Pith. sign in

REVIEW 3 major objections 5 minor 1 cited by

CGCOD: Class-Guided Camouflaged Object Detection

T0 review · 3 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

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

Pith's one-line read CGNet uses class-name text prompts to steer attention, claiming consistent state-of-the-art results on four camouflaged-object detection benchmarks and reporting that the prompt generator lifts existing detectors by 4.4–19.7%.

desk verdict A useful incremental contribution with a real dataset, but the benchmark claims rest on inconsistent dataset counts and a circular hard/normal split. read the letter →

arxiv 2412.18977 v2 pith:F7A4XOA6 submitted 2024-12-25 cs.CV cs.LG

Chenxi Zhang , Qing Zhang , Jiayun Wu , Youwei Pang This is my paper · ORCID
classification cs.CVcs.LG
keywords class-guidedcamouflagedobjectdetectionCamoClassdatasettext-guidedsegmentationCLIPpromptgeneratormulti-modalCODplug-and-playmodulezero-shotgeneralization
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 proposes a new task, class-guided camouflaged object detection, in which the model receives not just an image but also the class name of the camouflaged object it should find. The authors argue that pure visual COD is unstable because camouflaged objects are defined by semantic difference, not visual contrast, and that explicit class text supplies the missing cue. To support this, they build CamoClass, a 10,523-image dataset with class labels, and design CGNet, a detector whose class prompt generator turns CLIP text embeddings into features that steer spatial and channel attention. The paper claims CGNet beats all compared COD and referring-segmentation methods on CAMO, COD10K, NC4K, and CHAMELEON, by an average 5.5% over the second-best, and that the prompt generator plugs into existing detectors and improves them.

What carries the argument

The class prompt generator (CPG) is the load-bearing component: it combines a frozen CLIP text encoder, multi-level visual features from a frozen CLIP visual encoder, a cross-modal multi-head attention module that aligns text tokens with pixels, and a multi-level visual collaboration module (MVCM) that progressively refines the fused features. The class-guided detector (CGD) then uses a semantics consistency module (SCM) that applies the prompt feature as an attention guide in two stages—spatial localization followed by channel refinement—to keep the network focused on class-relevant foreground.

What would settle it

Recompute the CamoClass sample counts and the four test sets' sizes from the released data; if the totals cannot be reconciled with 10,523 images or with the reported train/test split, the benchmark numbers in Table 1 cannot be reproduced. A second check is to rerun CGNet with class prompts replaced by a generic word ('object') or a wrong class: if accuracy does not drop on the hard subset, the class-text guidance is not the active ingredient.

Watch

Extended reading notes

Core claim

The central claim is that class-level textual knowledge, injected through a plug-and-play class prompt generator, resolves the perceptual ambiguity that limits visual-only COD models. In CGNet, a frozen CLIP text encoder and a frozen CLIP visual encoder feed a cross-modal attention module that produces class prompt features; these features are fused with multi-level visual features and then guide a semantics consistency module that first localizes class-relevant regions spatially and then refines channel information. The paper reports consistent state-of-the-art results on four datasets and shows that appending the class prompt generator raises existing COD detectors' scores (for example, 19.73% for SINet, 13.3% for BGNet, 12.68% for PFNet, and 4.43% for FSPNet), attributing the gains to the model's ability to suppress false positives and to recover occluded or structurally degraded objects.

Load-bearing premise

The paper's benchmark and generalization claims rest on the CamoClass dataset being complete and internally consistent, but the reported totals do not match (10,523 samples versus 4,040 training plus 8,615 test), so the exact composition and splits are the load-bearing premise.

Editorial extensions

If this is right

  • If class guidance is the missing cue, COD becomes a multimodal task, and future benchmarks would need class annotations to stay comparable with this new paradigm.
  • Existing COD detectors do not need redesign: the CPG can be appended to them and retrained on CamoClass, yielding reported gains of 4.4% to 19.7% on the tested baselines.
  • The seen/unseen split of CamoClass (Cseen and Cunseen) provides a way to measure zero-shot generalization of COD models, and the paper reports CGNet leads on both splits, especially on Cunseen.
  • The class prompt helps most in hard cases—multi-object scenes, occlusion, edge-similar backgrounds, and small objects—where the paper shows visual-only methods fail.
  • The proposed loss and multi-level supervision tie the prompt generator, the detector, and the final prediction into one trainable pipeline, making the class-guidance effect a direct outcome of optimization rather than a post-hoc refinement.

