. SegMaR-1 and 4 are SegMaR at one stage and four stages. "+" indicates optimizing the detector under our Cam- ouflageator framework. Swin and PVT denote Swin Transformer and PVT V2. The best results are marked in bold. For ResNet50 backbone in the common setting, the best two results are in red and blue fonts.
Effect of L, and F,t w/o Lg
Effect strategy
Metrics
w/ Ly
w/ L;
BO w/o Lus
BO w/ L.
AT w/ 15
M L
0.032
0.030
0.028
0.030
0.032
0.028
Fa
0.721
0.750
0.763
0.752
0.722
0.763
Eg t
0,899
0.907
0.920
0.910
0.895
0.920
S.
0.816
0.834
0.843
0.832
0.812
0.843
Table 2: Ablation study of Camouflageator on CODIOK.
(c) w/ L, Figure 6: Synthesized images of the generator r.5 = T.y + Xhat BO and AT are bi-level optimization and adversarial training.
(a) Origin (b) w/o D
(d) w/ L
trained by different losses.
Datasets. We use four COD datasets for evaluation, including CHAMELEON (Skurowski et al., 2018), CAMO (Le et al., 2019), CODIOK (Fan et al., 2021), and NC4K (Lv et al., 2021). CHAMELEON comprises 76 camouflaged images. CAMO contains 1, 1,250 images with 8 categories. CODIOK has 5,066 images with 10 super-classes. NC4K is the largest test set with 4,121 images. Following the common setting (Fan al., 2020; 2021), our training set involves 1,000 images from CAMO and 3,040 images from CODIOK, and our test set integrates the rest from the four datasets.
Metrics. Following previous methods (Fan et al., 2020; 2021), we employ four commonly-used metrics, including mean absolute error (M), adaptive F-measure (Fg), mean E-measure ( (E&), and structure measure (Sa). Note that smaller M or larger F'., Es, Sa signify better performance.
4.2 COMPARISON WITH THE STATE-OF-THE-AREE
Quantitative analysis. We compare our ICEG with 13 state-of-the-ar (SOTA) solutions in three different settings. Apart from the common setting, two other settings (multiple input scales and mul- tiple stages) are also included, where ICEG follows the corresponding practices of ZoomNet (Pang et al., 2022) and SegMaR (Jia et al., 2022). As shown in Table ICEG outperforms the SOTAS by large margin in all settings and backbones. In the common setting, ICEG overall surpasses the second-best methods in 2.1%, 5.2%. 8.3% with the backbone of ResNet50 (FEDER (He al., 2023c)), Res2Net50 (BGNet (Sun et al., 2022)), Swin Transformer (ICON (Zhuge et al., 2022))- Moreover, we also present the results of detectors optimized under Camouflageator. Table Cam- ouflageator generally improves other detectors by 2.8% (PreyNet), 2.2% (FGANet), 2.3% (FEDER), and increases our ICEG by 2.5% (ResNet50), 2.3% (Res2Net50), 1.4% (Swin Transformer), which verifies that our Camouflageator is allug-and-play framework. Results of the compared methods are generated by their provided models with the image size of 352 x 352 for fairness.
Qualitative analysis. Fig. 5 shows that ICEG gets more complete results than existing methods, especially for large objects whose intrinsic correlations are more dispersed (the last row). This substantiates the effectiveness of the our CFC module that excavates the internal coherence of cam-
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Metries
Ablation study of CFC component
Ablation study of ESD compument
Ours
w/o CPC
w/o CTA
FA w/o leg
w/o ESD
w/o SC->FC SC->BC w/o AN
w/o ER
ICRG
M
0.035
0.032
0.031
0.033
0.052
0.032
0.035
0.034
0.032
0.031
0,033
0.034
0.030
0.685
0.728
0.731
0,720
0.722
0.704
0.678
0.688
0.737
0.741
0.715
0.693
0.747
0,866
0.885
0.893
0.883
0.887
0.890
0.864
0.871
0.896
0.902
0.890
0.872
0.906
S
0.808
0.814
0.822
0.812
0.816
0.813
0.802
0.806
0.820
0.822
0.815
0.804
0.826
Table 3: Ablation study of ICEG on CODIOK. "->" is substitution. (a) FA includes both IFA and CFA. L is the first term of Lee in Eq. (12). (b) SC, FC, BC are short for separated (Eq. (16)), foreground (Eq. (16a), background (Eq. (16b)) calibration. Note that "w/o ER" removes edge predictions, thus including "w/o AN".
ouflaged objects for generating more complete prediction maps. Morcover, ICEG gets clearer edges for the predictions than the existing methods, thanks to our ESD module that decreases uncertainty fringes and eliminates unclear edges of the segmented object. Moreover, we can see that ICEG+ ob- tains even better results than ICEG, further verifying the effect of our Camouflageator framework.
4.3 ABLATION STUDY AND ANALYSIS
Validity of Camoufaggetor: We conduct validity analyses for Camouflageator, including our ob- jective function in Eq. (5) and the adversarial training manner. As shown in Fig. 6, the generator trained without Lý produces the images with severe artifacts, while the one trained with fidelity loss L5 only synthesizes visual-appealing images but fails to hide discriminative features. In contrast, the generator trained with our L5 generates high-quality images with more camouflaged objects, ensuring the generalizability of the detector (see Table ). We also compare Camouflageator with the bi-level optimization (BO) framework (He et al., 2023a) to verify the advancement of our adver- sarial manner. BO involves the auxiliary generator and the detector in both the training and testing phases without adversarial losses, i.e. Eqs. (4) and (6). As the concealment loss Let may limit the performance in such an end-to-end manner, we also report the results optimized without Let, namely with only Ly and segmentation loss (Eq. (20)). Table 2 verifies the effect of our adversarial manner.
