RUN: Reversible Unfolding Network for Concealed Object Segmentation
Authors: Chunming He
Rihan Zhang
Fengyang Xiao
Chengyu Fang
Longxiang Tang
Yulun Zhang
Linghe Kong
Deng-Ping Fan
Kai Li
Sina Farsiu
Venue: ICML 2025 poster (2025)
Keywords: Concealed object segmentation
Deep unfolding network
Reversible modeling strategy
Camouflaged object detection
Primary area: applications->computer_vision
TL;DR: The first unfolding-based concealed object segmentation algorithms.

Abstract: Concealed object segmentation (COS) is a challenging problem that focuses on identifying objects that are visually blended into their background. Existing methods often employ reversible strategies to concentrate on uncertain regions but only focus on the mask level, overlooking the valuable of the RGB domain. To address this, we propose a Reversible Unfolding Network (RUN) in this paper. RUN formulates the COS task as a foreground-background separation process and incorporates an extra residual sparsity constraint to minimize segmentation uncertainties. The optimization solution of the proposed model is unfolded into a multistage network, allowing the original fixed parameters to become learnable. Each stage of RUN consists of two reversible modules: the Segmentation-Oriented Foreground Separation (SOFS) module and the Reconstruction-Oriented Background Extraction (ROBE) module. SOFS applies the reversible strategy at the mask level and introduces Reversible State Space to capture non-local information. ROBE extends this to the RGB domain, employing a reconstruction network to address conflicting foreground and background regions identified as distortion-prone areas, which arise from their separate estimation by independent modules. As the stages progress, RUN gradually facilitates reversible modeling of foreground and background in both the mask and RGB domains, reducing false-positive and false-negative regions. Extensive experiments demonstrate the superior performance of RUN and underscore the promise of unfolding-based frameworks for COS and other high-level vision tasks. Code is available at https://github.com/ChunmingHe/RUN.

RUN: Reversible Unfolding Network for Concealed Object Segmentation
Chumming He' Rihan Zhang' Fengyang Xiao Chengyu Fang², Longxiang Tang
Yulun Zhang? Linghe Kong²
Deng-Ping Fan Kai Li Sina Farsiu
(a) Origin
(b)
(c) GLLE
(d) SINct
(c) FEDER
SINct+
FEDER+
(h) RUN (Ours)
COD
PIS
MTOS
TOD
0.75
0.88
COD1OK
ETIS
CORN
GDD
0.017f
0,0071
0.011
0.95
0.87
0.86
0.008t
0.73
SEN
0.72
0.85
0.94
0.71
0.84
0.85
FEDER
FSEL
FacusCHf
RÚN
ColnNit LSSNet PolyPPT RÚN
FFM
DSCNet
TAU
RUN (90g)
RFENRE
GDNE
Ghost
RUN
(EVeent) (REVSO) (POCV 34 (1an)
(TM/71) (MОCСАTS)) (AIRS))
(D00N5 (00333) (NP(3))
(BCAP2S) (TPMMI):TPPMPM
(0me)
Figure 1. Results of existing COS methods, including GLLE (Wang et al., 2019), SINet (Fan et al., 2020a), and FEDER (He et al., 2023b).
Our RUN demonstrates superiority accurately segmenting concealed objects (in the top section) and achieves leading places across multiple COS tasks (in the bottom section): camouflaged object detection (COD), polyp image segmentation (PIS), medical tubular object segmentation (MTOS),
transparent object detection (TOD). the top section, concealed object masks are highlighted blue and pink, overlaid on the original images for visual clarity. FEDER+ and SINet+ indicate integrating FEDER and SINet with our RUN framework. For the bottom section, we employ commonly used datasets, methods, and metries relevant to task.
Abstract
by incorporating an extra residual sparsity con- straint to minimize segmentation uncertainties. The iterative optimization steps of the proposed model are then unfolded into multistage net- work, with each step corresponding to stage. Each stage of RUN consists of two reversible mod- ules: the Coneeenttaiio-Orrnntet Foreground Sep- aration (SOFS) module and the Reconstruction- Oriented Background Extraction (ROBE) module. SOFS applies the reversible strategy the mask level and introduces Reversible State Space cap- ture non-local information. ROBE extends this to the RGB domain, employing reconstruction network to address conflicting foreground and background regions identified as distortion-prone areas, which arise from their separate estimation by independent modules. As the stages progress, RUN gradually facilitates reversible modeling of foreground and background in both the mask and RGB domains, directing the network's 's at- tention to uncertain regions and mitigating false- positive and false-negative results. Extensive
Existing concealed object segmentation (COS) methods frequently utilize reversible strate- gies, e.g., background reversible attention and bogground-backgrounn reversible calibration, address uncertain regions. However, these ap- proaches typically restricted to the mask domain, leaving the potential of the RGB do- main underexplored. To address this, we pro- pose the Reversible Unfolding Network (RUN), which applies reversible strategies across both mask and RGB domains through a theoretically grounded framework, enabling accurate segmen- tation. RUN first formulates novel COS model
'Duee University. PSIGS, Tsinghua University. 'Shanghai Jiao Tong University. *Nankai Institute of Advanced Re- search (SHENZHEN-FUTIAN) 'Meta. Correspondence to: Sina Farsiu <sina. farsiu@uuke..duus. Fengyang Xiao zfengyang.xiaot duke.edu>.
Proceedings of the 4nnd International Conference Machine Learning. Vancouver, Canada. PMLR 267, 2025. Copyright 2025 by the author(s).

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RUN: Reversible Unfolding Network for Conccaled Object Segmentation
experiments verify the superiority of RUN and highlight the potential of unfolding-hased frame- works for COS. Code is available at https: / //github... com/ Cht//inhuminieee /RUN.
combining them, to achieve more accurate segmentation. The COS task is formulated as a foreground-hackgree sep- aration process, and new segmentation model is developed by incorporating residual sparsity constraint to reduce seg- mentation uncertainties. The iterative optimization steps of the model-based solution are then unfolded into a multi- stage network, with each step corresponding to a stage. Each stage comprises two reversible modules: the Segmentation- Oriented Foreground Separation (SOFS) module and the Background Extraction (ROBE) module. By integrating optimization solutions with deep networks, our RUN framework achieves an effective balance between interpretability and generalizability.
1. Introduction
Concealed object segmentation (COS) aims to segment ob- jects that are visually blended with their surroundings. It serves an umbrella term with various applications, includ- ing camouflaged object detection (He et al., 2024b), polyp image segmentation (He et al., 2025b), and transparent ob- ject detection (Xiao et al., 2023), among others.
Iiconstruction-Oriinnn
We implement the reversible strategy within the mask do- main in SOFS and within the RGB domain in ROBE. In SOFS, the mask is initially updated strictly according to the optimization solution. Subsequently, the Reversible State Space (RSS) module, recognized for its strong capacity to extract non-local information, is employed to refine the seg- mentation mask using the previously estimated mask and background. In ROBE, the process begins with a mathemati- cal update of the background. A lightweight network is then used to reconstruct the entire image, while simultaneously refining the background, based on the estimated foreground and background. Since the estimation of foreground and background regions is performed by distinct modules, their assessments of concealed content can differ (see Fig. 2 (d) and (f)) Regions of conflicting judgments are identified as distortion-pret prone areas during the reconstruction process (see Fig. 2 (g) and (h)). This auxiliary reconstruction task, which targets the resolution of such distortions, effectively directs the network's 's attention to challenging regions where distinguishing between foreground and background is par- ticularly difficult, improving segmentation performance.
COS a challenging problem due to the intrinsic similarity between the object and its background. Traditional methods address this challenge by relying on manually designed mod- els with hand-crafted feature extractors tailored to subtle differences in textures, intensities, and colors (Wang al., 2019). While offering clear interpretability, they often struc- ture in complex scenarios. Deep learning advances COS by leveraging its strong generalization capabilities, driven by powerful feature extraction mechanisms. Early learning- based approaches, such as SINet (Fan et al., 2020a), pri- marily focus on foreground regions for segmentation, often overlooking discriminative cues in the background, leading to suboptimal performance (see Fig. 1). Recent algorithms, such as FEDER (He et al., 2023b), have sought to refine seg- mentation masks by reversibly modeling both foreground and background regions at the mask-level.
Reversible modeling enhances the network's capacity to extract subtle discriminative cues by directing attention to uncertain regions- pixels with values that are neither I nor O-thus improving segmentation results. However, current methods restrict the application of reversible strategies to the mask level, leaving the potential of the RGB domain underexplored. Such information can assist in identifying discriminative cues and enhancing segmentation quality. As shown in Fig. 2 (d) and (e), when reversibly separating the image into foreground and background regions based on the mask, the uncertainty regions in the mask tend to manifest as color distortion in the RGB space. Addressing these translates to a more precise separation of the fore- ground and background. In this case, two seemingly inde- segmentation and distortion restora- tion-share the same optimization goal. Existing research has shown that jointly optimizing such tasks helps guide the network toward an optimal solution (Xu et al., 2023).
As the stages progress, RUN incrementally facilitates re- versible modeling of foreground and background in both the mask and RGB domains. This approach effectively focuses the network on uncertain regions, reducing false-positive and felse-ne outcomes. Notably, RUN exhibits high flexibility, allowing seamless integration with existing meth- ods to achieve further performance enhancements.
