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Weakly Supervised Camouflaged Object Detection Based on the SAM Model and Mask Guidance

T0 review · 2 major / 1 minor · reviewed 2026-06-29 · grok-4.3

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Pith's one-line read Mask-guided network trained on SAM pseudo-labels from boxes matches state-of-the-art in camouflaged detection

desk verdict This paper describes a SAM-prompted pseudo-label pipeline plus custom modules for weakly supervised camouflaged detection, but the abstract supplies no metrics to support the competitive-performance claim. read the letter →

arxiv 2605.25385 v1 pith:MS2BSM7W submitted 2026-05-25 cs.CV cs.AI

Xia Li , Xinran Liu , Lin Qi , Junyu Dong This is my paper
classification cs.CVcs.AI
keywords camouflagedobjectdetectionweaklysupervisedpseudo-labelsSAMmaskguidanceboundingboxesedgeambiguitycontextenhancement
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper seeks to establish that high-quality pixel-level pseudo-labels can be generated from bounding box annotations using the Segment Anything Model with a redundant processing strategy. These labels then train MGNet, which addresses edge ambiguity through mask guidance from its Cascaded Mask Decoder and reduces missed detections with a Context Enhancement Module. A sympathetic reader cares because this reduces reliance on costly pixel-level annotations for a difficult task where objects blend into their surroundings. The result is competitive performance with fully supervised methods while using easier-to-obtain weak labels.

What carries the argument

MGNet, which uses initial masks from the Cascaded Mask Decoder to guide segmentation and enhance edge predictions, combined with BoxSAM for pseudo-label generation.

What would settle it

An experiment retraining the network with the generated pseudo-labels and measuring its performance on standard camouflaged object detection test sets, finding it significantly below fully supervised baselines, would falsify the central claim.

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Extended reading notes

Core claim

The central claim is that BoxSAM generates high quality pixel-level pseudo-labels by leveraging the Segment Anything Model with bounding-box prompts and redundant processing, allowing MGNet to be trained effectively. MGNet tackles edge ambiguity and missed detections by utilizing initial masks from the Cascaded Mask Decoder to guide the segmentation process, incorporates a Context Enhancement Module to reduce missing detections, and a Mask-guided Feature Aggregation Module for effective feature aggregation, delivering competitive performance against current state-of-the-art methods.

Load-bearing premise

The pseudo-labels generated by BoxSAM using bounding-box prompts and redundant processing are of sufficient quality to train MGNet without the performance degradation typically seen in weakly supervised methods.

Editorial extensions

If this is right

  • The method delivers competitive performance against current state-of-the-art methods.
  • It provides high quality pixel-level pseudo-labels for training without typical weak supervision degradation.
  • The Context Enhancement Module reduces missed detections.
  • The Mask-guided Feature Aggregation Module enables effective feature aggregation.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The success with bounding box prompts suggests SAM's versatility in generating labels for other detection domains.
  • Redundant processing could be a general technique to boost pseudo-label quality in prompt-based systems.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

2 major / 1 minor

Summary. The paper proposes a weakly supervised camouflaged object detection method called MGNet. It generates pixel-level pseudo-labels via BoxSAM (SAM prompted by bounding boxes with a redundant processing strategy) and trains MGNet using initial masks from a Cascaded Mask Decoder (CMD) to guide segmentation, a Context Enhancement Module (CEM) to mitigate missed detections, and a Mask-guided Feature Aggregation Module (MFAM) for feature aggregation. The central claim is that this pipeline overcomes limitations of coarse annotations in weakly supervised COD and achieves competitive performance versus state-of-the-art methods.

Significance. If the empirical results hold, the work could demonstrate a practical route to high-quality pseudo-labels for COD by combining SAM with redundant box prompting, potentially lowering annotation costs while addressing edge ambiguity and missed detections. The modular design (CMD/CEM/MFAM) offers reusable components for mask-guided segmentation.

major comments (2)
  1. [Abstract] Abstract: the claim that the method 'delivers competitive performance against current state-of-the-art methods' and supplies 'high quality pixel-level pseudo-labels' is unsupported by any quantitative metrics, tables, baselines, or ablation results. Without these data it is impossible to assess whether the central empirical claim holds.
  2. [Abstract / Method description] The weakest assumption—that pseudo-labels from BoxSAM and the CMD initial masks are of sufficient quality to avoid the performance degradation typical of weakly supervised methods—requires explicit validation. No comparison to fully supervised upper bounds or to other box-supervised baselines is referenced in the provided text.
minor comments (1)
  1. [Abstract] Abstract: 'Context Enhancement Module(CEM)' is missing a space before the parenthesis.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address each major comment point by point below, indicating where revisions will be made to strengthen the presentation of results and validation.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the claim that the method 'delivers competitive performance against current state-of-the-art methods' and supplies 'high quality pixel-level pseudo-labels' is unsupported by any quantitative metrics, tables, baselines, or ablation results. Without these data it is impossible to assess whether the central empirical claim holds.