Effeet of CFC and ESD. The efficacy of CFC modules is verified in Table 3. In Table 3, we examine the impact of the CFC module (in (a)) and investigate the effect of individual components in CFC, including feature aggregation components (in (b), (c), and (d)), and Lec (in (c) and (f)). As shown in Table 3 (e), our camouflaged consistency loss Trce generally improves our detector by 3.4%, which its positive effect. Furthermore, we demonstrate the superiority of T.oe by incorporating T.ce into existing cutting-edge detectors, as detailed in the supplementary materials. Additionally, we present detailed ablation results for ESD in Table 3, where we highlight the benefits of ESD, ESC, separated calibration, adaptive normalization, and the joint strategy to integrate the ER task into the COD task. Moreover, as observed in Table 3, the combination of edge guidance with foreground/backgroun splitting using attentive masks is shown to further boost detection performance. Such discovery can bring insights for the design of edge guidance modules.
CONCLUSION
In this paper, propose to address COD both the prey and predator sides. On the prey side, we introduce a novel adversarial training strategy, Camouflageator, to enhance the generalizability of the detector by generating more camouflaged objects harder for COD detector to detect. On the predator side, we design a novel detector, dubbed ICEG, to address the issues of incomplete segmentation and ambiguous boundaries. In specific, ICEG employs the CFC module to excavate the internal coherence of camouflaged objects and applies the ESD module for edge prominence, thus producing complete and precise detection results. Extensive experiments verify our superiority.
Acknowledgements. This work is supported by National Key R&D Program of China (Grant No. 2020AAA0108303), Shenzhen Science and Technology Project (Grant No. JCYJ20200109 01120200109143044 010099443419999 Shenzhen Stable Supporting Program (WD7C22000082020055530 Shenzhen Key Laboratory of next generation interactive media innovative technology (Grant No. ZDSYS 20210623092000000 The authors express their appreciation to Dr. Fengyang Xiao for her insightful comments, improving the quality of this paper.
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REFERENCES
Lizhen Deng. Chunming He, Guoxia Xu, Hu Zhu, and Hao Wang. Pegan: A noise robust conditional generative adversarial network for one shot learning. IEEE Trans. Intell. Transp. Syst., 23(12): 2022. 3
Deng-Ping Fan, Ge-Peng Ji, Guolei Sun, Ming-Ming Cheng, Jianbing Shen, and Ling Shao. Cam- ouflaged object detection. In CVPR, pp. 2777-2787, 2020. 7,8
Deng-Ping Fan, Ge-Peng Ji, Ming-Ming Cheng, and Ling Shao. Concealed object detection. IEEE Trans. Pattern Anal. Mach. Intell., 2021. 7,
Shang- Hua Gao, Ming-Ming Cheng, Kai Zhao, Xin- Yu Zhang, Ming-Hsuan Yang, and Philip Torr. Res2net: A new multi-scale backbone architecture. IEEE Trans. Pattern Anal. Mach. Intell., 43 (2):652-662, 2019.
Chunming He, Xiaobo Wang, Lizhen Deng, and Guoxia Xu. Image threshold segmentation based glle histogram. In CPSCom, pp. 410-415. IEEE, 2019.
Chunming He, Kai Li, Guoxia Xu, Jiangpeng Yan, Longxiang Tang, Yulun Zhang, Yaowei Wang, and Xiu Li. Hqg-net: Unpaired medical image enhancement with high-quality guidance. IEEE Trans. Neural. Netw. Learn. Syst., 2023a.
Chunming He, Kai Li, Guoxia Xu, Yulun Zhang, Runze Hu, Zhenhua Guo, and Xiu Li. Degradation- resistant unfolding network for heterogeneous image fusion. In ICCV, pp. 1261 12611-1 2023b.
Chunming He, Kai Li, Yachao Zhang, Longxiang Tang, Yulun Zhang, Zhenhua Guo, and Xiu Li. Camouflaged object detection with feature decomposition and edge reconstruction. In CVPR, pp- 22046-22055, 2023c. 1, 2, 3, 6, 8
Chunming He, Kai Li, Yachao Zhang, Guoxia Xu, Longxiang Tang, Yulun Zhang, Zhenhua Guo, and Xiu Li. Weakly-supervised concealed object segmentation with sam-based pseudo labeling and multi-scale feature grouping. NeurlPS, 2023d.
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recog- nition. In CVPR, pp. 770-778, 2016. 7,7
Jianqin Yin Yanbin Han Wendi Hou and Jinping Li. Detection of the mobile object with camouflage color under dynamic background based on optical flow. Procedia Eng., 15:2201-2205, 2011.
Shruti Jadon. A survey of loss functions for semantic segmentation. In CIBCB, pp. 1-7. IEEE, 2020.
Qi Jia, Shuilian Yao, Yu Liu, Xin Fan, Risheng Liu, and Zhongxuan Luo. Segment, magniff and reiterate: Detecting camouflaged objects the hard way. In CVPR, pp. 4713-4722, 2022. 3, 4,
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. Imagenet classification with deep convo- lutional neural networks. Commun. ACM, 60(6):84 90, 2017.
Trung-Nghia Le, Tam V Nguyen, Zhongliang Nie. Minh-Tri Tran, and Akihiro Sugimoto. Anabranch network for camouflaged object segmentation. Comput. Vis. Image Underst, 184: 45-56, 2019. 8
Aixuan Li. Jing Zhang, Yunqiu Lv, Bowen Liu, Tong Zhang, and Yuchao Dai. Uncertainty-awee aware joint salient object and camouflaged object detection. In CVPR, pp. 10071-10081, 2021. 3
Kai Li, Yulun Zhang, Kunpeng Li, and Yun Fu. Adversarial feature hallucination networks for few-shot learning. CVPR, pp. 13470-13479, 2020.
Li Liu, Wanli Ouyang, Xiaogang Wang, Paul Fieguth, Jie Chen, Xinwang Liu, and Matti Pietikäinen. Deep learning for generic object detection: A survey. Int. J. Comput. Vis., 128(2):261-33) 2020.
10

---

Published as a conference paper at ICLR 2024
Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. Swin transformer: Hierarchical vision transformer using shifted windows. ICCV, pp. 10012- 10022, 2021.