Our contributions are summarized as follows:
(I) We propose RUN for the COS task. To the best of our knowledge, this represents the first application of deep unfolding network to address the COS problem, thereby balancing interpretability and generalizability.
pendent
(2) RUN proposes a novel COS model designed to reduce segmentation uncertainties and introduces SOFS and ROBE modules to integrate model-based optimization solutions with deep networks. By enabling reversible modeling of foreground and background across both the mask and RGB
To achieve this, we first introduce deep unfolding network termed the Reversible Unfolding Network (RUN) for COS. RUN established& a thcoretical foundation to reversibly in- tegrate the two aforementioned tasks, rather than directly
2

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RUN: Reversible Unfolding Network for Conccaled Object Segmentation
(a) (b) GT
(c) M
(c) CM (e) C/I-M)
(f) B (g) C.++B (h) [aH]]]] 0
( C
(K) (()-0)
Figure 2. Correspondence between uncertainties the mask domain and distortions the RGB domain. C is the concealed image and B is the estimated background, which has conflicting judgments of concealed regions with the mask M. This conflict leads to Distortion-prome areas in their direct combination (g). Panel (h) illustrates the difference between (g) and the original image (a). However, after refinement through the network B(+), the reconstructed image Ć becomes much closer to the original image, accompanied by
refined background B with improved accuracy. This refined background is passed to the next stage to further facilitate segmentation.
domains, RUN directs the network's ' focus to uncertain re- gions, reducing false-positive and false-negative outcomes.
ground region F and background region B, expressed as
C= F= B.
(1)
(3) Experiments on five COS tasks, as well salient ob- ject detection, validate the superiority of our RUN method. Besides, its plug-and-play structure underscores the effec- tiveness and adaptability of anfolding-basedd frameworks for the COS task and other high-level vision tasks.
Based on Eq. (1), the foreground and background regions can be obtained by optimizing the objective function:
(F _|IC- - F Blß + (F((F) + Ao(B), (2)
where |1-12 is a 22-norm for smooth. (FF) and 6(B) are reg- ularization terms for F and B with two trade-off parameters 3 and X. To suit the segmentation task, we directly focus on the mask M, where = C- M, and is dot product. Substituting into Eq. (2), the objective function becomes |C- 1 2
2. Related Works
Concealed object segmentation. Deep learning methods have advanced COS (Xiao et al., 2024). Among them, those using reversible techniques to segment from foreground and background aspects are gaining attention. PraNet (Fan et al., 2020b) introduced a parallel structure with reversible atten- tion to enhance segmentation. FEDER (He et al., 2023b) used foreground and background masks to identify con- cealed objects with edge assistance. BiRefNet (Zheng et al., 2024) proposed a reconstruction module to refine the mask with gradient information. However, they only focus on the mask level, leaving the RGB domain underexplored. Hence, we propose the first deep unfolding network, RUN, for COS. RUN proposes a novel COS model and introduces SOFS and ROBE. By integrating optimization solutions with deep networks, RUN enables reversible modeling across mask and RGB domains, improving segmentation accuracy.
L (M, where (M) and are the regularization term and trade-off parameter for M. Due to the intrinsic ambiguity of fore- ground objects in concealed images and the diverse nature of their backgrounds, manually defining regularization terms for M and B can be challenging. To address this, we utilize deep neural networks to implicitly learn these constraints in a data-driven manner. Beyond the two intrinsic regular- ization terms above, we introduce an extra residual sparsity constraint S(.) to further refine segmentation and minimize uncertainties. This leads to the final objective function:
C-M B ( Ao (3)
L (M, = 2 [|C - C. - B1 + (M)
(4)
+ + + as w (w-(M- M))
Deep unfolding network. The deep unfolding network, well-established technique in low-level vision, integrates model-based and learning-based approaches (He et al., 2023a; Fang et al., 2024), offering enhanced interpretabil- ity compared to pure learning- based methods. However, its application in high-level vision remains underexplored, primarily due to the lack of explicit intrinsic models for high-level vision tasks. In this paper, we introduce deep unfolding network, RUN, in COS and formulate a novel COS model. RUN achieves more accurate segmentation re- sults by integrating optimization-base solutions with deep networks, verifying its potential for advancing COS.
where controls the weight of the sparsity constraint, S()) represents an f--nomm, M is the refined mask after uncertainty-removal mapping, and w is the attention map. For a pixel located at (i, M and w can be defined as
0.1
M, E [0.1, 0.4).
M(r))
0.9
M, E (0.6.0.9).
M, Otherwise.
(5)
O M, E [0.4,0.6].
((.)) 1 Otherwise.
This design encourages the generation of segmentation masks with high certainty. Following the practice of (He et al., 2024a), pixels with values in the ambiguous range [0.4, 0.6] are excluded from further consideration, while ex- treme values for M are set to 0.1 and 0.9 instead of 0 and I to allow greater flexibility for optimization.
3. Methodology
3.1. COS Model
A concealed image C can be decomposed into its fore-
3

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RUN: Reversible Unfolding Network for Conccaled Object Segmentation
Slage
Sagg22 2
Sage K
Sage
Segmentation
SOTS MOSLMS
SOTS MHHAA
50TS WiSSMi
SOTS MAHLANES
Scaed
ROBE Polille
ROBE
ROBE
ROBE
M
E.
n
The framework of the proposed RUN
(i) oint optimization.
M MM M C)
M.,
MM.
C)
B,= (C,M B..)
C,
M
SOFS
M
0,(+]
B C
M
M
0.0+)
RSS
E
B
B2
B
(b) ROBE
M
VSS
M
Matrix Product 5 Square
All-Ones Matrix
C
(c) R.S
Matrix Inverse
O
Dot P Product
O 5 Summation
B
C
VSS
E
VSS
Visual State 5 Space
3x3 Convalution
(iii Details of SOFS. ROBE, and RSS the k* stage.
Figure 3. Framework of our RUN. The network connections in MM-) and B-) are derived strictly based mathematical principles. thus enhancing interpretability. For clarity, we replace certain redundant details with Qa. Qb. and Qe and present M(-) according to Eq. (18). Panel (ii) illustrates that the joint optimization of image segmentation and reconstruction tasks facilitates the network's 's progression toward an optimal solution.
3.2. RUN
thresholding for flexibility problem-solving Following the practice of (Goldstein, 1977). we approximate S(w**
3.2.1. 1. MODEL OPTIMIZATION
(M MR)) at the 1 iteration (for simplicity, we let R Wk (M M&)), expressed as follows:
We utilize the proximal gradient algorithm (Fang et al., 2024) to optimize Eq. (4), ultimately deriving the optimal mask M and background B*
S (R) =
(10)
(M", B arg min L. (M, B).
(6)
where
M.B
Ls |R-R
S(R, Rk-
|
VS(R Cs. (11)
Css
The optimization process involves alternating updates of the two variables over iterations. Here we take the hh stage
Ls
where Ls is the Lipschitz constant. VS(Ra-1) is the Lips- chitz continuous gradient function ((R with Cs. positive constant that can be omitted in optimization. Sub- stituting into Eq. (9), obtain the following equations:
(1 k S K) to present the alternative solution process.
Optimizing Mg. First, the optimization function parti- tioned update the foreground mask Mc:
[| - CIC C- BB l2 (M)
M
| 2
C. - B 111
M
M
M arg min M 2
2
(7)
aLs 2
1
(12)
+ aas (W&. [M (M-M))
R R VS (
Ls
The solution comprises two terms: the gradient descent term and the proximal term. To address the proximal term, we introduce an auxiliary variable Mg, resulting in:
Unlike 4nnorm methods, Eq. (12) can be solved directly by equating its derivative to zero. The closed-form solution is
M, (Q.=) (Q.M, C CBR-1+0Q +Q)), (13)
M = prox, (B Mr).
(8)
where Q. = C² + L + μI, I an all-ones matrix,
where Bg is initialized as zero. Having gotten Eqs. (7) and (8), M2 can be solved by optimizing:
Qu LLS μI, Qc aLsw (w, Qa)
aw, VS M Qa). and Qa = W M
M, 2
1 -| C. M BB- 12 JM - 11-
Optimizing B The optimization function of B is
+ +aS (W& (MM-Ma)
B& arg min
B |C C. C. - -BI + (14)
Same as the optimization rule for Mj. the gradient descent term and the proximal term are correspondingly defined as:
where M, is also initialized as zero. Note that Wa. and M2 are constructed based on M For the term S(we. (M- M,)), we employ a Taylor expansion rather than soft
B& 1 CC C. M - B1B 2 1 B 1 (15)
>

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RUN: Reversible Unfolding Network for Conccaled Object Segmentation
B prox (B&, M
(16)
criminative features, achieving accurate segmentation.
The closed-form solution of B₁ can be acquired similarly: B₁ = ((1+ +) (AB,-1 + C ..
ROBE. In ROBE, the calculation of BN relies on B(+), simi- lar to Eq. (15) but with the fixed parameters made learnable:
(17)
B& = 1 (Bx-1, C)2
(20)
3.2.2. DEEP UNFOLDING MECHANISM
-(1++)
CC
We unfold the iterative optimization steps of the model- based solution into multi-stage network, termed Reversible Unfolding Network (RUN), with each step corresponding to stage. As shown in Fig. 3, each stage has two reversible modules: the Segmentation -rrrinttttio--rieteeee Foreground Separa- tion (SOFS) and Reconstruction Orientte Background Ex- traction (ROBE) modules. The detailed variable update process for each stage is outlined in Algorithm S1.
This is essentially a dynamic fusion of the previously es- timated background and the reversed foreground derived in the current stage. To refine Bg, we propose simple U- shaped network (Xu et al., 2023) with three layers, denoted as B(-). However, as shown in Fig. 2, since separate modules estimate the foreground and background, their interpreta- tions of the concealed content may differ. Hence, regions with conflicting interpretations are identified as distortion- prone areas in reconstruction. To address this, the network also generates a reconstructed result Ck. formulated as:
SOFS. SOFS, derived from Eqs. (8) and (13), utilizes M() and M()) to compute the optimization solution M and the refined mask M at each stage, respectively. Given B Mg-1, and M we define M2 as follows:
B₁, C₁ = B
(21)
C₁ is designed to be consistent with the concealed image, thereby mitigating distortions. This alignment fosters con- sistent judgments between SOFS and ROBE for foreground- background separation, improving segmentation accuracy. As the stages progress, RUN incrementally facilitates re- versible modeling of the foreground and background in both the mask and RGB domains. This iterative process directs the network's 's attention to regions of uncertainty, reducing false-positive and false-negative outcomes. Hence, RUN ensures robust and accurate segmentation performance.
