Pith. sign in

REVIEW 4 major objections 4 minor 50 references

SCOUT: Semi-supervised Camouflaged Object Detection by Utilizing Text and Adaptive Data Selection

T0 review · 4 major / 4 minor · reviewed 2026-08-05 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read Semi-supervised camouflaged object detection reaches state-of-the-art accuracy by fusing text knowledge with adaptive selection of unlabeled data.

desk verdict Promising combination of text guidance and adaptive selection for semi-supervised COD, but the abstract alone leaves the core SOTA claim and annotation budget unverifiable. read the letter →

arxiv 2508.17843 v1 pith:2FGY5A33 submitted 2025-08-25 cs.CV

Weiqi Yan , Lvhai Chen , Shengchuan Zhang , Yan Zhang , Liujuan Cao This is my paper · ORCID
classification cs.CV
keywords camouflagedobjectdetectionsemi-supervisedlearningtext-visualfusionadaptivedataselectionactiveannotationefficiencyRefTextCOD
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

Pixel-level annotations for camouflaged objects are expensive and hard to come by. This paper argues that previous semi-supervised approaches still underuse the unlabeled images, so it builds a two-part pipeline: an adaptive data selection module that chooses which unlabeled images are worth labeling, and a text fusion module that injects camouflage-related language knowledge into the visual detector. To support the text side, the authors construct a new dataset (RefTextCOD) that pairs camouflaged images with descriptive text. Their experiments report that the combined approach surpasses earlier semi-supervised COD methods, achieving state-of-the-art performance while relying on only a small set of labeled data. If correct, the result means language priors and smart sample selection can significantly lower the annotation cost in finding animals or objects hidden in plain sight.

What carries the argument

ADAS (Adaptive Data Augment and Selection): an adversarial augmentation and sampling module that scores unlabeled images and selects the ones most valuable for annotation. TFM (Text Fusion Module): a text-visual interaction module that injects camouflage-related knowledge (derived from the new RefTextCOD dataset, which pairs images with descriptive text) into the detection features. The central load-bearing mechanism is the combination—ADAS decides what to annotate next, and TFM makes the most of those annotations plus the text priors, with RefTextCOD supplying the textual knowledge that the fusion module consumes.

What would settle it

A controlled experiment where ADAS is replaced by random sampling of the same number of unlabeled images, keeping TFM and all training settings identical; if the final detection scores on standard COD benchmarks are not clearly higher for ADAS, the claimed benefit of adaptive data selection is not supported.

Watch

Extended reading notes

Core claim

On its own terms, the paper claims that semi-supervised camouflaged object detection can be pushed markedly beyond previous methods by addressing two under-exploited resources: the unlabeled image pool and natural-language descriptions of camouflage. The Adaptive Data Augment and Selection (ADAS) module is designed to identify the most annotation-worthy unlabeled images through an adversarial augmentation and sampling strategy, and the Text Fusion Module (TFM) lets the detector absorb camouflage-related textual knowledge through text-visual interaction. Together with the newly built RefTextCOD dataset, which supplies the text supervision signal, the full SCOUT pipeline is reported to outperf

Load-bearing premise

The central gamble is that the adversarial sampling in ADAS picks unlabeled images whose annotation genuinely improves the model, rather than merely hard or noisy ones; if selection adds no labeling value over random choice, the method reduces to ordinary semi-supervised learning plus a text branch.

Editorial extensions

If this is right

  • If the claims hold, camouflaged object detection can be deployed at scale with a small labeling budget, since the model chooses what to annotate and learns from text cues.
  • The text fusion branch suggests that other low-annotation vision tasks, such as rare-species monitoring or defect detection, might benefit from pairing images with cheap language descriptions instead of expensive pixel masks.
  • The adversarial sampling criterion, if it tracks annotation value, could be reused as an active-learning strategy in dense prediction beyond COD.
  • The gap between semi-supervised and fully supervised COD performance should narrow, making the semi-supervised route a practical default when full masks are not available.