Yunqiu Lv, Jing Zhang, Yuchao Dai, Aixuan Li, Bowen Liu, Nick Barnes, and Deng-Ping. Fan. Simultaneously localize, segment and rank the camouflaged objects. In CVPR, pp. 11591-11601, 2021. 8
Fausto Milletari, Nassir Navab, and Seyed-Ahmad Ahmadi. V-net: Fully convolutional neural net- works for volumetric medical image segmentation. 3DV, pp. 565-571. IEEE, 2016.
Yuxin Pan, Yiwang Chen, Qiang Fu, Ping Zhang, and Xin Xu. Study on the camouflaged target detection method based on 3d convexity. Mod. Appl. Sci., 5(4):152, 2011.
Youwei Pang, Xiaoqi Zhao, Tian-Zhu Xiang, Lihe Zhang, and Huchuan Lu. Zoom in and out: A mixed-scale triplet network for camouflaged object detection. In CVPR, pp. 2160-2170, 2022. 8
Natasha Price, Samuel Green, Jolyon Troscianko, Tom Tregenza, and Martin Stevens. Background matching and disruptive coloration as habitat-specific strategies for camouflage. Sci. Rep., 9(1): 1-10, 2019. 4
Md Atiqur Rahman and Yang Wang. Optimizing cnnreection-over-niii in deep neural networks for image segmentation. In ISVC, pp. 234-244. Springer, 2016.
Jingjing Ren, Xiaowei Hu, Lei Zhu, Xuemiao Xu, Yangyang Xu, Weiming Wang, Zijun Deng, and Pheng-Ann Heng. Deep eeture-aware features for camouflaged object detection. IEEE Trans. Circuits Syst. Video Technol., 2021. 3
Przemyslaw Skurowski, Hassan Abdulameer, Blaszczyk, Tomasz Depta, Adam Kornacki, and P Koziet. Animal camouflage analysis: Chameleon database. Unpublished manuscript, 2(6):7, 2018. 8
Yujia Sun, Shuo Wang, Chenglizhao Chen, and Zian-Zhu Xiang. Houndary-guided camouflaged object detection. arXiv preprint arXiv:2207.00794, 2022. 3, 7,8
Longxiang Tang, Kai Li, Chunming He, Yulun Zhang, and Xiu Li. Source-free domain adaptive fundus image segmentation with bass-balanced mean teacher. In MICCAI, pp. Springer, 2023.
Sanghyun Woo, Jongchan Park, Joon- Young Lee, and In So Kweon. Cbam: Convolutional block attention module. ECCV, pp. 3-19, 2018.
Mochu Xiang, Jing Zhang, Yunqiu Lv, Aixuan Li, Yiran Zhong, and Yuchao Dai. Exploring depth contribution for camouflaged object detection. arXiv e-prints, pp. arXiv-2106, 2021. 3
Fengyang Xiao, Pan Zhang, Chumming He, Runze Hu, and Yutao Liu. Concealed object segmenta- tion with hierarchical coherence modeling. In CAAI, pp. 16-27. Springer, 2023,
Fan Yang. Qiang Zhai, Xin Li, Rui Huang, Ao Luo, Hong Cheng, and Deng-Ping Fan. Uncertainty- guided transformer reasoning for camouflaged object detection. In ICCV, pp. 4146-4155, 2021.
Maoke Yang, Kun Yu, Chi Zhang, Zhiwei Li, and Kuiyuan Yang. Denseaspp for semantic segmen- tation in street scenes. CVPR, pp. 3684-3692, 2018.
Qiang Zhai, Xin Li, Fan Yang, Chenglizhao Chen, Hong Cheng, and Deng-Ping Fan. Mutual graph learning for camouflaged object detection. CVPR, pp. 12997-13007, 2021.
Wei Zhai, Yang Cao, Jing Zhang, and Zheng-Jun Zha. Exploring figure-gruund assignment mecha- nism in perceptual organization. In NeurIPS, volume 35, 2022. 1, 8
Chaoning Zhang, Philipp Benz, Chenguo Lin, Adil Karjauv, Jing Wu, and In So Kweon. A survey universal adversarial attack. arXiv preprint arXiv:2103.014:0 2021. 3

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Published as a conference paper at ICLR 2024
Miao Zhang, Shuang Xu, Yongri Piao, Dongxiang Shi, Shusen Lin, and Huchuan Lu. Preynet: Preying on camouflaged objects. ACM MM, pp.
2022. 3, 6, 8
Xiang Zhang, Ce Zhu, Shuai Wang, Yipeng Liu, and Mao Ye. A bayesian approach to camouflaged moving object detection. IEEE Trans. Circuits Syst. Video Technol. 270):2001-21113 2016. 3
Yulun Zhang, Kunpeng Li, Kai Li, Lichen Wang, Bineng Zhong, and Yun Fu. Image super- resolution using very deep residual channel attention networks. In ECCV, pp. 286-301, 2018.
Mingchen Zhuge, Deng-Ping Fan, Nian Liu, Dingwen Zhang, Dong Xu, and Ling Shao. Salient object detection via integrity learning. IEEE Trans. Pattern Anal. Mach. Intell., 2022. 8
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SUPPLEMENTARY MATERIALS FOR STRATEGIC PREYS MAKE ACUTE PREDATORS:
ENHANCING CAMOUFLAGED OBJECT DETECTORS BY GENERATING CAMOUFLAGED OBJECTS
Chunming He Kai Li Yachao Zhang', Yulun Zhang
Chenyu You', Zhenhua Guo Xiu Martin Danelljan" e Fisher Yu 'Shenzhen International Graduate School. Tsinghua University, 2NEC Laboratories America, Shhanghai Jiao Tong University, 'Yae University, TTianyi Traffic Technology. 6ETH Zürich,
CONTENTS
Comparison with the state-of-the-arts
Ablation study and analysis
Limitations and future work
Methods
Backbonos
CHAMEL RON (76 images)
MF
Fot
Es
1
S.1
CAMO (250 imagos)) Pi S..