    Authors: The abstract summarizes findings from the experimental section (Section 4), which contains quantitative tables, baseline comparisons, and ablation studies supporting the performance claims and pseudo-label quality. To improve self-containment and address the concern directly, we will revise the abstract to include key quantitative metrics (e.g., specific mIoU or F-measure values) and explicit references to the relevant tables and figures. revision: yes

  2. Referee: [Abstract / Method description] The weakest assumption—that pseudo-labels from BoxSAM and the CMD initial masks are of sufficient quality to avoid the performance degradation typical of weakly supervised methods—requires explicit validation. No comparison to fully supervised upper bounds or to other box-supervised baselines is referenced in the provided text.

    Authors: We agree that explicit validation of pseudo-label quality through comparisons would strengthen the manuscript. The current work emphasizes comparisons against state-of-the-art weakly supervised COD methods, but we will add comparisons to fully supervised upper bounds and additional box-supervised baselines in the revised experiments section, along with an analysis quantifying any performance gap attributable to the pseudo-labels. revision: yes

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity detected

full rationale

The paper describes an empirical pipeline (BoxSAM pseudo-label generation via SAM with bounding-box prompts and redundant processing, followed by MGNet training with CMD, CEM, and MFAM modules) and asserts competitive results on experiments. No equations, fitted parameters, derivations, uniqueness theorems, or self-citation chains appear in the provided text. All load-bearing claims reduce to standard architectural choices and external empirical validation rather than any self-referential reduction by construction.

Assumptions & free parameters 0 free parameters · 0 assumptions · 0 invented entities

Unable to enumerate free parameters, axioms, or invented entities because only the abstract is available; the approach appears to rest on standard deep-learning assumptions for segmentation and the pre-trained SAM model.

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Cite this review

Pith. "Pith review of Weakly Supervised Camouflaged Object Detection Based on the SAM Model and Mask Guidance." pith.science (2026). https://pith.science/paper/MS2BSM7W

@misc{pith2026260525385,
  author       = {Pith},
  title        = {Pith review of: Weakly Supervised Camouflaged Object Detection Based on the SAM Model and Mask Guidance},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/MS2BSM7W}},
  note         = {Machine review of arXiv:2605.25385}
}
read the original abstract

Camouflaged object detection (COD) from a single image is a challenging task due to the high similarity between objects and their surroundings. Existing fully supervised methods require labor-intensive pixel-level annotations, making weakly supervised methods a viable compromise that balances accuracy and annotation efficiency. However, weakly supervised methods often experience performance degradation due to the use of coarse annotations. In this paper, we introduce a new weakly supervised approach for camouflaged object detection to overcome these limitations. Specifically, we propose a novel network, MGNet, which tackles edge ambiguity and missed detections by utilizing initial masks generated by our custom-designed Cascaded Mask Decoder (CMD) to guide the segmentation process and enhance edge predictions. We introduce a Context Enhancement Module(CEM) to reduce the missing detection, and a Mask-guided Feature Aggregation Module (MFAM) for effective feature aggregation. For the weak supervision challenge, we propose BoxSAM, which leverages the Segment Anything Model (SAM) with bounding-box prompts to generate pseudo-labels. By employing a redundant processing strategy, high quality pixel-level pseudo-labels are provided for training MGNet. Extensive experiments demonstrate that our method delivers competitive performance against current state-of-the-art methods.

Figures

Figures reproduced from arXiv: 2605.25385 by the authors.