Reading between the lines

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

  • An implied testable extension is to feed the ADAS-selected images to a human annotator and measure downstream gain against an equal-size random sample; this would isolate the selection module's contribution.
  • The text descriptions in RefTextCOD could be replaced by automatically generated captions, turning the pipeline into a fully unlabeled-text method that removes the need for any manual text annotation.
  • Because camouflage depends on scene and habitat, text priors may generalize to new benchmarks better than pixel-level pseudo-labels, a property the paper does not directly quantify.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 4 minor

Summary. The paper proposes SCOUT, a semi-supervised camouflaged object detection (COD) method combining an Adaptive Data Augment and Selection (ADAS) module and a Text Fusion Module (TFM), plus a new author-built dataset, RefTextCOD. ADAS is said to select valuable unlabeled data for annotation through adversarial augmentation and sampling; TFM fuses camouflage-related text knowledge with visual features. The abstract claims that SCOUT surpasses previous semi-supervised COD methods and achieves state-of-the-art performance, but provides no benchmark names, quantitative margins, ablations, or statistical significance information. The paper promises code release at a GitHub URL.

Significance. If the claimed results hold, SCOUT would advance semi-supervised COD by reducing annotation cost while leveraging text priors, an underexplored direction in this field. The central novelty—adaptive data selection coupled with text–visual fusion—could be valuable beyond COD. However, the manuscript currently offers no verifiable evidence. The abstract's lack of quantitative evaluation, the potential annotation-budget confound, and the self-described construction of RefTextCOD 'to adapt to this work' make it impossible to assess whether the claimed superiority is real or an artifact. The intended contribution is plausible, but the current presentation is not sufficient for a rigorous empirical claim.

major comments (4)
  1. [Abstract] The abstract's central claim—'surpasses previous semi-supervised methods ... and achieves state-of-the-art performance'—is stated with no supporting numbers: no benchmark (e.g., COD10K, NC4K, CAMO), no metrics (e.g., mIoU, S-measure, E-measure), no margins over prior methods, and no error bars or significance tests. An empirical claim of this strength must be accompanied by at least a reference to a results table or quantitative summary in the abstract. As written, the claim is unverifiable.
  2. [Abstract; ADAS module design] The ADAS module 'selects valuable data for annotation.' If selected unlabeled images are then human-annotated and added to the labeled pool, the method's effective annotation budget grows relative to conventional semi-supervised baselines that train only on the initial labeled set. The abstract does not state that all compared methods use the same annotation budget. The reported SOTA margin could then be explained entirely by extra labeled examples rather than by better use of unlabeled data or text priors. The paper must clarify whether baselines receive the same additional annotations, and must include an ablation replacing ADAS with random selection while holding the annotation budget fixed.
  3. [RefTextCOD dataset description] The abstract says the new dataset RefTextCOD was built 'to adapt to this work.' This creates a circularity risk: the camouflage-related text knowledge used in TFM may encode the same concepts used to construct the benchmark, and the method may be tuned to the dataset's quirks. The manuscript must show that SCOUT generalizes to established COD benchmarks, and must demonstrate that RefTextCOD is a valid evaluation set independent of the design of TFM. This is a load-bearing validity concern for the SOTA claim.
  4. [Overall experimental evaluation] No ablation isolates the contributions of ADAS and TFM. The claim that both modules 'further leverage' data requires at least: (i) SCOUT without TFM, (ii) SCOUT without ADAS, (iii) ADAS with random sampling, and (iv) TFM with generic text versus camouflage-specific text. Without these controls, the individual contributions of the modules are not established, and the paper's title's emphasis on text and adaptive data selection is not justified.
minor comments (4)
  1. [Abstract] The module is named 'ADAS' in one place and 'ADSA' in another. Please standardize the acronym.
  2. [Abstract] The phrase 'valuable data' is undefined. Specify the selection criterion (e.g., uncertainty, adversarial confidence) and the annotation budget (number of selected images/pixels).
  3. [General] The GitHub link is appreciated, but the manuscript should state whether code will include training/evaluation scripts and pretrained models to enable reproducibility.
  4. [General] If RefTextCOD is introduced, the full paper should provide dataset statistics, annotation guidelines, and a comparison to existing COD benchmarks in terms of image diversity and camouflage difficulty.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity; the abstract reports an empirical method without a derivation chain that reduces to its inputs.