Fa
CODIOK (2,026 images)
Fa
Fs 8.
NGAK (4,121 images)
FET
Ex 8
Common Setting: Single Inpus Scale and Single Stage
HiNes (Hu al, 2023)
PVT
0.024
0861 0.944 0.967 0.060
0.791
0,892
0.834
0.027 (1700)
0.922
0.847
B.42
0.825
0.11 08858
ICEG (Ours)
PVT
0,022
0.879
0.957
0.913
0.043
0,563
0.933
0,876
0.022 0,805 0,938
0,811
0,030
0,869
0,941 0.890
ILEG (Uurs)
0.913
0.041
0.367
0.877
0,021
0.875
Table SI: Quantitative comparisons of ICEG and HitNet (Hu et al., 2023) on four benchmarks. "+" indicates
optimizing the detector under Camouflageator framework. PVT denotes PVT V2 (Wang al., 2022). The best results are marked in bold.
A COMPARISON WITH THE STATE-OFF-TE--ARTS
Quantitative analysis. We provide the results of ICEG using the PVT V2 backbone (Wang et al., 2022) and compare it with HitNet (AAA12023) (Hu et al., 2023). As illustrated in Table S1, ICEG outperforms HitNet by 9.6%. demonstrating the superiority of our ICEG. Furthermore, we also present the results of ICEG+ and discover that the Camouflageator framework generally improves ICEG performance by 1.0% on average in PVT V2, thus further validating the efficacy of our Cam- ouflageator framework.
B ABLATION STUDY AND ANALYSIS
We conduct the ablation study on the CODIOk dataset.
Ablation study of fidelity loss and concealment loss. In the manuscript, we apply the fidelity loss Ly only to the background rather than the whole image. This is because imposing the fidelity loss on the entire image may hinder the generation of more concealed objects in the foreground. In order verify the effect of our fidelity loss, we conduct an experiment, and the results are shown in Table $2. In this experiment, the fidelity loss in Lel is imposed on the whole image and the rest components in Igl the same those in Lg. where IG Ls XLa- Lgl is defined as:
1e
|X x|| + ALd
"Corresponding Author, The work mainly done when Yulun Zhang was ETH Zürich.

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Published as a conference paper at ICLR 2024
Methods
M +
F9 t
Fig. 1
Sa 1
w/ 5
0.028
0.763
0.920
0.843
- Lel
0.029
0.754
0.917
0.838
g - c2
0.029
0.758
0.915
0.836
w/o Cam
0.030
0.747
0.906
0.826
Table S2: Effect of our fidelity loss and concealment loss, where L L, + ALot. means substitution, "w/" and "w/o" denote with and without. Cam is short for Camoullageator.
Methods
M 1
F8 1
Es
S.
352 x 352
0.030
0.747
0.906
0.826
384 x 384
0.030
0.749
0.911
0.826
512 X 512
0.028
0.755
0.915
0.843
702 x 702
0.027
0.762
0.919
0.848
Table S3: Ablation study of ICEG at different spatial resolutions.
Methods
M +
F3 1
Es 1
S. 1
w/ ASPP
0.030
0.747
0.906
0.826
w/o ASPP
0.030
0.745
0.903
0.824
Table S4: Effect of ASPP.
Methods
M
Fg
Es
Sa
SegMaR-1 1 (Jia et al., 2022)
0.035
0.699
0.890
0.813
BegMaR-1+/-er
0.034
0.716
0.895
0.816
PreyNet (Zhang et al., 2022a)
0.034
0.715
0.894
0.813
PreyNet+Lec.
0.032
0.734
0.898
0.819
FGANet (Zhai al., 2022)
0.032
0.708
0.894
0.803
FGANet+L
0.032
0.721
0.896
0.808
FEDER (He et al., 2023b)
0.032
0.715
0.892
0.810
FEDER+ Tocc
0.031
0.728
0.899
0.817
ICEG-R50 (Ours)
0.030
0.747
0.906 0.826
Table S5: Effect of our camouflaged consistency loss Lee with the backbone of ResNet50, where R50 denotes
ResNet50 (Hc et al., 2016).
Methods
M 1
F8 t
Eg 1
S 1
MGL (Zhai et al., 2021)
0.035
0.680
0.851
0.814
MGL+ESD
0.034
0.687
0.854
0.816
FEDER (He et al., 2023b)
0.032
0.715
0.892
0.810
FEDER+ESD
0.031
0.722
0.896
0.814
ICEG-R50 (Ours)
0.030
0.747
0.906
0.826
BG-Net (Sun et al., 2022)
0.033
0.714
0.901
0.831
BG-Net+ESD
0.032
0.725
0.904
0.835
ICEG-R2N (Ours)
0.028
0.752
0.914
0.845
Table S6: Comparison with other edge-based methods with the same backbone, where R50 and R2N denote ResNet50 (He al., 2016) and Res2Net50 (Gao al., 2019), respectively.
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Methods
M +
F3 t
EΦ t
S. 1
(0,0,0,0)
0.034
0.688
0.871
0.806
(1,0,0,0)
0.032
0.710
0.884
0.814
(1,1,0,0)
0.031
0.723
0.892
0.817
(1,1,1,0)
0.030
0.734
0.902
0.822
(1,1.1,1)
0.030
0.747
0.906
0.826
Table S7: Effect of the ESC module.
Methods
M 4
Fp 1
Es 1
S
Methods
M 4
Fg
Eg 1
R50 w/o Cam
0,030
0.747
0,906
0.826
PVT w/o Cam
0.022
0.805
0.938
0.871
R50 w/o pretrain
0.029
0.755
0.913
0.835
PVT w/o pretrain
0.022
0.807
0.940
0.870
R50 w/ pretrain
0.028
0.763
0.920
0.843
PVT w/ pretrain
0.021
0.811
0.939
0.873
(a) Effect the pre-trained model with ResNet50.