M₁ MM (Bx
M, C)2
(18) Eq. (18) retains the same formulation as Eq. (13), but all originally fixed parameters, including V.(+), are relaxed to be learnable, improving the model's generalizability.
= =(Qa) (Q.M CBB-BB++++2 Qc).
To refine the initial mask Mg. we introduce the Reversible State Space (RSS) module RSS(-), which has a robust capacity for non-local information extraction. The RSS module incorporates two Visual State Space (VSS) (+) modules (Liu al., 2024) with distinct perception fields. The VSS with a small perception field locally refines uncer- tain regions along the edges from the foreground perspec- tive, while the VSS with a large perception field globally identifies missed segmented regions from the background perspective. This dual-perception mechanism ensures both accurate and comprehensive segmentation results. Follow- ing (He et al., 2023b), we also integrate an auxiliary edge output Eg to further enhance segmentation performance. Consequently, the computation of M, and E, is defined as:
Loss function. term and reconstruction term. We adopt the training strat- egy from FEDER (He et al., 2023b) for the segmentation part. A mean square error loss governs the reconstruction component. The overall loss function is defined as
The loss function comprises a segmentation
1 L Σ 2k-k LL (M,, (M. (M,, GT,) (22)
+ L GT,) + - C
where K is the number of stages. L'C is the weighted binary cross-entropy loss, Loou is the weighted intersection- over-union loss, and Ldice is the dice loss. GT, and GTa are the ground truth of the segmentation mask and edge.
Mg, E2 (B M, C) = RB M,, C)2
= =VS.3(()S M, + ECC)
(19)
+ (ES)) (B.((((()(BB-1/)---) (B,-I/C)+
where conv3 is 3 x 3 convolution. V*)((+) and ())
4. Experiments
have small and large perception fields, incorporating convo- lutions with varying kernel sizes. For brevity, we omit the detailed description of VSS. Unlike low-level vision tasks, segmentation tasks, particularly inherently complex COS, strongly depend semantic information. It is challenging to extract this fully using a shallow network. To address this, we adopt the common practice of leveraging deep features E(C),, extracted from an encoder (default: ResNet50 (He et al., 2016)). Rather than directly processing the concealed image, this approach enables the extraction of subtle dis-
Implementation details. We implement our method using PyTorch two RTX4090 GPUs In line with (Fan et al., 2020a), we incorporate deep features from encoder-shaped networks into our framework. All images are resized to 352 x 352 for the training and testing phases. During train- ing, we use the Adam optimizer with momentum parameters (0.9, 0.999). The batch size set to 36, and the initial learn- ing rate is configured 0.0001, which is reduced by 0.1 every 80 epochs. The stage number K is set as 4. Additional
5

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RUN: Reversible Unfolding Network for Conecaled Object Segmentation
Table 1. Results on camouflaged object detection. SegMaR-1/-4 are SegMaR with one or four stages. The hest results are marked in bold. For the ResNet50 backbone in the common setting, the best two results are in red and blue fonts.
CHAMELEON
CAMO
CODIOK FgT
NC4K
Methods
Backbones
1 E, 1 S.
L
Fg 1
E. 1
1
M 4
Es 1
8,
M 1
Fa E S.,
Common Setting: Single Input Scale and Single Stage
SINct et al., 2020d)
ResNet50
0.034 0.823
0.936
0.872
0.092
0.712
0.804
0.745
0.043
0.667
0.864
0.776
0.058
0.768
0.871
0.808
LSR (L.v et al, 2021)
ResNet50
0.030
0.835 0.935
0.890
0.080
0.756
0.838
0.787
0.037
0.699
0,880
0.804
0.048
0.802
0.890
0.834
FEDER (He et al., 2023b)
ResNet50
0.028
0.850 0,944
0.892
0.070
0.775
0.870
0.802
0.032
0.715
0.892
0.810
0.046
0.808
0.900
0.842
FGANet (Zhai et al., 2023)
ResNer50
0.030
0.838 0.945
0.891
0.070
0.769
0.865
0.800
0.032
0.708
0.894
0.803
0.047
0.800
0.891
0.837
FocusDiff (Zhao al., 2024)
ResNet50
0.028
0.843 0,938
0.890
0.069
0.883
0.812
0.730
0.897
0.820
0.044
0.810
0,902
0.850
FSEL (Sun al., 2024)
ResNet50
0.029
0,847 0,941
0.893
0.069
0.779
0.881
0.816
0.032
0.722
0,891
0.822
0,045
0.807
0.901
0.847
RUN (Ours)
ResNot50
0.027
0.855
0,952
0.895
0.070
0.781
0.868
0.806
0,030
0.747
0,903
0.827
0.042
0.824
0.908
0.851
BSA-Net (Zhu ot al., 2022)
Res2Net50
0.027
0.851
0,946
0.895
0.079
0.768
0.851
0.796
0.034
0.723
0.891
0,818
0.048
0.805
0.897
0.841
FEDER (He et al., 2023b)
Res2Ne150
0.026
0.856
0,947
0,903
0.066
0.807
0.897
0.836
0.029
0.748
0.911
0.844
0.042
0.824
0.913
0.862
RUN (Ours)
Res2Ne150
0.024
0.879
0.956
0.907
0.066
0.815
0.905
0.843
0.028
0.764
0,914
0.849
0.041
0.830
0.917
0.859
HitNet (Hu et al., 2023)
PVT V2
0.024
0,861
0,944
0.907
0.060
0.791
0.892
0.834
0.027
0.790
0,922
0.847
0.042
0.825
0.911
0.858
CamoFocus (Khan et al., 2024)
PVT V2
0.023
0.869
0.953
0.906
0.044
0.861
0.924
0.870
0.022
0.818
0.931
0.868
0.031
0.862
0.932
0.886
RUN (Ours)
PVT V2
0.021 0.877
0,958
0.916
0.045
0.861
0.934
0.877
0.021
0.810
0.941
0.878
0.030
0.868
0.940
0.892
Other Setting:
Multiple Input Scales (MIS)
ZoomNet (Pang et al., 2022)
ResNet50
0.024
0.858
0,943
0,902
0.066
0.792
0.877
0.820
0.029
0.740
0.888
0.838
0.043
0.814
0.896
0.853
FEDER (He al., 2023b) RUN (Ours)
ResNet50
0.023
0.869
0.959 0.906
0.064
0.801
0.893
0.827
0.028
0.756
0.913
0.837
0.041
0.832
0.915
0.859
ResNet50
0.022
0.878
0.967
0.911
0.064
0.807
0.902
0.832
0.027
0.772
0.920
0.843
0,040
0,836
0.922
0,868
Other Setting:
Multiple Stages (MS)
SeyMaR-4 4 (Tia et al., 2022)
ResNet50
0.025
0,855
0,955
0.906
0.071
0.779
0.865
0.815
0.033
0.737
0.896
0.833
0.047
0.793
0.892
0.845
FEDER-4 (He et al., 20236)
ResNet50
0.025
0.874
0,964
0,907
0.067
0.809
0.886
0.822
0.028
0.752
0.917
0.851
0.042
0.827
0.917
0.863
RUN 4 (Ours)
ResNei50
0.024
0.889
0.968
0.913
0.815
0.829
0.926
0.041
0.833
0.925
0.870
Table 2. Results polyp image segmentation.
Table 4. Results on transparent object detection.
CVC-ColonDB mloU
ETTS
GDD
GSD
Methods
Methods
mDize
S.1
miDice 1 mloU 1 S. 1
mloU
F3
mloU
P3
ProNet (Fan al., 2020h)
0.709
0.640
0.819
0.628
0.567
0.794
GDNet (Mei et al., 2020)
0.876
0.937
0.063
0.790
0.869
0.069
CASCADE (Rahman, 2023)
0,809
0.731
0.867
0,781
0.706
0.853
EBLNet (Ile et al., 2021)
0.870
0.922
0.064
0,817
0,878
0.059
PolypPVT (Dong et al., 2023)
0.808
0.727
0.865
0,787
0.706
0.871
RFENet (Fan et 2025b)
0.886
0.938
0.057
0.865
0.931
0.048
ColnNet (Jain et al., 2023)
0.797
0.729
0.875
0.759
0.690
0,859
IEBAF (Han at. 2024)
0.887
0.944
0.056
0.861
0.926
0.049
LSSNet (Wang et al., 2024)
0.741
0.867
0.779
0.701
0,867
GhostingNet (Yan et al., 2024)
0.893
0.943
0.054
0.838
0.904
0.055
RUN (Ours)
0.822
0.742
0.880
0.788
0.709
0.878
RUN (Ours)
0.895
0.952
0.051
0.866
0.938
0.043
Table 3. Results on medical tubular object segmentation.
approaches across all three backbones: ResNet50 (He et al., 2016), Res2Net50 (Gao et al., 2019), and PVT V2 (Wang et al., 2022). This superior performance on four datasets, particularly on the largest dataset, CODIOK, and the largest testing dataset, NC4K, underscores the robustness and gen- eralization capabilities of our RUN framework. Further- more, in the MIS and MS settings, our RUN adheres to the evaluation protocols of FEDER (He et al., 2023b) and delivers improved results over existing methods. As illus- trated in Fig. 4, our method generates more complete and accurate segmentation maps. This is attributed to our jointly reversible modeling at both the mask and RGB levels.
DRIVE
CORN
Methods
mDice
AUC 1 SEN
mDice 1
AUC
SEN
CS2-Net (Mou al., 2021)
0.795
0.983
0.822
0.607
0.960
0.817
DSCNet (Qi et al., 2023)
0.805
0.955
0.830
0.618
0.964
0.856
SGAT (Lin et al., 2023)
0.806
0.953
0.832
0.639
0
0.853
TAU (Gupta et al., 2024)
0.798
0.977
0.825
0.643
0.949
0.859
FFM (Huang et al., 2025)
0.791
0.972
0.830
0.647
0.952
0.835
RUN (Ours)
0.812
0.985
0.845
0.652
0.962
0.870
parameters inherited from traditional methods are optimized in learnable manner with random initialization.