Figure 1
Figure 1. Visual examples of different methods. (a) RGB images. (b) Ground truth. (c)-(d) The detection results obtained by (c) Our results, (d) FSPNet [19]. which involves costly pixel-level annotation [1]. Conse￾quently, weakly supervised COD holds significant research importance. Weakly supervised camouflaged object detec￾tion (WSCOD) relies on point annotations, bounding-box annotations or scribble annotations in the fore… view at source ↗
Figure 2
Figure 2. Overview of WSCOD with bounding-box supervision (BoxSAM). By annotating the bounding-boxes of the camouflaged objects and combining the SAM [25] model to output the pseudo-labels, we design a redundancy processing strategy (see Section 3.2) with MGNet (see Section 3.3) to process the pseudo-labels [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Bounding-box annotations method, images from CAMO [26] and COD10K [8]. The green bounding-boxes represent our annotated bounding-boxes. The annotations are classified into 3 cases: (a) A bounding-box contains a camouflaged object. (b) Multiple bounding-boxes contain multiple camouflaged objects. (c) A bounding-box contains multiple camouflaged objects. and interpretation of camouflaged objects. DiffCOD [6] is also b… view at source ↗
Figures from the paper (11 more)
Figure 4
Figure 4. Figure 4: Overview of Mask-guided Network (MGNet). The MGNet consists of our designed Cascaded Mask Decoder (CMD, see Section 3.3.2), Context Enhancement Module (CEM, see Section 3.3.3) and Mask-guided Feature Aggregation Module (MFAM, see Section 3.3.4) [PITH_FULL_IMAGE:figure…
Figure 5
Figure 5. Figure 5: Some examples of SAM segmentation with bounding￾boxes [PITH_FULL_IMAGE:figures/full_fig_p005_5.png]
Figure 6
Figure 6. Figure 6: The details of Cascaded Mask Decoder (CMD). The multi-level features are processed through the Cascaded Mask Decoder (CMD), which progressively integrates infor￾mation from each layer to generate the mask 𝑃4 . The multi￾level features introduce rich context information…
Figure 8
Figure 8. Figure 8: The details of Mask-guided Feature Aggregation Module (MFAM). features from all four branches are combined and the features are extracted through a convolutional layer to obtain 𝐶𝑖 , as expressed in the Eq. 7 and Eq. 8: 𝐷𝑖 = 𝐵𝑁(𝐷 1 𝑖 + 𝐷 2 𝑖 + 𝐷 3 𝑖 ) (7) 𝐶𝑖 = 𝑅𝑒𝐿𝑈(𝐶𝑜𝑛…
Figure 9
Figure 9. Figure 9: Qualitative comparison with WSCOD methods. (a) RGB images. (b) Ground truth. (c)-(g) The detection results obtained by (c) Our method based on bounding-box supervision, (d) Our method based on scribble supervision, (e) WS-SAM [14] based on scribble supervision, (f) Our…
Figure 10
Figure 10. Figure 10: Qualitative comparison with Transformer-based COD methods. (a) RGB images. (b) Ground truth. (c)-(f) The detection results obtained by (c) Our method based on Transformer, (d) HitNet [18], (e) FSPNet [19], (f) DTINet [36] [PITH_FULL_IMAGE:figures/full_fig_p010_10.png]
Figure 11
Figure 11. Figure 11: Some examples of masks. (a) RGB images. (b) Ground truth. (c) Some examples of SAM segmentation with bounding￾boxes. (d) Some examples of masks after redundancy processing. 4.3.2. Qualitative comparisons [PITH_FULL_IMAGE:figures/full_fig_p011_11.png]
Figure 12
Figure 12. Figure 12: Visual verification of the effectiveness of the proposed components. (a) RGB images. (b) Ground truth. (c)-(e) The detection results obtained by (c) Our method (MGNet), (d) MGNet without CEM, (e) MGNet without CMD and MFAM. datasets, the 𝑆𝛼 improves by an average of 1…
Figure 13
Figure 13. Figure 13: Failure cases of SAM segmentation results using bounding-box prompts. dataset. By comparing the results in the #7 and #8 of [PITH_FULL_IMAGE:figures/full_fig_p014_13.png]
Figure 14
Figure 14. Figure 14: Qualitative comparison on CVC-ClinicDB dataset [2] and CVC-ColonDB [44] dataset. (a) RGB images. (b) Ground truth. (c)-(e) The detection results obtained by (c) Our method, (d) SINet￾V2[7], (e) HitNet[18]. metrics include 𝑚𝐷𝑖𝑐𝑒, 𝑚𝐼𝑜𝑈, 𝑆𝛼 , , and 𝐸𝜙 . Among these metr…
Figure 15
Figure 15. Figure 15: Qualitative comparison on CDS2K dataset [9]. (a) RGB images. (b) Ground truth. (c)-(e) The detection results obtained by (c) Our method, (d) SINet-V2 [7], (e) MSCAF-Net [33]. [33]. The results were obtained by training and testing on equipment with the same configurat…

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Works this paper leans on

76 extracted references · 2 canonical work pages

  1. [1]

    What’s the point: Semantic segmentation with point supervision, in: Euro- pean conference on computer vision, Springer

    Bearman, A., Russakovsky, O., Ferrari, V., Fei-Fei, L., 2016. What’s the point: Semantic segmentation with point supervision, in: Euro- pean conference on computer vision, Springer. pp. 549–565

    2016

  2. [2]

    Wm-dova maps for accurate polyp highlightingincolonoscopy:Validationvs.saliencymapsfromphysi- cians

    Bernal, J., Sánchez, F.J., Fernández-Esparrach, G., Gil, D., Ro- dríguez, C., Vilariño, F., 2015. Wm-dova maps for accurate polyp highlightingincolonoscopy:Validationvs.saliencymapsfromphysi- cians. Computerized medical imaging and graphics 43, 99–111

    2015

  3. [3]

    Ma-sam: Modality-agnostic sam adaptation for 3d medical image segmentation

    Chen, C., Miao, J., Wu, D., Zhong, A., Yan, Z., Kim, S., Hu, J., Liu, Z., Sun, L., Li, X., et al., 2024a. Ma-sam: Modality-agnostic sam adaptation for 3d medical image segmentation. Medical Image Analysis , 103310