(b) Effect of the pre-trained model with PVT.
Table S8: Explorations of the Camouflageator framework, where "Cam" is short for Camouflageator. (a) R50
means ResNet50 (He al., 2016). The best epoch of "R50 w/o pretrain" is 67 (CODIOK), (b) PVT denotes
PVT V2 (Wang al., 2022). The best epoch of "PVT pretrain" help Camouflageator to better improve segmentation performance.
(COD10K). The pre-traines models
Methods
M 4
F's
Eg
S. 1
w/o discriminator
0.028
0.763
0.920
0.843
w/ discriminator
0.029
0.758
0.915
0.840
Table S9: Effect of the discriminator.
Additionally, we also illustrate the feasibility of corrupting the edge information in weighted man- ner. Specifically, we substitute the unweighted edge mask y: for the weighted edge mask ye utilized in Lie and reformulate the concealment loss, termed T.G2:
Lg² Ly + A 2 X.
11
(2)
+ Allyy xx, -
where P!l is the image-level edge prototype which an average of edge pixels specified by y!-
As shown in Table S2, the detector trained with Lgl or I also achieves performance gains, but as significant those obtained when trained with Lç- These results comprehensively verify the advancement of our fidelity loss L, and concealment loss Let-
the improvements
Performance different resolutions. To investigate the impact of the image resolution ICEG, we evaluate the segmentation results of our ICEG with various image resolutions. As illustrated in Table S3, we observe that performance improves the image resolution increases. This discovery motivates us to incorporate image super-resolution techniques into the COD task in future research.
Effect of ASPP. We employ ASPP to enlarge the receptive field, fuse the multi-context information. and generate a coarse segmentation result to guide the subsequent segmentation, whose effectiveness demonstrated in Table $4.
Effect of camouflaged consistency loss. Apart from focusing on feature correlations as in existing multi-scale frameworks, we design a novel camouflaged consistency loss Lce address incomplete segmentation by enhancing the internal consistency of camouflaged objects. As shown in Table S5, we incorporate Loe to existing multi-scale frameworks (Jia et al., 2022; Zhang et al., 2022a; Zhai et al., 2022) and find improved performance, thereby confirming the superiority of Loe-
Comparisons with other edge-based methods. As mentioned the Related work, ICEG stands apart from existing edge-based detectors (Zhai et al., 2021; Sun et al., 2022) by utilizing edge guid- ance that is updated with the segmentation module from coarse to fine to adaptively guide segmen- tation under the separated attentive framework. The superiority of our edge- based ICEG is verified in Table S6, where we replace the edge modules in MGL (Zhai et al., 2021) and BG-Net (Sun et al., 2022) with ESD and observe evident improvement in segmentation results.
Effect of ESC. ESC is employed to explicitly guide the segmentation with edge information. In Ta- ble S7, conduct experiment to verify the superiority of ESC, where (* * * ***) means whether
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(a) Origin Figure $1: Failure of ICEG.
(b) GT
(c) ICEG
ESC is retained in DE. e.g., (1,0,0,0) indicates ESC is retained in D4 but not in the other three de- coders. As shown in Table S7, ESC can promote better segmentation results, and the current version in ICEG, namely (1,1,1,1), achieves the optimal performance.
Discussions of Camouflageator. In this discussion, we explore two problems related to Camou- flageator, namely "How effectively employ the Camouftageator framework?" and "How deal with the domain shift problem caused by synthesized images?". To address the first problem, we conduct ablations for detectors (see Tables S8a and S8b), and find that using pre-trained detector can improve the segmentation performance. In Tables S8a and S8b, we train the detector with no pre-trained model by 130 epochs and report the best results within the 130 epochs. As reported in Tables S8a and S8b, ICEG with the pretrain model achieves better results in the backbones of ResNet50 and PVT V2 and thus indicates the value of the pre-trained model. This lies in the fact that a naive detector cannot effectively learn from the synthesized challenging images and fails to give valuable feedback the generator, thereby affecting training stability and segmentation results.
Regarding to the second problem, the purpose of the Camouflagector framework is to strengthen the generalizability of the detector, not to ensure that the distribution of the synthesized images is completely unbiased from the original camouflaged data. Focusing too much on the latter may even cause a degradation of segmentation performance, which verified by Table S9, where we introduce classification-base discriminator (Deng et al., 2022) for the generator to enforce the "realism" of synthesized images. However, this degrades performance because the generator needs to balance the requirements of the discriminator while also accommodating the detector. Additionally, already ensured the quality of the generated camouflaged images by employing the fidelity loss L constrain the visually consistent between the generated image and the original image, and only tne-tune the pre-trained detector by 30 epochs to enhance the generalizability of the detector without overfitting the synthesized concealed images.
have
C LIMITATIONS AND FUTURE WORK
Camouflageator. As shown in Table S8b, owing to the strong feature extraction capacity of the transformer, Camouflageator can only bring limited improvements for ICEG with the backbone of PVT. In future work, we plan to propose more powerful generators to cater to those transformer- based backbones by further reinforcing their generalizability. Additionally, we also consider ex- tending our framework to other tasks such as saliency object detection, industrial defect detection, medical image segmentation, etc.