4.1. Comparative Evaluation
We conduct experiments on various COS tasks and compare our performance with SOTA methods using standard met- rics. Details on datasets and metrics are in Appendix A. For fairness, all results are evaluated with consistent task- specific evaluation tools. Except for COD, other tasks have few publicly open-sourced methods, limiting quantitative analysis.
Medical concealed object segmentation. We conducted experiments on two medical COS tasks, including polyp image segmentation (CVC-Colonil and ETIS datasets) and medical tubular object segmentation (DRIVE and CORN datasets). Considering that recent SOTAS commonly use Transformer-based encoders, we adopt PVT V2 as our de- fault encoder. As shown in Tables 2 and 3, our method achieves top performance across three tasks. Furthermore, the results in Fig. 4 confirm the effect of our approach in segmenting small polyps and fine vessels and nerves.
Camouflaged object detection. As shown in Table 1, our method achieves SOTA performance across all three set- tings. In the common setting, it outperforms competing
6

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RUN: Reversible Unfolding Network for Conccaled Object Segmentation
Crigr
ordn
Oryn
GT
GT
GT
RUN
RUN
RUN
POWPPVT Proflet
CS2Nes
GDV
DSCNet
EBL
(a) Camuufaaged bjjct
(b) Polyo Image Segmentation
Object Segmentation
(d) Transparent Object
Figure 4. Visual comparison on COD, PIS, MTOS, and TOD tasks.
Table 5. Ablation study the COD task CODIOK.
Effect
Effect of ROBE
Fixed Learnable
RUN
Metries
C + C(C)
w/o RSS
w/o VSS
w/o prior M;
w/o prior Bx-1
w/o Egg
(+ - B(+)
B2(+) - B(+)
w/o C2:
(Ours)
MU
0.053
0.034
0.031
0.032
0,031
0.031
0.030
0,030
0,031
0.032
0.030
0.617
0.710
0.740
0.728
0.735
0.733
0.746
0.749
0.736
0.731
0.747
Eg
0.825
0.887
0.897
0.891
0.893
0.905
0.906
0.896
0.903
S.
0.746
0.805
0.825
0.820
0.823
0.826
0.824
0.822
0.827
Table 6. Effect of our COS model. CM, PM, OS, and DL are shorts for conventional Table 7. Analysis of stage number K. We have sur-
model. proposed model. optimization solution, and deep learning.
passed most compared methods when K 2.
Metries PM+OS CMI+DL. CM2+DL CM3+DL. CM4+DL CM5+DL. PM+DL (Ours)
Metrics
K
2 K (Ours) K
M 0.062
0.032
0.031
0.031
0.031
0.032
0.030
M
0,033
0.031
0.030
0.030
0,030
Fa 0.573
0.729
0.735
0.733
0.740
0.735
0.747
0.715
0.727
0.747
0.749
0.751
Es 0.802
0.899
0.896
0.895
0.898
0.892
0.903
0,885
0.893
0.903
0,905
0.905
0.733
0.823
0.824
0.821
0.823
0.823
0.827
S,
0.803
0.812
0.827
0.826
0.830
Transparent object detection. Accurately segmenting transparent objects is crucial for autonomous driving. As demonstrated in Table 4 and Fig. 4, our RUN surpasses existing methods on two datasets, providing more precise segmentation of transparent objects compared to other ap- proaches. These results highlight our potential to contribute to the advancement of autonomous driving.
sufficient for background extraction and image reconstruc- tion. Furthermore, when the reconstructed output C2 is removed, our RUN also produces suboptimal results.
Other configurations in RUN. We validate the effect of var- ious configurations in RUN. As shown in Table 5, allowing originally fixed parameters to be learnable enhances perfor- mance. Furthermore, we compare our model with CMI to CM5, as detailed in Table 6. CMI corresponds to Eq. (3). CM2 also employs Eq. (4) for optimization but applies the f-normm the first term and employs soft thresholding (He et al., 2023a) to solve S(.). CM3-CM5 represent ablated versions of the refined mask and weighted map in Eq. (4): CM3 removes the weighted map w. CM4 modifies the refined range pixel values from [0.1, (0.6, 0.9) to [0.1, (0.3)&((.))))))))) 0.9], with corresponding adjustments the weighted map. CM5 extends the range include all pixel values, assigning 0.5 to the foreground region. As shown in Table 6, our approach achieves superior performance across traditional solutions and learning- unfolding strategies. Moreover, as verified in Table 7, we analyze the optimal stage number for our method. balance perfor- mance and computational efficiency, we set K 4. Under this configuration, the feature maps from the last four layers of the encoder progressively sent to the four stages, with features from deeper layers transferred first.
4.2. Ablation Study
We conduct ablation studies on CODIOK of the COD task.
Effect of SOFS. As presented Table 5, replacing the deep features E(C) with the concealed image results in perfor- mance decline, highlighting the critical role of incorporating deep features into the DUN-based framework. Additionally, we evaluate the impact of the state space-based based structure by removing the RSS and VSS modules. The effectiveness of our reversible strategy is further validated by excluding the foreground prior M, and the background prior B Finally, we confirm the utility of integrating the auxiliary edge output, contributing to performance improvements.
Effect of ROBE. As shown in Table 5, when replacing (-+) with other large-scal scale networks, i.e., the CNN-based network B (-) (Xu et al., 2023) and Transformer-based net- work B-(+) (He et al., 2025a), we observe no significant performance gains. This suggests simple network is

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RUN: Reversible Unfolding Network for Conccaled Object Segmentation
Table 8. Results on small object images (1,084 images).
Table 9. Results on multi-object images ( (186 images).
Metries
SegMaR
FEDER
FGANet FocusDiff
FSEL
RUN (Ours)
Metrics
SegMaR
FEDER
FGANet FocusDiff' PSEL RUN (Ours)
MU 4
0.049
0.044
0.044
0.042
0,043
0.040
M
0.076
0.068
0,065
0.062
0.062
0.060
Pa
0.605
0.646
0.642
0.670
0.668
0.682
Fat 1
0,436
0.480
0.481
0.500
0.496
0.505
F t
0.831
0.855
0.852
0.859
0.847
0,866
Es f
0.797
0.813
0.810
0.818
0.820
0.827
S,
0.765
0.777
0.776
0.781
0.776
0,789
S, 1
0,695
0.709
0.709
0.716
0.717
0.730
0.74
0.90
0.72
0.70
0.38
0.68
0.66
0.56
0.64
PRICK
PAEL
FIDEE
FSEL
0.62
KUN
RUN
(a) Origin
(b) GT
FEDER (d) FEDER-R (e) +EDER+
0.0
Concentration of Haze
0.1
0.2
0.3
0.4
0.5
0.0
Concentration of Haze
0.3
0.2
0.3 0.4
0.5
Figure 6. Potential applications of RUN. The concealed object masks are highlighted in red and overlaid on the original images.
Figure 5, Performance in degraded COS scenarios.
Table 10. Potential of RUN to serve as refiner, where "FEDER-
Table 11. Generalization of RUN, where "FEDER+" " means inte-
R" means refining FEDER's 's results with RUN.
grating our framework into FEDER.
Metries FEDER
FEDER-R R FGANet FGANet-R
FSEL
FSEL-R
Mctrics
FEDER
FEDER+
FGANet
FGANct+
FSEL
FSEL+
LL
0.032
0.031
0.032
0.032
0.032
0.031
M 4
0.032
0.031
0.032
0.031
0.032
0.030
Fat 0.715
0.721
0.708
0.716
0.722
0.725
Fa
0.715
0.726
0.708
0.730
0.722
0.738
Ex f
0.892
0.897
0,894
0.897
0.891
0.890
Es t
0.892
0.902
0,894
0.901
0.891
0,905
S. 0.810
0.812
0.803
0.805
0.822
0.825
S. 0.810
0.816
0.803
0.808
0.822
0.830
4.3. Further Analysis, Applications, and Meanings
ods without requiring retraining. Furthermore, we incorpo- rate the core structures of existing methods into our RUN framework, followed by retraining the entire network. This integration yields even greater improvements, demonstrat- ing that the unfolding framework can function as a plug- and-play solution to enhance the performance of existing methods. For example, shown in Fig. 6, we observe that while error predictions from FEDER influence FEDER-R, FEDER+ demonstrates better resilience to such errors.
Performance on small objects or multiple objects. Small objects and multiple objects are challenging for lacking dis- criminative cues. To evaluate our performance on the two conditions, we filtered images from CODIOK that satisfy these criteria, resulting in 1, 084 images having concealed objects smaller than quarter of the entire image and 186 images with multiple concealed objects. As shown in Ta- bles 8 and 9, while the performance of all methods declines, our approach consistently outperforms the competition.
Meanings of our framework. Beyond introducing the deep unfolding network to high-level vision for the first time and enabling reversible modeling across both mask and RGB domains, the proposed RUN framework offers the potential to establish a unified vision strutegy. By com- bining image segmentation and image reconstruction, our RUN introduces a novel approach to unifying low-level and high-level vision. Unlike existing strategies, such as livle optimization (Xu et al., 2023; He al., 2025d;c), our unfolding- based combination strategy is underpinned by explicit theoretical guarantees with the two models better coupled. Moreover, shown in Fig. 5 RUN+ using more complex low-level vision algorithms results in a strong abil- ity to resist complex degradation. This motivates further exploration of unfolding- based combination strategies to enhance high-level vision algorithms' resistance to environ- mental degradation and imaging interference. Simultane- ously, it promotes low-level vision algorithms by integrating deep semantic information and high-level guidance. To- gether, these advancements ensure practical applicability in both real-world high-level and low-level vision tasks.