Reading between the lines

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

  • If the class-guidance mechanism is as transferable as claimed, the same CPG idea could be applied to other ambiguous segmentation domains, such as polyp boundaries in medical images or crop/weed separation in agriculture, where the semantic class is known but visual contrast is low.
  • The paper's split of test images into 'hard' and 'normal' based on its own detector's scores (Sm >= 0.9) is self-referential; a cleaner test would use human-rated camouflage difficulty, which would make the claimed benefit on hard scenes more independent of the model being evaluated.
  • The reported dataset arithmetic (10,523 samples versus 4,040 training plus 8,615 test) needs to be reconciled; if the true test set is 6,473 images, the average 5.5% margin could shift when recomputed on a consistent split.
  • A direct next experiment would be to replace class prompts with generic or incorrect class names; if CGNet's advantage persists, the performance gain is coming from the extra training data and architecture, not from class semantics.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 5 minor

Summary. The paper introduces class-guided camouflaged object detection (CGCOD), a new task in which class-level textual information is used to guide camouflaged object detection. To support this task, the authors construct CamoClass, a dataset assembled from CAMO, COD10K, NC4K, and CHAMELEON with class annotations added. They also propose CGNet, a framework consisting of a plug-and-play class prompt generator (CPG) built on frozen CLIP features and a class-guided detector (CGD), together with the CamoClass benchmark. The main experiments compare CGNet and several existing COD/RIS methods with and without the CPG on four test sets, and also report Cseen/Cunseen and hard/normal subset analyses.

Significance. If the numbers are correct, the paper makes a useful contribution by adding a class-guided setting to COD, providing a class-annotated dataset, and showing that a plug-and-play text prompt module can improve existing detectors. The evaluation is conducted on external benchmarks, the training/test separation follows the standard COD paradigm, and the code is publicly linked. However, the quantitative foundation currently has unresolved inconsistencies: the dataset totals in Section 3 and Section 5.1 do not match each other or the sums in Tables 1 and 2, and the headline 'average 5.5%' improvement is not reproducible from Table 1. These issues must be fixed before the benchmark claims can be fully trusted.

major comments (3)
  1. [§3, §5.1, Tables 1 and 2] The dataset statistics are internally inconsistent. Section 3 states that CamoClass contains 10,523 samples, while Section 5.1 reports 4,040 training samples and 8,615 test samples, which sum to 12,655. Table 1 lists test-set sizes of 250 + 2,026 + 4,121 + 76 = 6,473, and Table 2 uses 6,109 + 364 = 6,473. The number 8,615 therefore does not equal the union of the four test sets, and 10,523 does not equal 4,040 + 6,473 = 10,513. Please correct the counts and specify exactly which images belong to CamoClass, how the 44 multi-class samples are counted, and how the train/test split is formed; until this is resolved, the benchmark comparisons and the Cseen/Cunseen analysis rest on unverified statistics.
  2. [§5.2] The claim that 'CGNet are on average 5.5% better than the second best method' is not derivable from Table 1. Neither 'second best' nor 'on average' is defined in the text or the table. For example, averaging the relative S-measure improvements over the four datasets gives roughly 1.1–1.6% per dataset, not 5.5%. Please define the reference method, the metric, whether the percentage is absolute or relative, and show the arithmetic that produces 5.5% from Table 1; otherwise the headline quantitative claim is not verifiable.
  3. [§5.2] The reported plug-and-play improvements of the CPG are not traceable from Table 1. The text states that CPG improves SINet by 19.73%, PFNet by 12.68%, BGNet by 13.3%, and FSPNet by 4.43%. Under an obvious interpretation, such as averaging S-measure gains across the four datasets, SINet+ improves over SINet by about 7.8%, not 19.73%. Please specify the metric and the exact computation for each percentage, or revise the claim so that it can be checked against the table.
minor comments (5)
  1. [§3, Figure 2] The manuscript text contains the local file path 'file:/E:/project/RefCOD-main/number_of_samples_by_class.png' where Figure 2 should be; the figure does not render in the PDF and must be replaced with an embedded image.
  2. [Table 2] The header 'Cuneen' should be 'Cunseen', and in Section 5.2 'FPSNet' should be 'FSPNet' for consistency with the method name used elsewhere.
  3. [§4] There are typos and grammatical errors, including 'tow parts' for 'two parts', 'feathers' for 'features', and 'traditional employ the traditional train paradigm' in Section 5.1; these should be corrected.
  4. [§5.3, Table 4] The Hard/Normal split is defined by CGNet's own S-measure scores (Sm >= 0.9). Since the difficulty label is derived from the method being evaluated, this introduces a potential bias when comparing methods on the resulting subsets. Please justify this choice or replace it with a difficulty criterion independent of the evaluated method.
  5. [§3] The criteria for the Cseen/Cunseen division are deferred to supplementary material. Please include the exact definition in the main text, as this split is used in Table 2 and is important for the generalization claims.