  4. [4]

    Sam-cod: Sam-guided unified framework for weakly-supervised camouflaged object detec- tion

    Chen, H., Wei, P., Guo, G., Gao, S., 2024b. Sam-cod: Sam-guided unified framework for weakly-supervised camouflaged object detec- tion. European Conference on Computer Vision

  5. [5]

    Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.,

  6. [6]

    IEEE transactions on pattern analysis and machine intelligence 40, 834– 848

    Deeplab: Semantic image segmentation with deep convo- lutional nets, atrous convolution, and fully connected crfs. IEEE transactions on pattern analysis and machine intelligence 40, 834– 848

  7. [7]

    Diffusion model for camouflaged object detection, in: ECAI 2023

    Chen, Z., Gao, R., Xiang, T.Z., Lin, F., 2023. Diffusion model for camouflaged object detection, in: ECAI 2023. IOS Press, pp. 445– 452

    2023

  8. [8]

    Concealed object detection

    Fan, D.P., Ji, G.P., Cheng, M.M., Shao, L., 2021. Concealed object detection. IEEE transactions on pattern analysis and machine intelli- gence 44, 6024–6042

    2021

Show all 76 references
  1. [9]

    Camouflaged object detection, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp

    Fan, D.P., Ji, G.P., Sun, G., Cheng, M.M., Shen, J., Shao, L., 2020a. Camouflaged object detection, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2777– 2787

  2. [10]

    Advances in deep concealed scene understanding

    Fan, D.P., Ji, G.P., Xu, P., Cheng, M.M., Sakaridis, C., Van Gool, L., 2023. Advances in deep concealed scene understanding. Visual Intelligence 1, 16

    2023

  3. [11]

    Pranet: Parallel reverse attention network for polyp segmen- tation, in: International conference on medical image computing and computer-assisted intervention, Springer

    Fan, D.P., Ji, G.P., Zhou, T., Chen, G., Fu, H., Shen, J., Shao, L., 2020b. Pranet: Parallel reverse attention network for polyp segmen- tation, in: International conference on medical image computing and computer-assisted intervention, Springer. pp. 263–273

  4. [12]

    A robust deep- learning-based detector for real-time tomato plant diseases and pests recognition

    Fuentes, A., Yoon, S., Kim, S.C., Park, D.S., 2017. A robust deep- learning-based detector for real-time tomato plant diseases and pests recognition. Sensors 17, 2022

    2017

  5. [13]

    Weakly supervised video salient object detection via point supervi- sion, in: Proceedings of the 30th ACM International Conference on Multimedia, pp

    Gao, S., Xing, H., Zhang, W., Wang, Y., Guo, Q., Zhang, W., 2022. Weakly supervised video salient object detection via point supervi- sion, in: Proceedings of the 30th ACM International Conference on Multimedia, pp. 3656–3665

    2022

  6. [14]

    Res2net: A new multi-scale backbone architecture

    Gao, S.H., Cheng, M.M., Zhao, K., Zhang, X.Y., Yang, M.H., Torr, P., 2019. Res2net: A new multi-scale backbone architecture. IEEE transactions on pattern analysis and machine intelligence 43, 652– 662

    2019

  7. [15]

    Weakly-supervised concealed object segmentation with sam- basedpseudolabelingandmulti-scalefeaturegrouping

    He,C.,Li,K.,Zhang,Y.,Xu,G.,Tang,L.,Zhang,Y.,Guo,Z.,Li,X., 2023a. Weakly-supervised concealed object segmentation with sam- basedpseudolabelingandmulti-scalefeaturegrouping. Advancesin Neural Information Processing Systems 36

  8. [16]

    Strategic preys make acute predators: Enhancing camouflaged object detectors by generating camouflaged objects, in: International Conference on Learning Representations

    He, C., Li, K., Zhang, Y., Zhang, Y., Guo, Z., Li, X., Danelljan, M., Yu, F., 2024. Strategic preys make acute predators: Enhancing camouflaged object detectors by generating camouflaged objects, in: International Conference on Learning Representations

    2024

  9. [17]

    Weakly-supervisedcam- ouflaged object detection with scribble annotations, in: Proceedings of the AAAI Conference on Artificial Intelligence, pp

    He,R.,Dong,Q.,Lin,J.,Lau,R.W.,2023b. Weakly-supervisedcam- ouflaged object detection with scribble annotations, in: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 781–789

  10. [18]

    Detection of the mobile object with camouflage color under dynamic background based on optical flow

    Hou, J.Y.Y.H.W., Li, J., 2011. Detection of the mobile object with camouflage color under dynamic background based on optical flow. Procedia Engineering 15, 2201–2205

    2011

  11. [19]