Performance on degraded COS scenarios. To assess the impact of environmental degradation, we followed (He et al., 2023a) simulate haze on concealed images in CODIOK and then evaluated the ability of the compared methods to resist degradation. As illustrated in Fig. 5, performance degrades as the haze concentration increases. However, our RUN demonstrates superior resilience to haze degra- dation, attributed to its multi-modality reversible modeling strategy. To enhance robustness, we replaced our recon- struction network B(+) with more complex network from CORUN (Fang et al., 2024), termed (+),,, which includes pretrained dehazing model. This brought novel unfolding network, RUN+, with (-) incorporating the pretrained model. Fig. 5 indicates integrating (+)+) enhances RUN's robustness in resisting haze degradation. This underscores the potential of RUN in addressing degraded scenarios.
Potential applications of RUN. First, we test the effect of our RUN as refiner, specifically by initializing Mo with the results of existing methods. As shown in Table 10, our approach can enhance the performance of SOTA meth-
8

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RUN: Reversible Unfolding Network for Conccaled Object Segmentation
5. Conclusions
Impact Statement
This paper proposes RUN to formulate the COS task as a baakguuuud separation model. Its optimized solution is unfolded into multistage network, where each stage comprises two reversible modules: SOFS and ROBE. SOFS applies the reversible strategy at the mask level and introduces RSS for non-local information extraction. ROBE employs reconstruction network to address conflicting foreground and background regions the RGB domain. Extensive experiments verify the superiority RUN.
This paper presents work whose goal is to advance the field of Machine Learning. There are many potential societal consequences of our work, none of which we feel must be specifically highlighted here.
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Wang, W. Sun, H., and Wang, X. Lssnet: A method for colon polyp segmentation based on local feature sup-

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RUN: Reversible Unfolding Network for Conccaled Object Segmentation
Algorithm S1 Proposed RUN Framework.
Input: concealed image C, stage number K
Output: concealed object mask MR. concealed object edge Eg, reconstructed concealed image Čк
1: Zero initialization for Mo. Bo
2: for each stage & E [1, K] do
3: Mr .M (Bk-14 M Mg-2, C),
4: Mg, Eg = (M MR. C)2
5: By B (Bx.,, M1 C)
6: B& C2 B
B Mg)
7: end for
A. Experiment
A.1. Datasets and metries
Camouflaged object detection. In this task, we follow the standard practice of SINet (Fan et al., 2020a) and perform experiments on four datasets: CHAMELEON (Skurowski et al., 2018), CAMO (Le et al., 2019), CODIOK (Fan et al., 2021a), and NC4K (Lv et al., 2021). The CHAMELEON dataset comprises 76 images, while the CAMO dataset contains 1 1,250 images divided into 8 classes. The CODIOK dataset includes 5,066 images categorized into 10 super-classes, and NC4K serves as the largest test set, with 4,121 images. For training. we use 1,000 images from CAMO and 3,040 images from CODIOK. The remaining images from these two datasets, along with all images from the other datasets, constitute the test set. To evaluate performance, we employ four widely-used metrics: mean absolute error (M,), adaptive F-measure (F3) (Margolin et al., 2014), mean E-measure (Eş) (Fan et al., 2021b), and structure measure (Sa)) (Fan al., 2017). Superior performance is indicated by lower values of M and higher values of F3, Es,, and S..
Medical concealed object segmentation. We evaluate the performance of our method on two specific tasks: polyp image segmentation and medical tubular object segmentation. For polyp image segmentation, we utilize two benchmarks: CVC- ColonDB (Tajbakhsh et al., 2015) and ET7S (Silva et al., 2014). The training protocol follows the setup of L.SNNet (Wang al., 2024). Quantitative evaluation conducted using three commonly adopted metrics: mean Dice (mDice), mean Intersection over Union (mloU), and structure measure (S,),, where higher values indicate better performance. For medical tubular object segmentation, we evaluate our method on the DRIVE' and CORN (Ma et al., 2021) datasets, with training and inference conducted separately for each dataset. For the DRIVE dataset, training and inference adhere to the dataset's ' predefined splits. For the CORN dataset, the last 70% of the data is used for training, while the first 30% serves as the test set. Following DSCNet (Qi et al., 2023), we employ three evaluation metries: mDice, area under the ROC curve (AUC), and sensitivity (SEN), with higher values reflecting better performance. To ensure fair comparison with state-of-the -f-rt medical concealed object segmentation methods, which predominantly utilize transformer-bas -bssed encoders, we adopt PVT V2 the backbone for our encoder.
Transparent object detection. For a fair comparison, we use PVT V2 as default backbone and conduct experiments on two datasets: GDD (Mei et al., 2020) and GSD (Lin & He, 2021). The training set consists of 2,980 images from GDD and 3,202 images from GSD, while the remaining images are reserved for inference. Consistent with GDNet-B (Mei et al., 2023), we evaluate performance using several metrics, including mloU, and maximum F-measure (I7maz). Superior performance indicated by lower values for M, or higher values for mloU and Fguer.
Concealed defect detection. In this task, we utilize PVT V2 as the default backbone. Consistent with established practices, evaluate the generalization capacity of our RUN framework the concealed defect detection task. Specifically, the model trained on the COD task to segment concealed objects in the CDS2K dataset (Fan et al., 2023a). Eight evaluation metrics are employed, where higher values indicate better performance for all metrics except MAE, for which lower values are preferred.
12


Reviews:
[{"review_id": "wFD0j9YRdb", "reviewer": "Reviewer_BHcc", "summary": "The paper introduces a Reversible Unfolding Network (RUN), a novel deep unfolding network that integrates object segmentation and distortion restoration tasks. RUN combines a Segmentation-Oriented Foreground Separation (SOFS) module and a Reconstruction-Oriented Background Extraction (ROBE) module to achieve more accurate segmentation by reducing uncertainties. The framework allows reversible modeling of foreground and background in both the mask and RGB domains, focusing on uncertain regions to improve accuracy. Experimental results across various COS tasks demonstrate that RUN outperforms existing methods and provides flexibility for integration with other models.", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thanks for the valuable comments. \n\n**W1. Why only conducting ablation studies on COD10K**\n\nGiven that COD10K is a representative and high-quality dataset, we follow existing methods [1,2,3] to conduct ablation studies on COD10K. We have now included the results of ablation studies on three extra datasets. As shown in the table, the outcomes remain consistent with our initial conclusions. We will add this content to the revised manuscript.\n|||$\\mathbf{C}\\rightarrow E(\\mathbf{C})$|w/o RSS|w/o VSS|w/o prior $\\hat{\\mathbf{M}}_k$|w/o prior $\\mathbf{B}_{k-1}$|w/o$\\mathbf{E}_k$|$\\mathcal{B}_1(\\cdot)\\rightarrow\\mathcal{B}(\\cdot)$|$\\mathcal{B}_2(\\cdot)\\rightarrow\\mathcal{B}(\\cdot)$|w/o $\\hat{\\mathbf{C}}_k$|Fixed$\\rightarrow$Learnable|RUN (Ours)|\n|-|-|-|-|-|-|-|-|-|-|-|-|-|\n|CHAMELEON|$M$|0.053|0.030|0.028|0.029|0.027|0.028|0.027|0.026|0.029|0.029|0.027|\n||$F_\\beta$|0.764|0.837|0.842|0.832|0.845|0.849|0.859|0.861|0.845|0.837|0.855|\n||$E_\\phi$|0.885|0.938|0.943|0.936|0.945|0.948|0.953|0.956|0.944|0.940|0.952|\n||$S_\\alpha$|0.802|0.883|0.889|0.886|0.889|0.891|0.893|0.900|0.893|0.889|0.895|\n|CAMO|$M$|0.097|0.075|0.071|0.072|0.070|0.071|0.069|0.068|0.072|0.072|0.070|\n||$F_\\beta$|0.693|0.752|0.773|0.766|0.775|0.776|0.783|0.780|0.770|0.764|0.781|\n||$E_\\phi$|0.788|0.836|0.858|0.847|0.863|0.861|0.870|0.866|0.852|0.845|0.868|\n||$S_\\alpha$|0.723|0.785|0.799|0.790|0.796|0.801|0.805|0.808|0.802|0.798|0.806|\n|NC4K|$M$|0.072|0.049|0.043|0.046|0.043|0.043|0.043|0.042|0.045|0.046|0.042|\n||$F_\\beta$|0.735|0.801|0.819|0.800|0.820|0.817|0.826|0.825|0.816|0.807|0.824|\n||$E_\\phi$|0.823|0.889|0.903|0.898|0.900|0.902|0.904|0.907|0.902|0.897|0.908|\n||$S_\\alpha$|0.792|0.835|0.845|0.838|0.849|0.846|0.853|0.852|0.845|0.842|0.851|\n\n**W2. Efficiency, trade-off, and the best scenario of RUN**\n\n(1) Efficiency: We compare parameters, FLOPS, and FPS in three backbones and find that our RUN surpasses existing cutting-edge methods across all three backbones.\n|||Parameters (M)|FLOPS (G)|FPS|\n|-|-|-|-|-|\n|ResNet50|FocusDiff|166.18|7618.49|0.23|\n||RUN|30.41|43.36|22.75|\n|Res2Net50|FEDER|45.92|50.03|14.02|\n||RUN|30.57|45.73|20.26|\n|PVT V2|CamoFocus|68.85|91.35|9.63|\n||RUN|65.17|61.83|15.82|\n\n(2) Structure-level trade-off: Although different stages in RUN share the same weights, the stage number still affects the overall computational cost. To analyze the trade-off, we investigate the impact of the stage number on our ResNet50-based framework. In the table, our method surpasses existing methods when the stage number reaches 3. For an optimal balance, we set the stage number to 4.