full rationale

The provided manuscript excerpt is an abstract describing an empirical semi-supervised COD method (SCOUT) with two modules (ADAS and TFM) and a new dataset (RefTextCOD). No equations or formal derivation chain are presented, so there is no step in which a derived quantity is equivalent to an input by construction. The ADAS module 'selects valuable data for annotation' could raise a question about annotation-budget fairness when comparing to fixed-label baselines, but that is an experimental-design concern, not circularity under the definitions: ADAS is not fitted to the target metric, and the abstract does not claim to predict a quantity that was used as a fit target. The RefTextCOD dataset is described as built 'to adapt to this work,' but without evidence that it is used as the sole test set or that the text priors are evaluated only on it, this does not constitute a self-definitional loop. There are no self-citations, uniqueness imports, or ansatz smuggled in via citation in the provided text. Therefore the paper, based on available evidence, is self-contained in its claims and has no significant circularity.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

For an empirical ML paper, the de facto free parameters are training choices: selection budget, text prompt templates, and optimization hyperparameters, none disclosed in the abstract. The central claim additionally rests on two domain assumptions about the ADAS selection criterion and the transferability of text priors, plus the validity of the author-built RefTextCOD benchmark.

free parameters (3)
  • ADAS selection budget and sampling criterion (number/threshold of samples chosen for annotation) = not reported in abstract
    The adaptive selection strategy must set how many unlabeled samples get annotated and by what adversarial score; this budget directly controls the labeled/unlabeled ratio and hence the reported gains.
  • Text prompt templates / camouflage-related knowledge wording in TFM = not reported in abstract
    Handcrafted text descriptions are a design choice; performance depends on their quality and coverage, and they plausibly encode the same knowledge used to build RefTextCOD.
  • Network hyperparameters (loss weights, augmentation strengths, training schedule) = not reported in abstract
    Standard training choices that co-determine the final SOTA numbers; cannot be audited from the abstract.
assumptions (4)
  • domain assumption ADAS's adversarial augment and sampling strategy identifies unlabeled samples whose annotation improves the model most.
    The abstract asserts the module 'selects valuable data for annotation' but provides no ablation, oracle study, or comparison to random sampling; the entire efficiency claim rests on this correlation.
  • domain assumption Camouflage-related text knowledge transferred through text-visual interaction improves pixel-level COD predictions.
    TFM assumes text descriptions carry usable supervision for dense prediction; no evidence is given in the abstract that this transfer works better than visual-only training on standard COD benchmarks.
  • domain assumption Unlabeled and labeled COD images come from the same distribution, and standard semi-supervised training assumptions hold.
    Shared inheritance of all semi-supervised COD frameworks; the paper does not state or test it.
  • ad hoc to paper RefTextCOD is a valid, unbiased benchmark for evaluating COD performance.
    The dataset is constructed by the authors 'to adapt to this work'; its annotation protocol, split design, and text description sourcing are not described in the abstract, and evaluating on it can bias results.

how reviews work

0 comments
Cite this review

Pith. "Pith review of SCOUT: Semi-supervised Camouflaged Object Detection by Utilizing Text and Adaptive Data Selection." pith.science (2026). https://pith.science/paper/2FGY5A33

@misc{pith2026250817843,
  author       = {Pith},
  title        = {Pith review of: SCOUT: Semi-supervised Camouflaged Object Detection by Utilizing Text and Adaptive Data Selection},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2FGY5A33}},
  note         = {Machine review of arXiv:2508.17843}
}
read the original abstract