ICEG. As depicted in Fig. $1, ICEG may fail to accurately identify those concealed objects that share very similar structural information to the background. To handle this, we consider extending our method in the following aspects cope with this problem: (1) We intend to employ coarse- to-fine framework, wherein we can first detect those candidates and then finetune the coarse maps to ensure accurate segmentation. (2) aim to enhance the feature extraction capacity make sure that we can better filter out those valuable objects. Furthermore, we will consider utilizing our seg- mentor extract more semantic-level information (Zhang et al., 2021; Xiao et al., 2024; Pu et al.,
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2023a; Xiao et al., 2023) and improve the generalizability to empower more fields (Fang & Han, 2023; Ma et al., 2023; Tang et al., 2023). Additionally, we plan to employ self-excavation strategies to mine the valuable information (Zhang et al., 2022b; Xu et al., 2022) or incorporating more pow- erful architectures, e.g., dynamic networks (Pu et al., 2023e; Wang et al., 2021), transformer (Guo et al., 2022; Pu et al., 2023b; Ma et al., 2022), and diffusion model (Ma et al., 2023; Li al., 2023; Guo et al., 2023b), with more strategic pretrain networks (Xu et al., 2023b; Ni et al., 2023; Guo al., 2023a). (3) We consider ensuring the generalizability of our segmentor even under extremely degraded scenarios. First, we think it would be desirable to employ image quality assessment tech- niques (Hu al., 2021a;b) to distinguish hard samples, which helps us focus more on those valuable samples. Furthermore, fusing multi-modality data, e.g. infrared and visible image fusion, can greatly improve the accuracy of downstream tasks (Xu et al., 2023a; et al., 2022). Therefore, multi-modality image fusion can be good solution to address camouflaged object detection. More- over, how to train a segmentor to directly cope with those degraded scenarios, including low-light scenarios and hazy environments, is valuable direction that is worth exploring. In this casc, the future design of our segmentor should also refer to low-quality image restoration techniques (He et al., 2023a; Ye et al., 2023; 2022) to improve the global illumination and texture details.
REFERENCES
Lizhen Deng, Chunming He, Guoxia Xu, Hu Zhu, and Hao Wang. Pogan: A noise robust conditional generative adversarial network for one shot learning. IEEE Trans. Intell. Transp. Syst., 23(12): 25249-25258, 2022. 4
Cheng-Yo Yu Fang and Xian-Feng Han. Joint geometric-semantic driven character line drawing gener- ation. In ICMR, pp. 226-233, 2023. 5
Shang-Huu Hua Gao, Bing-Ming Cheng, Kai Zhao, Xin- Yu Zhang, Ming-Hsuan Yang, and Philip Torr. Res2net: A new multi-scale backbone architecture. IEEE Trans. Pattern Anal. Mach. Intell., 43 (2):652-662, 2019.
Jiayi Guo, Chaoqun Du, Jiangshan Wang, Huijuan Huang, Pengfei Wan, and Gao Huang. Assessing single image in reference-guided image synthesis. In AAAI, volume 36, pp. 753-761, 2022. 5
Jiayi Guo, Chaofei Wang, You Wu, Eric Zhang, Kai Wang, Xingqian Xu, Humphrey Shi, Gao Huang, and Shiji Song. Zero-shot generative model adaptation via ipage-specific prompt learn- ing. In Proceedings of the IEEF/CVF Conference on Computer Vision and Pattern Recognition, pp. 11494-11503, 2023a.
Jiayi Guo, Xingqian Xu, Yifan Pu, Zanlin Ni, Chaofei Wang, Manushree Vasu, Shiji Song, Gao Huang, and Humphrey Shi. Smooth diffusion: Crafting smooth latent spaces in diffusion models. arXiv preprint 2rXiv:2312.04411 2023b. 5
Chunming He, Chengyu Fang Yulun Zhang, Kai Li, Longxiang Tang. Chenyu You, Fengyang Xiao, Zhenhua Guo, and Xiu Li. Reti-diff: Illumination degradation image restoration with retinex- based latent diffusion model. arXiv preprint arXiv:2311.1 1638, 2023a. 5
Chunming He, Kai Li, Yachao Zhang, Longxiang Tang, Yulun Zhang, Zhenhua Guo, and Xiu Li. Camouflaged object detection with feature decomposition and edge reconstruction. CVPR, pp. 22046-22053 2023b. 2
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recog- nition. In CVPR, pp. 770-778, 2016. 2, 3
Runze Hu, Yutao Liu, Ke Gu, Xiongkuo Min, and Guangtao Zhai. Toward no-reference quality metric for camera-captured images. IEEE Cybernetics, 2021a. 5
Runze Hu, Yutao Liu, Zhanyu Wang, and Xiu Li. Blind quality assessment of night-time image. Displays, 69:102045, 2021b. 5
Xiaobin Hu, Shuo Wang, Xuebin Qin, Hang Dai, Wengi Ren, Donghao Luo, Ying Tai, and Ling Shao. High-resolution iterative feedback network for camouflaged object detection. In AAAI, volume 37, pp. 881-888 2023. 1
5

---

Published as a conference paper at ICLR 2024
Qi Jia, Shuilian Yao, Yu Liu, Xin Fan, Risheng Liu, and Zhongxuan Luo. Segment, magnify and reiterate: Detecting camouflaged objects the hard way. In CVPR, pp. 4713 2022. 2, 3
Mingye Ju, Chunming He, Juping Liu, Bin Kang, Jian Su, and Dengyin Zhang. Ivf-net: An infrared and visible data fusion deep network for traffic object enhancement in intelligent transportation systems. JEEE Transactions on Intelligent Transporiation Systems, (21):1222--134, 2022. 5
Ronghui Li, Junfan Zhao, Yachao Zhang, Mingyang Su, Zeping Ren, Han Zhang, Yansong Tang, and Xiu Li. Finedance: A fine-grane fine-grained choreography dataset for 3d full body dance generation. In ICCV, pp. 10234-102220 2023. 5
Yue Ma. Tianyu Yang, Yin Shan, and Xiu Li. Simvtp: Simple video text pre-training with masked autoencoders. arXiv preprint arXiv:2212.034900, 2022.
Yue Ma, Yingqing He, Xiaodong Cun, Xintao Wang, Ying Shan, Xiu Li, and Qifeng Chen. Fol- low your pose: Pose-guided text-to-video generation using mose-free videos. arXiv preprint arXiv:2304.01 186, 2023. 5
Zanlin Ni, Yulin Wang, Jiangwei Yu, Haojun Jiang, Yu Cao, and Gao Huang. Deep incubation: Training large models by sriide-and-conueerng In ICCV, 2023.