\n||FLOPS (G)|FPS|$M$|$F_\\beta$|$E_\\phi$|$S_\\alpha$|\n|-|-|-|-|-|-|-|\n|RUN-2 stages|34.12|25.27|0.031|0.727|0.893|0.812|\n|RUN-3 stages| 38.73|23.63|0.030|0.735|0.898|0.822|\n|RUN-4 stages| 43.36|22.75|0.030|0.747|0.903|0.827|\n|RUN-6 stages| 52.65|19.86|0.030|0.749|0.905|0.826|\n\n(3) Framework-level trade-off: RUN achieves cutting-edge performance for the theoretical coupling of the segmentation module (SOFS) and the reconstruction module (ROBE). As shown in the table, ROBE brings an evident gain with limited costs. This can also be verified by Table 11, where replacing SOFS with the core structures of existing methods brings clear profits.  Ablations in Table 5 indicate that replacing the simple reconstruction network with more complex ones yields marginal gains. Hence, we opt for a simple network here.\n||w/o ROBE|RUN (Ours)|\n|-|-|-|\n|$M$|0.032|0.030|\n|$F_\\beta$|0.713|0.747|\n|$E_\\phi$|0.892|0.903|\n|$S_\\alpha$|0.816|0.827|\n|Parameters (M)|26.97|30.41|\n|FLOPS (G)|37.64|43.36|\n\n(4) The best application scenarios: RUN is not only a novel unfolding-based application for COS in segmenting clean concealed objects, but also has the potential to **ensure robust performance in degraded scenes**. As shown in Fig. 5, when replacing the reconstruction network with a more complex one, we get RUN+ and observe better hazy resistance capacity.\n\nTo further verify this, we compare with bi-level optimization (BLO), a degradation-resistant framework. The differences between the two frameworks can be seen in Fig. S2, where our framework is superior in the theoretical coupling of the two models. We retrain our network (RUN+) in the BLO framework and report the results on COD10K with $F_\\beta$ and $E_\\phi$. We find that our RUN+ surpasses BLO across different hazy concentrations.\n|$F_\\beta$/$E_\\phi$|0.1|0.2|0.3|0.4|0.5|\n|-|-|-|-|-|-|\n|BLO|0.741/0.897|0.732/0.890|0.720/0.883|0.705/0.872|0.697/0.860|\n|RUN+|0.743/0.899|0.735/0.894|0.726/0.889|0.718/0.883|0.708/0.876|\n\nBesides, we analyze performance in two other degradation scenes, i.e., the low-light and low-resolution scenes, and observe that our RUN framework consistently surpasses BLO. For space limitations, we put the results in the response of **W5** of the reviewer MZkc. The reviewer can refer to the tables for more details.\n\n[1] PFNet, CVPR21\n\n[2] ZoomNet, CVPR22\n\n[3] FEDER, CVPR23", "claims_and_evidence": "This paper claims to be the first deep unfolding network for Concealed Object Segmentation (COS) problem. Based on the literature review of the past research works done for COS, this claim is clear and convincing.  Other than this claim, this paper is mainly positioned as an application paper, which optimize the objective function with new parameters and new architecture designs to tackle COS tasks. Therefore, the claim is specified for solving the particular COS problems among the given benchmarking datasets. Based on the experiments in the main paper and the supplementary materials, the proposed optimization techniques and networks are well supported.", "methods_and_evaluation_criteria": "The theoretical part of the proposed method is inspired by the observation that segmentation mask gradients should be sparse. It introduces an extra term in the objective function to enforce the certainty. The optimization of the objective function is achieved by implementing corresponding network and train the network based on the objective function loss. The methods are evaluated in 4 widely used benchmarking datasets : CHAMELEON, CAMO, COD10K, and NC4K. The baseline methods are selected from the recent state of the art methods in this task.  The proposed method consistently outperforms the baseline methods.", "theoretical_claims": "I checked and verified the correctness of the equations from Eq. (4) to Eq. (17) for theoretical derivation of the proposed objective function.", "experimental_designs_or_analyses": "The experiments have been conducted to compare the proposed method with a series of state of the art methods on the aforementioned 4 datasets. The evaluation metrics are selected properly.  For the ablation study, I can see only COD10K dataset is selected, is there any particular reason for not running it over all the datasets?", "relation_to_broader_scientific_literature": "The proposed method has contribution to the restricted area of image segmentation. The proposed optimization techniques can be applied to a broader scientific literature where the objective function has similar content in nature. The sparsity of gradients can be found in many fields including optical flow, denoising, etc.", "essential_references_not_discussed": "N.A.", "other_strengths_and_weaknesses": "Strengths\n1. This paper is well written with thorough theoretical derivation, implementation details and experiment verification. \n2. The supplementary material has provided extra complement information. The code is available and valid for generating the proper results claimed in the paper.", "other_comments_or_suggestions": "The authors are suggested to address the weakness mentioned above in the rebuttal period.", "questions_for_authors": "1. What is the processing speed of the propose method compared to other state of the art methods? What is the trade-off and what is the best scenario for application.", "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": "Ft6mWRNln5", "reviewer": "Reviewer_45Vr", "summary": "The authors propose Reversible Unfolding Network (RUN) for Concealed Object Segmentation. RUN integrates optimization-based solutions with deep learning, enabling reversible modeling across both mask and RGB domains. It also introduces the Segmentation-Oriented Foreground Separation (SOFS) module and the Reconstruction-Oriented Background Extraction (ROBE) module. Extensive experiments validate its superiority.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thanks for the valuable comments.\n\n**W1. Efficiency analysis**\n\nWe compare the parameters, FLOPS, and FPS between our RUN against cutting-edge methods on three backbones. Our stage number is set to 4. As shown in the table, our method is more efficient across all three backbones with the input size of 352×352.\n|||Parameters (M)|FLOPS (G)|FPS|\n|-|-|-|-|-|\n|ResNet50|FocusDiff|166.18|7618.49|0.23|\n||FSEL|29.15|51.03|2.19|\n||RUN|30.41|43.36|22.75|\n|Res2Net50|FEDER|45.92|50.03|14.02|\n||RUN|30.57|45.73|20.26|\n|PVT V2|CamoFocus|68.85|91.35|9.63|\n||RUN|65.17|61.83|15.82|\n\nBesides, we analyze the computational cost of our RUN framework with varying stage numbers, along with their performance using ResNet50 as the backbone on COD10K. Since the SOFS and ROBE modules at different stages share the same weights, all versions of RUN maintain a consistent parameter count. As shown in the table, our method outperforms existing state-of-the-art methods once the stage number reaches 3. For an optimal balance between efficiency and performance, we set the stage number to 4.\n||FLOPS (G)|FPS|$M$|$F_\\beta$|$E_\\phi$|$S_\\alpha$|\n|-|-|-|-|-|-|-|\n|RUN-1 stage|29.95|28.61|0.033|0.715|0.885|0.803|\n|RUN-2 stages|34.12|25.27|0.031|0.727|0.893|0.812|\n|RUN-3 stages| 38.73|23.63|0.030|0.735|0.898|0.822|\n|RUN-4 stages| 43.36|22.75|0.030|0.747|0.903|0.827|\n|RUN-6 stages| 52.65|19.86|0.030|0.749|0.905|0.826|\n|RUN-8 stages| 62.02|16.53|0.030|0.751|0.905|0.830|\n\n**W2. Ablation study: baseline and more results**\n\nAs shown in Table 5, we conduct ablation studies on the ResNet50-based RUN framework.\n\nTo ensure the integrity of our reversible modeling strategy, we retain both SOFS and ROBE rather than performing breakdown ablations. Instead, in Table 5, we evaluate the contribution of each content within SOFS and ROBE by either replacing them with alternative designs or directly removing them, both of which lead to performance decreases.\n\nHere we report the results of removing SOFS and ROBE. To remove the segmentation module SOFS, we add an extra segmentation head, i.e., the convolution blocks, after ROBE, with the segmentation loss unchanged. For the case w/o ROBE, we simply remove ROBE and keep SOFS. Besides, we include a baseline where both SOFS and ROBE are removed, leaving only the segmentation head in each stage. As shown in the table, adding SOFS and ROBE brings a clear performance gain.\n||Baseline|w/o SOFS|w/o ROBE|RUN (Ours)|\n|-|-|-|-|-|\n|$M$|0.053|0.038|0.032|0.030|\n|$F_\\beta$|0.552|0.683|0.713|0.747|\n|$E_\\phi$|0.718|0.855|0.892|0.903|\n|$S_\\alpha$|0.682|0.797|0.816|0.827|\n\n**W3. Comparison with segmentation refinement methods**\n\nOur RUN is an end-to-end network designed for COS. For its structural characteristics, it can also be applied for refining coarse masks by initializing $\\mathbf{M}_0$ as the coarse mask.\n\nWe compare our method with segmentation refinement methods, including traditional methods (dense crf (DCRF) [1] and biliteral solver (BS) [2]) and learning-based methods (SegRefiner [3] and SAMRefiner [4]). We use segmentation results from FEDER and FGANet as coarse masks. For a fair comparison, we also provide retrained versions of the learning-based refiners for the COS task, SegRefiner+ and SAMRefiner+, following their original training rules.\n\nAs shown in the table, traditional methods fail to improve the segmentation results of FEDER and FGANet. SegRefiner and SAMRefiner also achieve suboptimal performance with their provided models. \n\nThis is because concealed object segmentation is an inherently difficult task, where objects are visually blended with their surroundings. Hence, unlike common segmentation tasks, this task provides far fewer discriminative cues for feature extraction. Besides, the coarse segmentation masks often suffer from significant quality issues, such as mis-segmentation, blurred edges, and high uncertainty in certain pixel regions, making mask refinement particularly challenging.