Circularity Check

0 steps flagged · score 2.0 of 10

No load-bearing circularity: the core benchmark comparisons are externally grounded; the flagged issues are internal data-count inconsistencies and reliance on author-provided results, not circular derivations.

full rationale

The paper's central derivation chain is self-contained against external benchmarks: CGNet is trained on the CamoClass training split (CAMO + COD10K, 4,040 samples), conditioned on class-level text at test time, and evaluated on standard CAMO/COD10K/NC4K/CHAMELEON test sets against independently published baselines. No fitted parameter is renamed as a prediction, and the class labels are part of the task definition rather than a hidden answer key. The only self-citations (ZoomNet as a baseline, and the training setup following Pang et al.) are not load-bearing for the reported gains. Two items flagged in the text are verification concerns rather than circularity: the CamoClass sample counts are internally inconsistent (10,523 in Section 3 versus 4,040 + 8,615 in Section 5.1, while the test sets in Tables 1 and 4 sum to 6,473), and the Cseen/Cunseen numbers are said to rely on results provided by the authors, which is unverifiable absent released results. The Section 5.3 Hard/Normal split uses CGNet's own S-measure to define difficulty; this is a self-referential evaluation design, but it does not force CGNet's reported cross-method superiority and is not the paper's central benchmark claim. Overall, the core comparisons do not reduce to their inputs by construction.

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

The central claims rest on the accuracy and consistency of the new dataset annotations and splits, and on the transferability of CLIP features. No free parameters are hand-fitted; the model weights are learned.

assumptions (3)
  • domain assumption Existing COD datasets (CAMO, COD10K, CHAMELEON, NC4K) can be merged and annotated with class labels without introducing labeling errors that significantly change the task difficulty.
    The paper constructs CamoClass by integrating these datasets and adding class annotations (Section 3). If the annotations are noisy or inconsistent, the benchmark results are not reliable.
  • domain assumption Frozen CLIP visual and text encoders provide transferable class-semantic knowledge that can effectively guide segmentation in the COD domain.
    The CPG uses a frozen CLIP model to generate text and visual features (Section 4.1). The whole method assumes that CLIP's pretrained representation is useful for camouflaged objects, which are visually similar to the background.
  • domain assumption The standard train/test splits of the original COD datasets are compatible with the new class-annotated training set, and the addition of class labels does not change the evaluation protocol.
    The paper claims CamoClass is flexible and that existing training/test partitioning can be maintained (Sec. 3). The comparisons in Table 1 rely on this compatibility.

how reviews work

0 comments
Cite this review

Pith. "Pith review of CGCOD: Class-Guided Camouflaged Object Detection." pith.science (2026). https://pith.science/paper/F7A4XOA6

@misc{pith2026241218977,
  author       = {Pith},
  title        = {Pith review of: CGCOD: Class-Guided Camouflaged Object Detection},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/F7A4XOA6}},
  note         = {Machine review of arXiv:2412.18977}
}
read the original abstract

Camouflaged Object Detection (COD) aims to identify objects that blend seamlessly into their surroundings. The inherent visual complexity of camouflaged objects, including their low contrast with the background, diverse textures, and subtle appearance variations, often obscures semantic cues, making accurate segmentation highly challenging. Existing methods primarily rely on visual features, which are insufficient to handle the variability and intricacy of camouflaged objects, leading to unstable object perception and ambiguous segmentation results. To tackle these limitations, we introduce a novel task, class-guided camouflaged object detection (CGCOD), which extends traditional COD task by incorporating object-specific class knowledge to enhance detection robustness and accuracy. To facilitate this task, we present a new dataset, CamoClass, comprising real-world camouflaged objects with class annotations. Furthermore, we propose a multi-stage framework, CGNet, which incorporates a plug-and-play class prompt generator and a simple yet effective class-guided detector. This establishes a new paradigm for COD, bridging the gap between contextual understanding and class-guided detection. Extensive experimental results demonstrate the effectiveness of our flexible framework in improving the performance of proposed and existing detectors by leveraging class-level textual information.