Yifan Pu, Yizeng Han, Yulin Wang, Junlan Feng, Chao Deng, and Gao Huang. Fine- line-grained recog- nition with learnable semantic data augmentation. MxKi:223990033999 2023a. 4
Yifan Pu, Weicong Liang, Yiduo Hao, Yuhui Yuan, Yukang Yang, Chao Zhang, Han Hu, and Gao Huang. Renk-detr for high quality object detection. In NeurlPS, 2023b. 5
Yifan Pu, Yiru Wang, Zhuofan Xia, Yizeng Han, Yulin Wang, Weihao Gan, Zidong Wang, Shiji Song, and Gao Huang. Adaptive rotated convolution for rotated object detection. In ICCV, 2023c. 5
Yujia Sun, Shuo Wang. Chenglizhao Chen, and Tian-Zhi Xiang. Honndry--guddes camouflaged object detection. arXiv preprint arXiv:2207.00794 2022. 2,3
Longxiang Tang, Kai Li, Chunming He, Yulun Zhang, and Xiu Li. Consistency regularization for generalizable source-free domain adaptation. In ICCV, pp. 4323-4333 2023. 5
Wenhai Wang, Enze Xie, Xiang Li, Deng- Fan, Kaitao Song. Ding Liang, Tong Lu, Ping Luo, and Ling Shao. Pvt v2: Improved baselines with pyramid vision transformer. Comput. Vis. Media, 8(3):415-424, 2022. 3,3
Yulin Wang. Zanlin Ni, Shiji Song, Le Yang, and Gao Huang. Revisiting locally supervised learning: an alternative to end-to-end training. In ICLR, 2021. 5
Yicheng Xiao, Yue Ma, Shuyan Li, Hantao Zhou, Ran Liao, and Xiu Li. Semanticac: semantics- assisted framework for audio classification. In ICASSP, pp. 1-5. IEEE, 2023. 5
Yicheng Xiao, Zhuoyan Luo, Yong Liu, Yue Ma, Hengwei Bian, Yatai Ji, Yujiu Yang, and Xiu Li. Bridging the gap: A unified video comprchension framework for moment retrieval and highlight detection. CVPR, 2024. 4
Guoxia Xu, Chunming He, Hao Wang, Hu Zhu, and Weiping Ding. Dm-fusion: Deep model-driven network for heterogeneous image fusion. IEEE Transactions Neural Networks and Learning Systems, 2023a. 5
Lei Xu, Hui Wu, Chumming He, Jun Wang, Changqing Zhang, Feiping Nie, and Lei Chen. Multi- modal sequence learning for alzheimer's disease progression prediction with incomplete variable- length longitudinal data. Medical Image Analysis, 82:102643, 2022. ISSN 1361-8415. doi:
https://doi.org// org. 10t6//.media........ 2022.102642.1 5
Zunnan Xu, Zhihong Chen, Yong Zhang, Yibing Song, Xiang Wan, and Guanbin Li. Bridging vision and language encoders: Pfrfmeter-efficient tuning for referring image segmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision pp. 17503-17512, 2023b.

---

Published as a conference paper at ICLR 2024
Tian Ye, Yunchen Zhang, Mingchao Jiang, Liang Chen, Yun Liu, Sixiang Chen, and Erkang Chen. Perceiving and modeling density for image dehazing. In ECCV, pp. 130-145. Springer, 2022. 5
Tian Ye, Sixiang Chen, Jinbin Bai, Jun Shi, Chenghao Xue, Jingxia Jiang, Junjie Yin, Erkang Chen, and Yun Liu. Adverse weather removal with codebook priors. In ICCV, pp. 12653-12664, 2023.
Qiang Zhai, Xin Li, Fan Yang. Chenglizhao Chen, Hong Cheng, and Deng- Fan. Mutual graph learning for camouflaged object detection. CVPR, pp. 12997-13007, 2021. 2,
Wei Zhai, Yang Cao, Jing Zhang, and Zheng-Jun Zha. Exploring figure-groung assignment mecha- nism in perceptual organization. In NewrIPS, volume 35, 2022. 2,
Miao Zhang, Shuang Xu, Yongri Piao, Dongxiang Shi, Shusen Lin, and Huchuan Lu, Preynet:
Preying on camouflaged objects. In ACM MM, pp. 5323-5332, 2022a. 3,
Yachao Zhang, Yanyun Qu, Yuan Xie, Zonghao Li, Shanshan Zheng, and Cuihua Li. Perturbed self- distillation: Weakly supervised large-scale point cloud semantic segmentation. In Proceedings of the IEEF/CVF International Conference on Computer Vision, pp. 15520-15528 2021. 4
Yachao Zhang, Yuan Xie, Cuihua Li, Zongze Wu, and Yanyun Qu. Learning all- in collaborative mul- tiview binary representation for clustering. IEEE Transactions on Neural Networks and Learning Systems, pp. 1-14, 2022b. doi: 10. 1111//NNNNSS (009/TNNLS.2022232220002 5


Reviews:
[{"review_id": "CqJXckppk9", "reviewer": "Reviewer_fgyc", "summary": "This paper is inspired by the prey-vs-predator game, proposing novel algorithms from both the prey side and the predator side. An adversarial framework is proposed to generate more challenging camouflaged objects. While the ICEG aims to detect these objects. Experiments show that this paper achieves the best results.", "questions": "No further question.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The motivation is great. This paper takes advantage of adversarial training to generate harder camouflaged objects. This idea is novel. \n2. Their proposed Camouflageator is a plug-and-play framework. It is effective for ICEG and three existing methods.\n3. The experiments are very detailed and thorough.", "weaknesses": "To some extent, the ICEG solved the incomplete segmentation. However, it fails to identify the foreground object when concealed objects have very similar structural information to the background. \n\nMany false positive detection results are outputted.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "ORar3TLXht", "reviewer": "Reviewer_VfAG", "summary": "The present paper proposes an innovative framework for the COD task based on adversarial training. While adversarial training has been established as an effective method in domains such as image classification, image/video deblur and object detection, this study successfully implemented it for the COD task. The efficacy of the proposed method is demonstrated through extensive experimentation on COD benchmarks.", "questions": "N/A", "limitations": "", "rating": 5, "confidence": 2, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. The paper generally well written. \n2. The idea of using an adversarial generator to create more difficult training examples addresses an important weakness (lack of diversity) in existing COD datasets.\n3. ICEG's modules for improving segmentation completeness and boundary precision tackle the limitations of prior work. The design choices are well-motivated.\n4. Extensive experiments demonstrate SOTA results on multiple datasets. Ablations verify the contributions of individual components.", "weaknesses": "1. The paper presents two significant contributions: firstly, the introduction of an adversarial training framework for COD tasks, and secondly, the implementation of ICEG to overcome the limitations of incomplete segmentation and ambiguous boundaries for camouflaged objects. However, the two contributions seem disconnected, and they appear to be independent approaches aimed at enhancing network segmentation without a clear connection between them. Consequently, the primary storyline of the work is missing. The authors must establish the relationship between the two contributions and articulate how they work together to achieve the overall objective of the paper.