The difficulty of pixel-level annotation has significantly hindered the development of the Camouflaged Object Detection (COD) field. To save on annotation costs, previous works leverage the semi-supervised COD framework that relies on a small number of labeled data and a large volume of unlabeled data. We argue that there is still significant room for improvement in the effective utilization of unlabeled data. To this end, we introduce a Semi-supervised Camouflaged Object Detection by Utilizing Text and Adaptive Data Selection (SCOUT). It includes an Adaptive Data Augment and Selection (ADAS) module and a Text Fusion Module (TFM). The ADSA module selects valuable data for annotation through an adversarial augment and sampling strategy. The TFM module further leverages the selected valuable data by combining camouflage-related knowledge and text-visual interaction. To adapt to this work, we build a new dataset, namely RefTextCOD. Extensive experiments show that the proposed method surpasses previous semi-supervised methods in the COD field and achieves state-of-the-art performance. Our code will be released at https://github.com/Heartfirey/SCOUT.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

50 extracted references · 45 canonical work pages

  1. [1]

    Queries and concept learning

    Dana Angluin. Queries and concept learning. ML , 2(4):319–342, apr 1988

    work page 1988

  2. [2]

    Argamon-Engelson and I

    S. Argamon-Engelson and I. Dagan. Committee-based sample selection for probabilistic classifiers. JAIR , 11:335–360, November 1999

    work page 1999

  3. [3]

    Qwen-vl: A versatile vision-language model for understanding, localization, text reading, and beyond, 2023

    Jinze Bai, Shuai Bai, Shusheng Yang, Shijie Wang, Sinan Tan, Peng Wang, Junyang Lin, Chang Zhou, and Jingren Zhou. Qwen-vl: A versatile vision-language model for understanding, localization, text reading, and beyond, 2023

    2023

  4. [4]

    Cannaday, Curt H

    Alan B. Cannaday, Curt H. Davis, and Trevor M. Bajkowski. Detection of camouflage-covered military objects using high-resolution multi-spectral satellite imagery. In IGARSS 2023 , pages 5766--5769, Pasadena, CA, USA, 2023. IEEE

    work page 2023

  5. [5]

    Semi-supervised semantic segmentation with cross pseudo supervision

    Xiaokang Chen, Yuhui Yuan, Gang Zeng, and Jingdong Wang. Semi-supervised semantic segmentation with cross pseudo supervision. In CVPR 2021 , pages 2613--2622, 2021

    work page 2021

  6. [6]

    Camouflaged object detection via context-aware cross-level fusion

    Geng Chen, Si-Jie Liu, Yu-Jia Sun, Ge-Peng Ji, Ya-Feng Wu, and Tao Zhou. Camouflaged object detection via context-aware cross-level fusion. IEEE TCSVT , 32:1--1, 10 2022

    work page 2022

  7. [7]

    Softmatch: Addressing the quantity-quality trade-off in semi-supervised learning, 2023

    Hao Chen, Ran Tao, Yue Fan, Yidong Wang, Jindong Wang, Bernt Schiele, Xing Xie, Bhiksha Raj, and Marios Savvides. Softmatch: Addressing the quantity-quality trade-off in semi-supervised learning, 2023

    work page 2023

  8. [8]

    Large model based referring camouflaged object detection, 2023

    Shupeng Cheng, Ge-Peng Ji, Pengda Qin, Deng-Ping Fan, Bowen Zhou, and Peng Xu. Large model based referring camouflaged object detection, 2023

    work page 2023

Show all 50 references
  1. [9]

    Structure-measure: A new way to evaluate foreground maps, 2017

    Deng-Ping Fan, Ming-Ming Cheng, Yun Liu, Tao Li, and Ali Borji. Structure-measure: A new way to evaluate foreground maps, 2017

    2017

  2. [10]

    Enhanced-alignment measure for binary foreground map evaluation, 2018

    Deng-Ping Fan, Cheng Gong, Yang Cao, Bo Ren, Ming-Ming Cheng, and Ali Borji. Enhanced-alignment measure for binary foreground map evaluation, 2018

    2018

  3. [11]