\n\nAfter retraining, the learning-based refiners show noticeable performance gains, with SAMRefiner+ achieving results comparable to our RUN. However, our RUN framework achieves an FPS of 22.75, outperforming SAMRefiner with only 0.92 FPS.\n||FEDER|+DCRF|+BS|+SegRefiner|+SegRefiner+|+SAMRefiner|+SAMRefiner+|FEDER-R (Ours)|\n|-|-|-|-|-|-|-|-|-|\n|$M$|0.032|0.039|0.043|0.037|0.033|0.038|0.033|0.031|\n|$F_\\beta$|0.715|0.683|0.666|0.691|0.718|0.686|0.723|0.721|\n|$E_\\phi$|0.892|0.853|0.862|0.863|0.889|0.857|0.906|0.897|\n|$S_\\alpha$|0.810|0.797|0.770|0.781|0.803|0.783|0.805|0.812|\n\n||FGANet|+DCRF|+BS|+SegRefiner|+SegRefiner+|+SAMRefiner|+SAMRefiner+|FGANet-R (Ours)|\n|-|-|-|-|-|-|-|-|-|\n|$M$|0.032|0.041|0.042|0.043|0.034|0.037|0.034|0.032|\n|$F_\\beta$|0.708|0.665|0.649|0.662|0.698|0.682|0.725|0.716|\n|$E_\\phi$|0.894|0.846|0.828|0.847|0.887|0.866|0.900|0.897|\n|$S_\\alpha$|0.803|0.772|0.764|0.765|0.787|0.774|0.803|0.805|\n\n[1] Dense CRF, NeurIPS21\n\n[2] The fast bilateral solver, ECCV16\n\n[3] SegRefiner, NeurIPS23\n\n[4] SAMRefiner, ICLR25", "claims_and_evidence": "N/A", "methods_and_evaluation_criteria": "N/A", "theoretical_claims": "N/A", "experimental_designs_or_analyses": "N/A", "relation_to_broader_scientific_literature": "N/A", "essential_references_not_discussed": "N/A", "other_strengths_and_weaknesses": "Strengths\n- RUN develops the first deep unfolding network for COS, revealing the potential of deep unfolding network in COS.\n- RUN introduces SOFS and ROBE modules to direct attention to uncertain regions through iterative optimization\n- RUN achieves excellent performances across five different tasks against other task-specific methods.\n- RUN can integrate with existing methods to further boost performance, showcasing its flexibility.\n\nWeaknesses\n- This paper does not have any discussion of computational efficiency. It can be found that the performance is worse than most SOTA methods when the stage number K is small by comparing the results in Table 7 and Table 1. However, a larger K requires longer inference time. The authors should compare the computational cost between RUN and other methods. \n- Ablation studies are unclear. What is the baseline for this method? How does the model perform when the proposed SOFS and ROBE are removed?\n- The proposed method can be regarded as a post-processing method. Therefore, it should be compared with common post-processing methods in segmentation, such as dense crf and bilateral solver.", "other_comments_or_suggestions": "N/A", "questions_for_authors": "N/A", "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": "MBfZdaGeIP", "reviewer": "Reviewer_MZkc", "summary": "This paper proposes the first deep unfolding network, RUN, for the COS task, aiming to cope with one of the intrinsic challenges in the COS task, which is neglecting the importance of applying reversible strategies in the RGB domain. To achieve this goal, two reversible modules, SOFS and ROBE, are further proposed. By treating the foreground-background separation problem as the noise elimination problem, ROBE serves as a valuable assistant for SOFS in promoting accurate segmentation. Abundant experiments verify the potential of this method.", "questions": "", "limitations": "", "rating": 5, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thanks for the valuable comments.\n\n**W1. More related works**\n\nIn related works, the COS component focuses on the development of the reversible modeling strategy, while our coverage of DUN is limited due to the underexploration of this technique in high-level vision tasks. To provide a more comprehensive overview, we will add a section that systematically reviews the advancements in the COS field.\n\n**W2&W3. Why use a simple network in ROBE? How about replacing it with a more complex one?**\n\n(1) The reconstruction task in ROBE is relatively easy, compared to most low-level vision tasks, as the input images are of high quality. Thus, we select a lightweight network to balance performance and computational efficiency. Experimental results validate the effectiveness of this design choice.\n|||Parameters (M)|FLOPS (G)|FPS|\n|-|-|-|-|-|\n|ResNet50|FocusDiff|166.18|7618.49|0.23|\n||FSEL|29.15|51.03|2.19|\n||RUN|30.41|43.36|22.75|\n|Res2Net50|FEDER|45.92|50.03|14.02|\n||RUN|30.57|45.73|20.26|\n|PVT V2|CamoFocus|68.85|91.35|9.63|\n||RUN|65.17|61.83|15.82|\n\n(2) As shown in Table 5, replacing the simple network with more complex networks brings limited performance gains. However, the results in Fig. 5 indicate that a more complex network can better resist degradation conditions.\n\n(3) Furthermore, as shown in the table, increasing the stage number can also improve the network’s degradation resistance capacity. These findings can help us further exploit the potential of this DUN-based framework.\n|$F_\\beta$|0|0.1|0.2|0.3|0.4|0.5|\n|-|-|-|-|-|-|-|\n|RUN-4 stages|0.747|0.740|0.730|0.718|0.698|0.676|\n|RUN-6 stages|0.749|0.742|0.733|0.723|0.708|0.694|\n|RUN-8 stages|0.751|0.743|0.735|0.726|0.714|0.701|\n\n|$E_\\phi$|0|0.1|0.2|0.3|0.4|0.5|\n|-|-|-|-|-|-|-|\n|RUN-4 stages|0.903|0.898|0.892|0.885|0.876|0.865|\n|RUN-6 stages|0.905|0.898|0.893|0.886|0.878|0.869|\n|RUN-8 stages|0.905|0.900|0.896|0.891|0.885|0.877|\n\n**W4. Performance of SOFS**\n\nAs shown in the table, removing ROBE leads to a noticeable performance decline. The resulting model underperforms compared to the state-of-the-art method, FocusDiff, further highlighting the effect of our approach. This can also be verified by Table 11, where replacing SOFS with the core structures of existing methods results in clear performance improvements.\n||w/o ROBE|RUN (Ours)|\n|-|-|-|\n|$M$|0.032|0.030|\n|$F_\\beta$|0.713|0.747|\n|$E_\\phi$|0.892|0.903|\n|$S_\\alpha$|0.816|0.827|\n\n**W5. Compare with bi-level optimization on more degradation scenes**\n\n(1) As shown in Fig. 5, replacing the original simple network with a more complex dehazing network leads to RUN+, which improves robustness against haze degradation. To further evaluate our approach, we test its performance within a bi-level optimization (BLO) framework following the setup of HQG-Net [1], where ROBE and SOFS are treated as low-level and high-level vision blocks, as illustrated in Fig. S2. Evaluating performance on COD10K using $F_\\beta$ and $E_\\phi$, we find that RUN+ consistently outperforms the BLO framework across different haze concentrations.\n|$F_\\beta$|0.1|0.2|0.3|0.4|0.5|\n|-|-|-|-|-|-|\n|BLO|0.741|0.732|0.720|0.705|0.697|\n|RUN+|0.743|0.735|0.726|0.718|0.708|\n\n|$E_\\phi$|0.1|0.2|0.3|0.4|0.5|\n|-|-|-|-|-|-|\n|BLO|0.897|0.890|0.883|0.872|0.860|\n|RUN+|0.899|0.894|0.889|0.883|0.876|\n\n(2) Additionally, we further analyze the performance in two degradation scenarios that are extremely challenging for the COS task, i.e., the low-light and low-resolution scenarios. \n\nLow-light scenarios deepen the concealment of objects by lowering color contrast, while low-resolution scenes directly reduce the discriminative cues by reducing the number of valid pixels.\n\nAs with the above content, we also simulate degradation on concealed images in COD10K. \n\nFor the low-light scenarios, we applied image darkening [2] and degraded the dataset into three levels: slight low-light, medium low-light, and severe low-light. We employ Reti-Diff [3] as the reconstruction network in the ROBE module. Experiments verify that our RUN framework achieves better performance than the BLO version.\n|$F_\\beta$ / $E_\\phi$|slight|medium|severe|\n|-|-|-|-|\n|BLO|0.719/0.870|0.691/0.853|0.662/0.813|\n|RUN+|0.735/0.889|0.716/0.878|0.683/0.855|\n\nFor the low-resolution scenes, we use bicubic downsampling and select x2, x4, x8 for reconstruction and segmentation. DiffIR [4] is selected as the reconstruction network. As shown in the table, RUN still outperforms BLO in the low-resolution challenges. \n|$F_\\beta$/$E_\\phi$|x2|x4|x8|\n|-|-|-|-|\n|BLO|0.706/0.865|0.662/0.847|0.580/0.786|\n|RUN+|0.729/0.881|0.707/0.866|0.625/0.820|\n\nTo sum up, our RUN framework is not only a novel unfolding-based application for high-level vision tasks but also **effectively integrates high-level and low-level vision, ensuring robust performance even in degraded scenarios**.\n\n[1] HQGNet, TNNLS23\n\n[2] StableLLVE, CVPR21\n\n[3] Reti-Diff, ICLR25\n\n[4] DiffIR, ICCV23", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "Yes", "theoretical_claims": "The theoretical analysis in this paper is accurate.", "experimental_designs_or_analyses": "The abundant experiments are both convincing and well-discussed.", "relation_to_broader_scientific_literature": "This is the first application of deep unfolding network, a widely-used strategy in low-level vision tasks that is favored in achieving a balance in interpretability and generalizability, in high-level vision tasks. This brings positive effects to the whole high-level vision field.", "essential_references_not_discussed": "N/A", "other_strengths_and_weaknesses": "Strengths:\nThe proposed RUN framework is not only a novel DUN-based application for the high-level vision task but also allows for the combination of high-level and low-level vision, ensuring robust performance in high-level vision tasks even in degraded scenarios.\n\nWeaknesses:\n1 The related work is very short. Please consider providing a more detailed description in the revised version.\n2 Why use a very simple reconstruction network in ROBE, given its reconstruction capacity is highly limited in this condition? \n3 Also, the reviewer wants to know if changing the simple network in ROBE to a more complex framework brings an evident performance gain.