Figures

Figures reproduced from arXiv: 2412.18977 by the authors.

Figure 1
Figure 1. Visualization of the proposed CGNet illustrating its seg [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Class distribution in the proposed CamoClass dataset. [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Overview of the proposed CGNet. textual embeddings Ft from the class-level textual informa￾tion. Simultaneously, we extract multi-level visual features {Fi} 3 i=1 from 8, 16, and 24 layers of the frozen CLIP visual encoder. These features are then fused via a FPN module [Wang et al., 2022] and decode through a Transformer block to generate preliminary multi-modal features Fm. To further strengthen the alignment betw… view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: Visualization of the internal feature in the CPG. [PITH_FULL_IMAGE:figures/full_fig_p004_4.png]
Figure 5
Figure 5. Figure 5: Visual comparisons of prediction maps produced by dif [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]
Figure 6
Figure 6. Figure 6: Visual results of the effectiveness of our components, the [PITH_FULL_IMAGE:figures/full_fig_p007_6.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Retrospective Memory for Camouflaged Object Detection

    cs.CV 2025-06 conditional novelty 6.0 of 10

    RetroMem adds a prototype memory bank and inference pattern reconstruction to a DINOv2-based encoder, and it beats prior camouflaged object detection methods on four standard benchmarks.

Reference graph

Works this paper leans on

45 extracted references · 42 canonical work pages · cited by 1 Pith paper

  1. [1]

    Models and metrics for signature strength evaluation of camouflaged targets

    Anthony C Copeland and Mohan M Trivedi. Models and metrics for signature strength evaluation of camouflaged targets. In Algorithms for synthetic aperture radar imagery IV , volume 3070, pages 194--199. SPIE, 1997

    work page 1997

  2. [2]

    Structure-measure:, a new way to evaluate foreground maps

    D Fan, M Cheng, Y Liu, T Li, and A Botji. Structure-measure:, a new way to evaluate foreground maps. In Proc. IEEE/CVF Int. Conf. Comput. Vis , pages 4548--4557, 2017

    work page 2017

  3. [3]

    Enhanced-alignment measure for binary foreground map evaluation

    Deng-Ping Fan, Cheng Gong, Yang Cao, Bo Ren, Ming-Ming Cheng, and Ali Borji. Enhanced-alignment measure for binary foreground map evaluation. In Proceedings of International Joint Conference on Artificial Intelligence , pages 698--704, 7 2018

    work page 2018

  4. [4]

    Camouflaged object detection

    Deng-Ping Fan, Ge-Peng Ji, Guolei Sun, Ming-Ming Cheng, Jianbing Shen, and Ling Shao. Camouflaged object detection. In Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , pages 2774--2784, 2020

    work page 2020

  5. [5]

    Pranet: Parallel reverse attention network for polyp segmentation

    Deng-Ping Fan, Ge-Peng Ji, Tao Zhou, Geng Chen, Huazhu Fu, Jianbing Shen, and Ling Shao. Pranet: Parallel reverse attention network for polyp segmentation. In International conference on medical image computing and computer-assisted intervention , pages 263--273. Springer, 2020

    work page 2020

  6. [6]

    Concealed object detection

    Deng-Ping Fan, Ge-Peng Ji, Ming-Ming Cheng, and Ling Shao. Concealed object detection. IEEE Transactions on Pattern Analysis and Machine Intelligence , 44(10):6024--6042, 2022

    work page 2022

  7. [7]

    Texture segmentation by multiscale aggregation of filter responses and shape elements

    Galun, Sharon, Basri, and Brandt. Texture segmentation by multiscale aggregation of filter responses and shape elements. In Proceedings Ninth IEEE international conference on computer vision , pages 716--723. IEEE, 2003

    work page 2003

  8. [8]

    Camouflaged object detection with feature decomposition and edge reconstruction

    Chunming He, Kai Li, Yachao Zhang, Longxiang Tang, Yulun Zhang, Zhenhua Guo, and Xiu Li. Camouflaged object detection with feature decomposition and edge reconstruction. In Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , pages 22046--22055, 2023

    work page 2023

Show all 45 references
  1. [9]

    Segmentation from natural language expressions

    Ronghang Hu, Marcus Rohrbach, and Trevor Darrell. Segmentation from natural language expressions. In Computer Vision--ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11--14, 2016, Proceedings, Part I 14 , pages 108--124. Springer, 2016