    Camouflaged object detection

    Deng-Ping Fan, Ge-Peng Ji, Guolei Sun, Ming-Ming Cheng, Jianbing Shen, and Ling Shao. Camouflaged object detection. In CVPR 2020 , pages 2774--2784, 2020

    2020

  4. [12]

    Concealed object detection

    Deng-Ping Fan, Ge-Peng Ji, Ming-Ming Cheng, and Ling Shao. Concealed object detection. IEEE TPAMI , 44(10):6024--6042, 2022

    2022

  5. [13]

    Semi-supervised camouflaged object detection from noisy data

    Yuanbin Fu, Jie Ying, Houlei Lv, and Xiaojie Guo. Semi-supervised camouflaged object detection from noisy data. In ACMM MM 2024 , page 4766–4775, New York, NY, USA, 2024. Association for Computing Machinery

    2024

  6. [14]

    Semi-supervised learning by entropy minimization

    Yves Grandvalet and Yoshua Bengio. Semi-supervised learning by entropy minimization. In NIPS 2004 , page 529–536, 2004

    2004

  7. [15]

    Relax image-specific prompt requirement in sam: A single generic prompt for segmenting camouflaged objects

    Jian Hu, Jiayi Lin, Shaogang Gong, and Weitong Cai. Relax image-specific prompt requirement in sam: A single generic prompt for segmenting camouflaged objects. In AAAI 2025 , volume 38, pages 12511--12518, 2025

    2025

  8. [16]

    Deep gradient learning for efficient camouflaged object detection

    Ge-Peng Ji, Deng-Ping Fan, Yu-Cheng Chou, Dengxin Dai, Alexander Liniger, and Luc Van Gool. Deep gradient learning for efficient camouflaged object detection. MIR , 20:92--108, 2023

    2023

  9. [17]

    Segment, magnify and reiterate: Detecting camouflaged objects the hard way

    Qi Jia, Shuilian Yao, Yu Liu, Xin Fan, Risheng Liu, and Zhongxuan Luo. Segment, magnify and reiterate: Detecting camouflaged objects the hard way. In CVPR 2022 , pages 4703--4712, 2022

    2022

  10. [18]

    King, Ken E

    Ross D. King, Ken E. Whelan, Ffion Mair Jones, Philip G. K. Reiser, Christopher H. Bryant, Stephen H. Muggleton, Douglas B. Kell, and Stephen G. Oliver. Functional genomic hypothesis generation and experimentation by a robot scientist. Nature , 427:247--252, 2004

    2004

  11. [19]

    Camoteacher: Dual-rotation consistency learning for semi-supervised camouflaged object detection

    Xunfa Lai, Zhiyu Yang, Jie Hu, Shengchuan Zhang, Liujuan Cao, Guannan Jiang, Zhiyu Wang, Songgan Zhang, and Rongrong Ji. Camoteacher: Dual-rotation consistency learning for semi-supervised camouflaged object detection. In ECCV 2024 , 2024

    2024

  12. [20]

    Nguyen, Zhongliang Nie, Minh-Triet Tran, and Akihiro Sugimoto

    Trung-Nghia Le, Tam V. Nguyen, Zhongliang Nie, Minh-Triet Tran, and Akihiro Sugimoto. Anabranch network for camouflaged object segmentation. CVIU , 184:45–56, July 2019

    2019

  13. [21]

    Mildetr: Detection transformer for military camouflaged target detection

    Bing Li, Rongqian Zhou, Lu Yang, Qiwen Wang, and Huang Chen. Mildetr: Detection transformer for military camouflaged target detection. IEEE Access , 12:26163--26174, 2024

    2024

  14. [22]

    Jiaying Lin, Xin Tan, Ke Xu, Lizhuang Ma, and Rynson W. H. Lau. Frequency-aware camouflaged object detection. TOMM , 19(2), March 2023

    2023

  15. [23]

    Swin transformer: Hierarchical vision transformer using shifted windows

    Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. Swin transformer: Hierarchical vision transformer using shifted windows. In ICCV 2021 , pages 9992--10002, 2021