\n\n2. The adversarial training framework, which represents the primary contribution of this paper, is not a novel concept. Although it has yet to be implemented in COD tasks, the fundamental idea and implementation are similar to other tasks, such as image deblurring. It would be beneficial to underscore the unique difficulties and differences between COD and other tasks if the implementation is non-trivial.\n\n3. Missing detailed descriptions for Figure 3. It lacks detailed descriptions and makes it challenging to comprehend the main idea presented. Given the complexity of the figure, it is important to provide proper explanations to ensure a comprehensive understanding. I think that the explanation of the network design may come across as a technical report and, as such, should be included in the supplementary material. In the body of the paper, I would like to see clear and concise motivations and rationales for each design, rather than technical details. The author should enhance the descriptions in Figure 3 to ensure they are easily understandable for readers.\n\n4. It is imperative to ascertain the efficacy of the proposed adversarial training framework for other state-of-the-art (SOTA) networks by conducting ablations of the framework with alternative methods. This endeavor would furnish evidence to substantiate the generalizability of the proposed approach and its applicability to other networks.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "HO5RtfMyjG", "reviewer": "Reviewer_bgwW", "summary": "The paper proposed to address COD on both the prey and predator sides. On the prey side, it introduced a novel adversarial training strategy, Camouflageator, to enhance the generalizability of the detector by generating more camouflaged objects harder for a COD detector to detect. On the predator side, it designed a novel detector, dubbed ICEG, to address the issues of incomplete segmentation and ambiguous boundaries.", "questions": "Please address my major concerns as listed in the Weaknesses section.", "limitations": "", "rating": 5, "confidence": 1, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "++ An adversarial training framework, Camouflageator, for the COD task to employ an auxiliary generator that generates more camouflaged objects that are harder for COD detectors to detect and hence enhances the generalizability of those detectors. Camouflageator is flexible and can be integrated with various existing COD detectors.\n\n++ A new COD detector, ICEG, to address the issues of incomplete segmentation and ambiguous boundaries that existing detectors face. ICEG introduces a novel CFC module to excavate the internal coherence of camouflaged objects to obtain complete segmen-tation results, and an ESC module to leverage edge information to get precise boundaries.", "weaknesses": "-- “CamDiff: Camouflage Image Augmentation via Diffusion Model” also utilizes the generation of camouflaged images to help with COD and should be included to the comparison.\n\n-- There exist many COD methods are not mentioned in  RELATED WORK: CAMOUFLAGED OBJECT DETECTION part, like:\nMutual graph learning for camouflaged object detection\nA Bayesian approach to camouflaged moving object detection\nUncertainty-guided transformer reasoning for camouflaged object detection\nDeep texture-aware features for camouflaged object detection\nAlthough some papers are cited, they need to be reflected in the related work\n\n-- Why does Equation 4 use two losses for segmentation.\n\n-- In Sec 3.2.1 Camouflaged consistency loss, what is the difference between the ideas in the paper and the contrastive loss\n\n-- In Eq 17, are mu and sigma the same?\n-- Why backbone chose resnet50 and not the transformer structure", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "xyJ9hR3vLV", "reviewer": "Reviewer_pcqB", "summary": "This paper presents an adversarial training framework, Camouflageator, generating more yet more challenging camouflaged objects to enhance generalizability. Additionally, it proposes a new COD method, ICEG to tackle the incomplete segmentation and ambiguous boundary limitations of existing methods.", "questions": "- Please provide related statistics about the synthesized camouflaged objects? For example, the number of categories and the camouflaged property (e.g., color, contrasts, intensity comparisons of foreground and background).\n- Please supplement the experiments regarding the Camouflageator generalizability with Res2Net50 and Swin based methods?\n- Please provide the visualization maps of module ablations, and consider leveraging CAM-family (activation maps) for better illustration.", "limitations": "", "rating": 8, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The inspiration from the prey-vs-predator game provides an interesting and effective COD method.\n- Designed a 2-phase training pipeline combining the generation and detection.\n- Proposed a Camouflageator with flexibility and generalizability that could be applied to various existing COD detectors.\n- Proposed an ICEG detector including a CFC module and an ESC module which leads to a better segmentation quality.\n- Achieved the state-of-the-art on four benchmarks.", "weaknesses": "- The paper mentions the proposed Camouflageator generates \"more camouflaged objects that are harder for COD detectors\" many times. However, the paper does not include a detailed description of the quantity or quality of the synthesized camouflaged objects.\n- Though the generalizability of Camouflageator has been validated with ResNet50 based COD methods, the paper does not conduct experiments with other COD methods employing Res2Net50 and Swin as backbones. \n- Though Fig 5 indicates the ICEG has better segmentation quality in terms of completeness and boundaries compared to other methods, they do not provide the comparison (visualization) of how each module improves the backbone.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (poster)