    High-resolutioniterativefeedbacknetworkforcamouflaged object detection, in: Proceedings of the AAAI Conference on Artifi- cial Intelligence, pp

    Hu, X., Wang, S., Qin, X., Dai, H., Ren, W., Luo, D., Tai, Y., Shao, L.,2023. High-resolutioniterativefeedbacknetworkforcamouflaged object detection, in: Proceedings of the AAAI Conference on Artifi- cial Intelligence, pp. 881–889

    2023

  12. [20]

    5557–5566

    Huang,Z.,Dai,H.,Xiang,T.Z.,Wang,S.,Chen,H.X.,Qin,J.,Xiong, H.,2023.Featureshrinkagepyramidforcamouflagedobjectdetection with transformers, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 5557–5566

    2023

  13. [21]

    Kvasir-seg: A segmented polyp dataset, in: MultiMedia modeling: 26th international conference, MMM2020,Daejeon,SouthKorea,January5–8,2020,proceedings, part II 26, Springer

    Jha, D., Smedsrud, P.H., Riegler, M.A., Halvorsen, P., De Lange, T., Johansen, D., Johansen, H.D., 2020. Kvasir-seg: A segmented polyp dataset, in: MultiMedia modeling: 26th international conference, MMM2020,Daejeon,SouthKorea,January5–8,2020,proceedings, part II 26, Springer....

    2020

  14. [22]

    Deep gradient learning for efficient camouflaged object detection

    Ji, G.P., Fan, D.P., Chou, Y.C., Dai, D., Liniger, A., Van Gool, L., 2023a. Deep gradient learning for efficient camouflaged object detection. Machine Intelligence Research 20, 92–108

  15. [23]

    segmentanything

    Ji, G.P., Fan, D.P., Xu, P., Zhou, B., Cheng, M.M., Van Gool, L., 2023b. Sam struggles in concealed scenes—empirical study on “segmentanything”.ScienceChinaInformationSciences66,226101

  16. [24]

    Ji,W.,Li,J.,Bi,Q.,Liu,T.,Li,W.,Cheng,L.,2024.Segmentanything is not always perfect: An investigation of sam on different real-world applications

    2024

  17. [25]

    Khan,A.,Khan,M.,Gueaieb,W.,ElSaddik,A.,DeMasi,G.,Karray, F., 2024. Camofocus: Enhancing camouflage object detection with split-featurefocalmodulationandcontextrefinement,in:Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1434–1443

    2024

  18. [26]

    Segment anything,in: Proceedingsof theIEEE/CVFInternational Conference on Computer Vision, pp

    Kirillov,A.,Mintun,E.,Ravi,N.,Mao,H.,Rolland,C.,Gustafson,L., Xiao, T., Whitehead, S., Berg, A.C., Lo, W.Y., et al., 2023. Segment anything,in: Proceedingsof theIEEE/CVFInternational Conference on Computer Vision, pp. 4015–4026

    2023

  19. [27]

    Anabranch network for camouflaged object segmentation

    Le, T.N., Nguyen, T.V., Nie, Z., Tran, M.T., Sugimoto, A., 2019. Anabranch network for camouflaged object segmentation. Computer vision and image understanding 184, 45–56

    2019

  20. [28]

    Visual saliency detection based on multiscale deep cnn features

    Li, G., Yu, Y., 2016. Visual saliency detection based on multiscale deep cnn features. IEEE transactions on image processing 25, 5012– 5024

    2016

  21. [29]

    Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp

    Li, Y., Zhang, X., Chen, D., 2018. Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1091–1100

    2018

  22. [30]

    Tree energy loss: Towards sparsely annotated semantic segmentation, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp

    Liang, Z., Wang, T., Zhang, X., Sun, J., Shen, J., 2022. Tree energy loss: Towards sparsely annotated semantic segmentation, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 16907–16916

    2022

  23. [31]

    Searchandrecoverynetworkforcamouflaged object detection

    Liu,G.,Wu,W.,2024. Searchandrecoverynetworkforcamouflaged object detection. Image and Vision Computing 151, 105247

    2024

  24. [32]

    Adaptive early-learning correction for segmentation from noisy an- notations,in:ProceedingsoftheIEEE/CVFConferenceonComputer Vision and Pattern Recognition, pp

    Liu, S., Liu, K., Zhu, W., Shen, Y., Fernandez-Granda, C., 2022a. Adaptive early-learning correction for segmentation from noisy an- notations,in:ProceedingsoftheIEEE/CVFConferenceonComputer Vision and Pattern Recognition, pp. 2606–2616

  25. [33]

    Weaklysupervisedsalientobjectdetection via bounding-box annotation and sam model

    Liu,X.,Huang,X.,2024. Weaklysupervisedsalientobjectdetection via bounding-box annotation and sam model. Electronic Research Archive 32, 1624–1645. Xia Li et al.:Preprint submitted to ElsevierPage 16 of 18 Weakly Supervised Camouflaged Object Detection Based on the SAM Model an...