    2021

  16. [24]

    Camouflaged instance segmentation via explicit de-camouflaging

    Naisong Luo, Yuwen Pan, Rui Sun, Tianzhu Zhang, Zhiwei Xiong, and Feng Wu. Camouflaged instance segmentation via explicit de-camouflaging. In CVPR 2023 , pages 17918--17927, 2023

    2023

  17. [25]

    How to evaluate foreground maps

    Ran Margolin, Lihi Zelnik-Manor, and Ayellet Tal. How to evaluate foreground maps. In CVPR 2014 , pages 248--255, 2014

    2014

  18. [26]

    Camouflaged object segmentation with distraction mining

    Haiyang Mei, Ge-Peng Ji, Ziqi Wei, Xin Yang, Xiaopeng Wei, and Deng-Ping Fan. Camouflaged object segmentation with distraction mining. In CVPR 2021 , 2021

    2021

  19. [27]

    Active teacher for semi-supervised object detection

    Peng Mi, Jianghang Lin, Yiyi Zhou, Yunhang Shen, Gen Luo, Xiaoshuai Sun, Liujuan Cao, Rongrong Fu, Qiang Xu, and Rongrong Ji. Active teacher for semi-supervised object detection. In CVPR 2022 , pages 14462--14471, 2022

    2022

  20. [28]

    Gpt-4 technical report, 2024

    OpenAI. Gpt-4 technical report, 2024

    2024

  21. [29]

    Zoom in and out: A mixed-scale triplet network for camouflaged object detection

    Youwei Pang, Xiaoqi Zhao, Tian-Zhu Xiang, Lihe Zhang, and Huchuan Lu. Zoom in and out: A mixed-scale triplet network for camouflaged object detection. In CVPR 2022 , 2022

    2022

  22. [30]

    Zoomnext: A unified collaborative pyramid network for camouflaged object detection, 2023

    Youwei Pang, Xiaoqi Zhao, Tian-Zhu Xiang, Lihe Zhang, and Huchuan Lu. Zoomnext: A unified collaborative pyramid network for camouflaged object detection, 2023

    2023

  23. [31]

    Saliency filters: Contrast based filtering for salient region detection

    Federico Perazzi, Philipp Krähenbühl, Yael Pritch, and Alexander Hornung. Saliency filters: Contrast based filtering for salient region detection. In CVPR 2012 , pages 733--740, 2012

    2012

  24. [32]

    Learning transferable visual models from natural language supervision, 2021

    Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever. Learning transferable visual models from natural language supervision, 2021

    2021

  25. [33]

    Deep texture-aware features for camouflaged object detection

    Jingjing Ren, Xiaowei Hu, Lei Zhu, Xuemiao Xu, Yangyang Xu, Weiming Wang, Zijun Deng, and Pheng-Ann Heng. Deep texture-aware features for camouflaged object detection. IEEE TCSVT , 33(3):1157--1167, 2023

    2023

  26. [34]

    Active learning literature survey

    Burr Settles. Active learning literature survey. In None , 2009

    2009

  27. [35]

    A simple semi-supervised learning framework for object detection

    Kihyuk Sohn, Zizhao Zhang, Chun-Liang Li, Han Zhang, Chen-Yu Lee, and Tomas Pfister. A simple semi-supervised learning framework for object detection. In arXiv:2005.04757 , 2020

    2005 arXiv

  28. [36]

    Boundary-guided camouflaged object detection

    Yujia Sun, Shuo Wang, Chenglizhao Chen, and Tian-Zhu Xiang. Boundary-guided camouflaged object detection. In Lud De Raedt, editor, IJCAI 2022 , pages 1335--1341, 7 2022. Main Track

    2022

  29. [37]

    Teachaugment: Data augmentation optimization using teacher knowledge

    Teppei Suzuki. Teachaugment: Data augmentation optimization using teacher knowledge. In CVPR 2022 , pages 10894--10904, New Orleans, LA, USA, 2022. IEEE

    2022

  30. [38]