\n4 The reviewer is curious about if the single SOFS module achieves comparable performance with existing methods.\n5 More experiments are preferred to analyze the performance in complex degradation. The reviewer thinks this is a major challenge in COS. Please fully prove whether RUN can address this challenge and make comparison with existing strategies, such as bi-level optimization.", "other_comments_or_suggestions": "I think this paper is very interesting. Please consider addressing the weaknesses. I will consider modifying the score based on the response.", "questions_for_authors": "See weaknesses.", "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": "mj9YFR9PAx", "reviewer": "Reviewer_tcYD", "summary": "This paper tackles the challenging Concealed Object Segmentation, which aims to segment objects that are visually blended with their surroundings. It introduces the Reversible Unfolding Network (RUN), a iteratively method to refine segmentation results and minimize uncertainties. RUN includes two main modules: the Segmentation-Oriented Foreground Separation (SOFS) module, which captures non-local context and applies the reversible strategy at the mask level, and the Reconstruction-Oriented Background Extraction (ROBE) module, which addresses conflicting foreground and background regions. The method was evaluated on multiple datasets to valid its effectiveness.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thanks for the valuable comments.\n\n**W1. Challenges in introducing DUNs and importance of modeling high-level vision tasks**\n\nIn P3L142, we state that deep unfolding networks (DUNs) are underexplored in high-level vision tasks for lacking intrinsic models. \n\n**Challenges:** DUNs rely on model-based optimization, which is effective in low-level vision tasks due to well-defined physical models (e.g., Retinex model for low-light enhancement, Atmospheric Scattering model for dehazing). **However, high-level vision tasks lack an explicit physical model, limiting DUN applications.**\n\n**Importance:** \n- Explicit constraints: Modeling high-level vision tasks enables task-specific constraints to guide optimization explicitly. For example, Eq. (4) introduces a residual sparsity constraint to refine segmentation and reduce uncertainties (P3L130, right part). This is more direct than implicit backpropagation. Table 5 shows a performance drop when removing this constraint (w/o prior $\\hat{\\mathbf{M}}_k$).\n- Introducing DUNs into high-level vision: With an explicit model, DUNs integrate optimization with deep networks, balancing interpretability and generalizability (P2L58, right part). RUN benefits in effectiveness (Table 1), efficiency (see **W2**), and degradation resistance (Fig. 5) in COS.\n\n**W2. Efficiency analysis**\n\nThe designs of $\\hat{\\mathcal{M}}(\\cdot)$ and $\\hat{\\mathcal{B}}(\\cdot)$ follow mathematical principles and may appear complex, but we only make fixed hyperparameters learnable and enforce weight sharing for SOFS and ROBE across stages. In the table, our method is more efficient than SOTAs.\n|||Param. (M)/FLOPS (G)/FPS|\n|-|-|-|\n|ResNet50|FocusDiff|166.18/7618.49/0.23|\n||RUN|30.41/43.36/22.75|\n|Res2Net50|FEDER|45.92/50.03/14.02|\n||RUN|30.57/45.73/20.26|\n|PVT V2|CamoFocus|68.85/91.35/9.63|\n||RUN|65.17/61.83/15.82|\n\n**W3. Comparison with more methods**\n\nBiRefNet [1] targets high-resolution segmentation (HRS); ZoomNeXt [2] focuses on multiple input scales (MIS), **differing from our setting**. \n\nHigher resolution and multiscale inputs enhance subtle discriminative cues but add computational overhead. We also report our results in these settings.\n\nFor HRS, we retrain our method following BiRefNet [4], replacing our encoder with SwinL and training at 1024 x 1024 resolution. We evaluate our results on the three datasets reported in BiRefNet and find that RUN outperforms BiRefNet in most metrics. This is primarily due to our reversible modeling strategy. \n|HRS ($M$/$F_\\beta$/$E_\\phi$/$S_\\alpha$)|CAMO|COD10K|NC4K|\n|-|-|-|-|\n|BiRefNet|0.030/0.895/0.954/0.904|0.014/0.881/0.960/0.913|0.023/0.925/0.953/0.914|\n|RUN|0.032/0.898/0.951/0.910|0.012/0.878/0.967/0.920|0.022/0.946/0.961/0.927|\n\nFor MIS, we compare with ZoomNeXt (352 × 352). In the table, RUN outperforms ZoomNeXt in 8 out of 12 metrics. Unlike ZoomNeXt’s specialized extraction-fusion strategy, RUN simply concatenates multiscale features. Adding ZoomNeXt’s strategy to RUN yields RUN++, improving results.\n|MIS ($M$/$F_\\beta$/$E_\\phi$/$S_\\alpha$)|CHAMELEON|CAMO|COD10K|NC4K|\n|-|-|-|-|-|\n|ZoomNext|0.020/0.872/0.963/0.912|0.069/0.782/0.883/0.822|0.026/0.765/0.918/0.855|0.038/0.827/0.919/0.869|\n|RUN|0.022/0.878/0.967/0.911|0.064/0.807/0.902/0.832|0.027/0.772/0.920/0.843|0.040/0.836/0.922/0.868|\n|RUN++|0.019/0.885/0.971/0.916|0.063/0.815/0.906/0.833|0.025/0.781/0.930/0.852|0.038/0.847/0.932/0.875|\n\n**W4. Performance in segmentation refinement**\n\nRUN, an end-to-end network for COS, can also serve as a segmentation refiner by initializing $\\mathbf{M}_0$ as the coarse mask.\n\nWe compare with several segmentation refiners: traditional methods (dense crf (DCRF) [3] and biliteral solver (BS) [4]) and learning-based methods (SegRefiner [5] and SAMRefiner [6]). To ensure fairness, we retrain the learning-based ones for COS, SegRefiner+ and SAMRefiner+, following their training rules.\n\nTraditional methods fail to refine FEDER’s segmentation in COD10K due to the challenges of COS (objects blending with surroundings). SegRefiner and SAMRefiner also perform suboptimally with their provided models, as coarse COS masks contain segmentation errors and uncertain regions, complicating refinement.\n\nRetraining improves SegRefiner+ and SAMRefiner+, with SAMRefiner+ getting results comparable to RUN. However, RUN (FPS: 22.75) outperforms SAMRefiner (FPS: 0.92) and SegRefiner (FPS: 0.89) in efficiency. \n\nRefinement results for FGANet’s masks are discussed in **W3** (reviewer 45Vr), with similar conclusions.\n\n||FEDER|+DCRF|+BS|+SegRefiner|+SegRefiner+|+SAMRefiner|+SAMRefiner+|FEDER-R (Ours)|\n|-|-|-|-|-|-|-|-|-|\n|$M$|0.032|0.039|0.043|0.037|0.033|0.038|0.033|0.031|\n|$F_\\beta$|0.715|0.683|0.666|0.691|0.718|0.686|0.723|0.721|\n|$E_\\phi$|0.892|0.853|0.862|0.863|0.889|0.857|0.906|0.897|\n|$S_\\alpha$|0.810|0.797|0.770|0.781|0.803|0.783|0.805|0.812|\n\n[1] BiRefNet, CAAI AIR24\n\n[2] ZoomNext, TPAMI24\n\n[3] Dense CRF, NIPS21\n\n[4] Bilateral Solver, ECCV16\n\n[5] SegRefiner, NIPS23\n\n[6] SAMRefiner, ICLR25", "claims_and_evidence": "The paper claims that previous works use reversible strategies but are generally limited to the mask domain. In contrast, this work introduces RUN, which extends reversible strategies to both the mask and RGB domains.\n\nIn P3L142, the paper states that integrating model-based and learning-based approaches remains underexplored due to the lack of intrinsic models. The paper should provide discussion to clarify the specific challenges involved and explain why addressing this gap is important.", "methods_and_evaluation_criteria": "The methods is evaluated on multiple benchmarks using the standard metrics. \n\nAccording to Fig. 3, the architecture appears somewhat complex. The paper should provide a comparison of FPS and parameter count to better demonstrate the efficiency of this method.", "theoretical_claims": "N/A", "experimental_designs_or_analyses": "The methods is evaluated on multiple benchmarks using the standard metrics. \n\nAlthough previous works have not fully explored the RGB domain for mask refinement, this method appears to underperform compared to BiRefNet [1] and ZoomNeXt [2]. For instance, the S-measure on CAMO (higher is better) is 0.806 for this work, whereas BiRefNet achieves 0.904. Additionally, the paper does not provide the comparison results with these methods.\n\nThe comparisons are insufficient. Please include a comparison with previous segmentation refinement methods, such as [3].\n\n[1] Bilateral Reference for High-Resolution Dichotomous Image Segmentation, CAAI AIR, 2024.\n\n[2] ZoomNeXt: A Unified Collaborative Pyramid Network for Camouflaged Object Detection, TPAMI, 2024.\n\n[3] SegRefiner: Towards Model-Agnostic Segmentation Refinement with Discrete Diffusion Process, NeurIPS, 2023.", "relation_to_broader_scientific_literature": "This work proposes an iterative method to refine segmentation results, which may benefit related segmentation tasks.", "essential_references_not_discussed": "[1] Bilateral Reference for High-Resolution Dichotomous Image Segmentation, CAAI AIR, 2024.\n\n[2] ZoomNeXt: A Unified Collaborative Pyramid Network for Camouflaged Object Detection, TPAMI, 2024.\n\n[3] SegRefiner: Towards Model-Agnostic Segmentation Refinement with Discrete Diffusion Process, NeurIPS, 2023.", "other_strengths_and_weaknesses": "N/A", "other_comments_or_suggestions": "N/A", "questions_for_authors": "Please refer to the comments in the above reviews.", "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:

Meta-review: After careful consideration of the reviewers' comments and the authors' rebuttal, I believe that the paper makes a meaningful contribution to the field of Concealed Object Segmentation. The novelty of the deep unfolding network approach, combined with the strong experimental results and robustness in degraded scenarios, justifies acceptance. While the framework's complexity and lack of baseline comparison are valid concerns, they do not outweigh the paper's strengths.
Decision: Accept (poster)