    2024

  26. [34]

    Mscaf-net: A general framework for camouflaged object detection via learning multi-scale context-aware features

    Liu, Y., Li, H., Cheng, J., Chen, X., 2023. Mscaf-net: A general framework for camouflaged object detection via learning multi-scale context-aware features. IEEE Transactions on Circuits and Systems for Video Technology 33, 4934–4947

    2023

  27. [35]

    Weakly- supervised salient object detection with saliency bounding boxes

    Liu, Y., Wang, P., Cao, Y., Liang, Z., Lau, R.W., 2021. Weakly- supervised salient object detection with saliency bounding boxes. IEEE Transactions on Image Processing 30, 4423–4435

    2021

  28. [36]

    Ssfam: Scribble supervised salient object detection family

    Liu, Z., Deng, S., Wang, X., Wang, L., Fang, X., Tang, B., 2025. Ssfam: Scribble supervised salient object detection family. IEEE Transactions on Multimedia

    2025

  29. [37]

    Boosting camouflaged object detection with dual-task interactive transformer, in: 2022 26th International Conference on Pattern Recognition (ICPR), IEEE

    Liu, Z., Zhang, Z., Tan, Y., Wu, W., 2022b. Boosting camouflaged object detection with dual-task interactive transformer, in: 2022 26th International Conference on Pattern Recognition (ICPR), IEEE. pp. 140–146

    2022

  30. [38]

    Simultaneously localize, segment and rank the camouflaged objects, in:ProceedingsoftheIEEE/CVFconferenceoncomputervisionand pattern recognition, pp

    Lv,Y.,Zhang,J.,Dai,Y.,Li,A.,Liu,B.,Barnes,N.,Fan,D.P.,2021. Simultaneously localize, segment and rank the camouflaged objects, in:ProceedingsoftheIEEE/CVFconferenceoncomputervisionand pattern recognition, pp. 11591–11601

    2021

  31. [39]

    Distraction-aware camouflaged object segmentation

    Mei, H., Yang, X., Zhou, Y., Ji, G.P., Wei, X., Fan, D., 2023. Distraction-aware camouflaged object segmentation. SCIENTIA SINICA Informationis (SSI) 3, 7

    2023

  32. [40]

    Studyonthe camouflagedtargetdetectionmethodbasedon3dconvexity

    Pan,Y.,Chen,Y.,Fu,Q.,Zhang,P.,Xu,X.,etal.,2011. Studyonthe camouflagedtargetdetectionmethodbasedon3dconvexity. Modern Applied Science 5, 152

    2011

  33. [41]

    IEEE transactions on pattern analysis and machine intelligence

    Pang,Y.,Zhao,X.,Xiang,T.Z.,Zhang,L.,Lu,H.,2024.Zoomnext:A unified collaborative pyramid network for camouflaged object detec- tion. IEEE transactions on pattern analysis and machine intelligence

    2024

  34. [42]

    Medical image segmentation via cascaded attention decoding, in: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp

    Rahman, M.M., Marculescu, R., 2023. Medical image segmentation via cascaded attention decoding, in: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 6222– 6231

    2023

  35. [43]

    Ssformer:Alightweighttransformerfor semantic segmentation, in: 2022 IEEE 24th International Workshop on Multimedia Signal Processing (MMSP), IEEE

    Shi,W.,Xu,J.,Gao,P.,2022. Ssformer:Alightweighttransformerfor semantic segmentation, in: 2022 IEEE 24th International Workshop on Multimedia Signal Processing (MMSP), IEEE. pp. 1–5

    2022

  36. [44]

    A new camouflage texture evaluation method based on wssim and nature image features, in: 2010 Inter- national conference on multimedia technology, IEEE

    Song, L., Geng, W., 2010. A new camouflage texture evaluation method based on wssim and nature image features, in: 2010 Inter- national conference on multimedia technology, IEEE. pp. 1–4

    2010

  37. [45]

    Automated polyp detectionincolonoscopyvideosusingshapeandcontextinformation

    Tajbakhsh, N., Gurudu, S.R., Liang, J., 2015. Automated polyp detectionincolonoscopyvideosusingshapeandcontextinformation. IEEE transactions on medical imaging 35, 630–644

    2015

  38. [46]

    Can sam segment anything? when sam meets camouflaged object detection

    Tang, L., Xiao, H., Li, B., 2023. Can sam segment anything? when sam meets camouflaged object detection. arXiv preprint arXiv:2304.04709

    2023

  39. [47]

    Wang, L., Lu, H., Wang, Y., Feng, M., Wang, D., Yin, B., Ruan, X.,

  40. [48]

    Learningtodetectsalientobjectswithimage-levelsupervision, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 136–145

  41. [49]