    Semi-supervised semantic segmentation using unreliable pseudo-labels

    Yuchao Wang, Haochen Wang, Yujun Shen, Jingjing Fei, Wei Li, Guoqiang Jin, Liwei Wu, Rui Zhao, and Xinyi Le. Semi-supervised semantic segmentation using unreliable pseudo-labels. In CVPR 2022 , pages 4238--4247, 2022

    2022

  31. [39]

    Cascaded partial decoder for fast and accurate salient object detection, 2019

    Zhe Wu, Li Su, and Qingming Huang. Cascaded partial decoder for fast and accurate salient object detection, 2019

    2019

  32. [40]

    Detection and identification of camouflaged targets using hyperspectral and lidar data

    Deepti Yadav, Kailash Tiwari, Manoj Arora, and Jayanta Ghosh. Detection and identification of camouflaged targets using hyperspectral and lidar data. Defence science journal , 10 2018

    2018

  33. [41]

    Jinnan Yan, Trung-Nghia Le, Khanh-Duy Nguyen, Minh-Triet Tran, Thanh-Toan Do, and Tam V. Nguyen. Mirrornet: Bio-inspired camouflaged object segmentation. IEEE Access , 9:43290--43300, 2021

    2021

  34. [42]

    Mutual graph learning for camouflaged object detection

    Qiang Zhai, Xin Li, Fan Yang, Chenglizhao Chen, Hong Cheng, and Deng-Ping Fan. Mutual graph learning for camouflaged object detection. In CVPR 2021 , 2021

    2021

  35. [43]

    Unsupervised camouflaged object segmentation as domain adaptation

    Yi Zhang and Chengyi Wu. Unsupervised camouflaged object segmentation as domain adaptation. In ICCV 2023 Workshops , pages 4334--4344, October 2023

    2023

  36. [44]

    Preynet: Preying on camouflaged objects

    Miao Zhang, Shuang Xu, Yongri Piao, Dongxiang Shi, Shusen Lin, and Huchuan Lu. Preynet: Preying on camouflaged objects. ACM MM 2022 , 2022

    2022

  37. [45]

    Referring camouflaged object detection, 2023

    Xuying Zhang, Bowen Yin, Zheng Lin, Qibin Hou, Deng-Ping Fan, and Ming-Ming Cheng. Referring camouflaged object detection, 2023

    2023

  38. [46]

    Learning camouflaged object detection from noisy pseudo label

    Jin Zhang, Ruiheng Zhang, Yanjiao Shi, Zhe Cao, Nian Liu, and Fahad Shahbaz Khan. Learning camouflaged object detection from noisy pseudo label. In ECCV 2024 , pages 158--174, 2024

    2024

  39. [47]

    Bilateral reference for high-resolution dichotomous image segmentation

    Peng Zheng, Dehong Gao, Deng-Ping Fan, Li Liu, Jorma Laaksonen, Wanli Ouyang, and Nicu Sebe. Bilateral reference for high-resolution dichotomous image segmentation. CAAI 2024 Artificial Intelligence Research , 2024

    2024

  40. [48]

    Detecting camouflaged object in frequency domain

    Yijie Zhong, Bo Li, Lv Tang, Senyun Kuang, Shuang Wu, and Shouhong Ding. Detecting camouflaged object in frequency domain. In CVPR 2022 , pages 4494--4503, 2022

    2022

  41. [49]

    Inferring camouflaged objects by texture-aware interactive guidance network

    Jinchao Zhu, Xiaoyu Zhang, Shuo Zhang, and Junnan Liu. Inferring camouflaged objects by texture-aware interactive guidance network. In AAAI 2021 , 2021

    2021

  42. [50]

    write newline

    " write newline "" before.all 'output.state := FUNCTION fin.entry add.period write newline FUNCTION new.block output.state before.all = 'skip after.block 'output.state := if FUNCTION new.sentence output.state after.block = 'skip output.state before.all = 'skip after.sentence '...

Pith tools

Reviewed August 5, 2026 · model on record in the stance chip above.