    Weakly supervised salient object detection algorithm based on bounding box annotation

    WANG, Q., HUANG, X., TONG, Q., LIU, X., 2023. Weakly supervised salient object detection algorithm based on bounding box annotation. Journal of Computer Applications 43, 1910

    2023

  42. [50]

    Salient object detection in the deep learning era: An in-depth survey

    Wang,W.,Lai,Q.,Fu,H.,Shen,J.,Ling,H.,Yang,R.,2021a. Salient object detection in the deep learning era: An in-depth survey. IEEE TransactionsonPatternAnalysisandMachineIntelligence44,3239– 3259

  43. [51]

    Pyramid vision transformer: A versatile backbone for dense prediction without convolutions, in: Proceedings of the IEEE/CVF international conference on computer vision, pp

    Wang, W., Xie, E., Li, X., Fan, D.P., Song, K., Liang, D., Lu, T., Luo, P., Shao, L., 2021b. Pyramid vision transformer: A versatile backbone for dense prediction without convolutions, in: Proceedings of the IEEE/CVF international conference on computer vision, pp. 568–578

  44. [52]

    Pvt v2: Improved baselines with pyramid vision transformer

    Wang,W.,Xie,E.,Li,X.,Fan,D.P.,Song,K.,Liang,D.,Lu,T.,Luo, P., Shao, L., 2022. Pvt v2: Improved baselines with pyramid vision transformer. Computational Visual Media 8, 415–424

    2022

  45. [53]

    Ipnet: Polarization-based camouflaged object detection via dual-flow net- work

    Wang, X., Ding, J., Zhang, Z., Xu, J., Gao, J., 2024. Ipnet: Polarization-based camouflaged object detection via dual-flow net- work. Engineering Applications of Artificial Intelligence 127, 107303

    2024

  46. [54]

    F 3net:fusion,feedbackandfocus for salient object detection, in: Proceedings of the AAAI conference on artificial intelligence, pp

    Wei,J.,Wang,S.,Huang,Q.,2020. F 3net:fusion,feedbackandfocus for salient object detection, in: Proceedings of the AAAI conference on artificial intelligence, pp. 12321–12328

    2020

  47. [55]

    Cbam: Convolutional block attention module, in: Proceedings of the European conference on computer vision (ECCV), pp

    Woo, S., Park, J., Lee, J.Y., Kweon, I.S., 2018. Cbam: Convolutional block attention module, in: Proceedings of the European conference on computer vision (ECCV), pp. 3–19

    2018

  48. [56]

    Guided multi-scale refinement network for camouflaged object detection

    Xu, X., Chen, S., Lv, X., Wang, J., Hu, X., 2023. Guided multi-scale refinement network for camouflaged object detection. Multimedia Tools and Applications 82, 5785–5801

    2023

  49. [57]

    Boundary guidancenetworkforcamouflageobjectdetection

    Xu, X., Zhu, M., Yu, J., Chen, S., Hu, X., Yang, Y., 2021. Boundary guidancenetworkforcamouflageobjectdetection. ImageandVision Computing 114, 104283

    2021

  50. [58]

    Hierarchical saliency detection, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp

    Yan, Q., Xu, L., Shi, J., Jia, J., 2013. Hierarchical saliency detection, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1155–1162

    2013

  51. [59]

    Camouflaged object segmentation based on matching–recognition–refinement network

    Yan, X., Sun, M., Han, Y., Wang, Z., 2023. Camouflaged object segmentation based on matching–recognition–refinement network. IEEE Transactions on Neural Networks and Learning Systems

    2023

  52. [60]

    Saliency detection via graph-based manifold ranking, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp

    Yang, C., Zhang, L., Lu, H., Ruan, X., Yang, M.H., 2013. Saliency detection via graph-based manifold ranking, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 3166–3173

    2013

  53. [61]

    Yang, F., Zhai, Q., Li, X., Huang, R., Luo, A., Cheng, H., Fan, D.P.,

  54. [62]

    4146–4155

    Uncertainty-guided transformer reasoning for camouflaged objectdetection,in:ProceedingsoftheIEEE/CVFinternationalcon- ference on computer vision, pp. 4146–4155

  55. [63]

    Spatial coherence loss for salient and camouflaged object detection and beyond

    Yang, Z., Choy, K., Farsiu, S., 2024. Spatial coherence loss for salient and camouflaged object detection and beyond. arXiv preprint arXiv:2402.18698

    2024

  56. [64]

    Reverse cross-refinement network for camouflaged object detection

    Ye, Q., Zhou, Y., Huo, G., Liu, Y., Zhou, Y., Li, Q., 2024. Reverse cross-refinement network for camouflaged object detection. Image and Vision Computing 150, 105218

    2024

  57. [65]

    Camoformer: Masked separable attention for camouflagedobjectdetection

    Yin, B., Zhang, X., Fan, D.P., Jiao, S., Cheng, M.M., Van Gool, L., Hou, Q., 2024. Camoformer: Masked separable attention for camouflagedobjectdetection. IEEETransactionsonPatternAnalysis and Machine Intelligence

    2024

  58. [66]

    Structure-consistent weakly supervised salient object detection with local saliency coher- ence, in: Proceedings of the AAAI conference on artificial intelli- gence, pp

    Yu, S., Zhang, B., Xiao, J., Lim, E.G., 2021. Structure-consistent weakly supervised salient object detection with local saliency coher- ence, in: Proceedings of the AAAI conference on artificial intelli- gence, pp. 3234–3242

    2021

  59. [67]

    Exploring deeper! segment anything model with depth perception for camou- flaged object detection, in: Proceedings of the 32nd ACM Interna- tional Conference on Multimedia, pp

    Yu, Z., Zhang, X., Zhao, L., Bin, Y., Xiao, G., 2024. Exploring deeper! segment anything model with depth perception for camou- flaged object detection, in: Proceedings of the 32nd ACM Interna- tional Conference on Multimedia, pp. 4322–4330

    2024

  60. [68]

    Dual-constraint coarse-to-fine network for camouflaged object detection

    Yue, G., Xiao, H., Xie, H., Zhou, T., Zhou, W., Yan, W., Zhao, B., Wang, T., Jiang, Q., 2023. Dual-constraint coarse-to-fine network for camouflaged object detection. IEEE Transactions on Circuits and Systems for Video Technology

    2023

  61. [69]

    Weakly- supervised salient object detection via scribble annotations, in: Pro- ceedingsoftheIEEE/CVFconferenceoncomputervisionandpattern recognition, pp

    Zhang, J., Yu, X., Li, A., Song, P., Liu, B., Dai, Y., 2020. Weakly- supervised salient object detection via scribble annotations, in: Pro- ceedingsoftheIEEE/CVFconferenceoncomputervisionandpattern recognition, pp. 12546–12555

    2020

  62. [70]

    Learning camouflaged object detection from noisy pseudo label, in: European Conference on Computer Vision, Springer

    Zhang, J., Zhang, R., Shi, Y., Cao, Z., Liu, N., Khan, F.S., 2025. Learning camouflaged object detection from noisy pseudo label, in: European Conference on Computer Vision, Springer. pp. 158–174

    2025

  63. [71]

    Image super-resolutionusingverydeepresidualchannelattentionnetworks, in: Proceedings of the European conference on computer vision (ECCV), pp

    Zhang, Y., Li, K., Li, K., Wang, L., Zhong, B., Fu, Y., 2018. Image super-resolutionusingverydeepresidualchannelattentionnetworks, in: Proceedings of the European conference on computer vision (ECCV), pp. 286–301

    2018

  64. [72]

    Zhang,Y.,Zhou,T.,Wang,S.,Liang,P.,Zhang,Y.,Chen,D.Z.,2023. Input augmentation with sam: Boosting medical image segmentation with segmentation foundation model, in: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer. pp. 129–139

    2023

  65. [73]

    IEEEtransactions on image processing 30, 1949–1961

    Zhang,Z.,Lin,Z.,Xu,J.,Jin,W.D.,Lu,S.P.,Fan,D.P.,2021.Bilateral attentionnetworkforrgb-dsalientobjectdetection. IEEEtransactions on image processing 30, 1949–1961. Xia Li et al.:Preprint submitted to ElsevierPage 17 of 18 Weakly Supervised Camouflaged Object Detection Based on ...

    2021

  66. [74]

    Focusdiffuser: Perceiving local disparities for camouflaged object detection, in: European Conference on Computer Vision, Springer

    Zhao,J.,Li,X.,Yang,F.,Zhai,Q.,Luo,A.,Jiao,Z.,Cheng,H.,2024. Focusdiffuser: Perceiving local disparities for camouflaged object detection, in: European Conference on Computer Vision, Springer. pp. 181–198

    2024

  67. [75]

    Bilateral reference for high-resolution dichotomous image segmentation

    Zheng, P., Gao, D., Fan, D.P., Liu, L., Laaksonen, J., Ouyang, W., Sebe, N., 2024. Bilateral reference for high-resolution dichotomous image segmentation. CAAI Artificial Intelligence Research 3

    2024

  68. [76]

    I can find you! boundary-guided separated attention network for camouflaged object detection, in: Proceedings of the AAAI conference on artificial intelligence, pp

    Zhu, H., Li, P., Xie, H., Yan, X., Liang, D., Chen, D., Wei, M., Qin, J., 2022. I can find you! boundary-guided separated attention network for camouflaged object detection, in: Proceedings of the AAAI conference on artificial intelligence, pp. 3608–3616. Xia Li et al.:Preprin...

    2022

Pith tools

Reviewed June 29, 2026 · model on record in the stance chip above.