    2016

  2. [10]

    High-resolution iterative feedback network for camouflaged object detection

    Xiaobin Hu, Shuo Wang, Xuebin Qin, Hang Dai, Wenqi Ren, Donghao Luo, Ying Tai, and Ling Shao. High-resolution iterative feedback network for camouflaged object detection. In Proceedings of the AAAI Conference on Artificial Intelligence , volume 37, pages 881--889, 2023

    2023

  3. [11]

    Efficient camouflaged object detection network based on global localization perception and local guidance refinement

    Xihang Hu, Xiaoli Zhang, Fasheng Wang, Jing Sun, and Fuming Sun. Efficient camouflaged object detection network based on global localization perception and local guidance refinement. IEEE Trans. Circuits Syst. Video Technol. , 34(7):5452--5465, 2024

    2024

  4. [12]

    Feature shrinkage pyramid for camouflaged object detection with transformers

    Zhou Huang, Hang Dai, Tian-Zhu Xiang, Shuo Wang, Huai-Xin Chen, Jie Qin, and Huan Xiong. Feature shrinkage pyramid for camouflaged object detection with transformers. In Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 5557--5566, 2023

    2023

  5. [13]

    A survey of methods for addressing the challenges of referring image segmentation

    Lixia Ji, Yunlong Du, Yiping Dang, Wenzhao Gao, and Han Zhang. A survey of methods for addressing the challenges of referring image segmentation. Neurocomputing , 583:127599, 2024

    2024

  6. [14]

    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. Computer Vision and Image Understanding , 184:45--56, 2019

    2019

  7. [15]

    Vscode: General visual salient and camouflaged object detection with 2 D prompt learning

    Ziyang Luo, Nian Liu, Wangbo Zhao, Xuguang Yang, Dingwen Zhang, Deng-Ping Fan, Fahad Khan, and Junwei Han. Vscode: General visual salient and camouflaged object detection with 2 D prompt learning. In Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , pages 17169--17180, June 2024

    2024

  8. [16]

    Simultaneously localize, segment and rank the camouflaged objects

    Yunqiu Lv, Jing Zhang, Yuchao Dai, Aixuan Li, Bowen Liu, Nick Barnes, and Deng-Ping Fan. Simultaneously localize, segment and rank the camouflaged objects. In Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , pages 11586--11596, 2021

    2021

  9. [17]

    U EDG :uncertainty-edge dual guided camouflage object detection

    Yixuan Lyu, Hong Zhang, Yan Li, Hanyang Liu, Yifan Yang, and Ding Yuan. U EDG :uncertainty-edge dual guided camouflage object detection. IEEE Trans. Multimedia , 26:4050--4060, 2024

    2024

  10. [18]

    Margolin, L

    R. Margolin, L. Zelnik-Manor, and A. Tal. How to evaluate foreground maps. In Proceedings of International Conference on Computer Vision and Pattern Recognition , pages 248--255, 2014

    2014

  11. [19]

    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 Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , pages 8768--8777, 2021

    2021

  12. [20]

    Hiding in plain sight: a study on camouflage and habitat selection in a slow-moving desert herbivore

    Melia G Nafus, Jennifer M Germano, Jeanette A Perry, Brian D Todd, Allyson Walsh, and Ronald R Swaisgood. Hiding in plain sight: a study on camouflage and habitat selection in a slow-moving desert herbivore. Behavioral Ecology , 26(5):1389--1394, 2015

    2015

  13. [21]

    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 Proc. IEEE/CVF Int. Conf. Comput. Vis , pages 2160--2170, 2022

    2022

  14. [22]

    Open-vocabulary camouflaged object segmentation

    Youwei Pang, Xiaoqi Zhao, Jiaming Zuo, Lihe Zhang, and Huchuan Lu. Open-vocabulary camouflaged object segmentation. In European Conference on Computer Vision , pages 476--495. Springer, 2025

    2025

  15. [23]

    Animal camouflage analysis: Chameleon database

    Skurowski Przemysław, Abdulameer Hassan, Błaszczyk Jakub, Depta Tomasz, Kornacki Adam, and Przemysław Kozieł. Animal camouflage analysis: Chameleon database. Unpublished Manuscript , 2018

    2018

  16. [24]

    Camouflaged object detection for machine vision applications

    Chennamsetty Pulla Rao, A Guruva Reddy, and CB Rama Rao. Camouflaged object detection for machine vision applications. International Journal of Speech Technology , 23:327--335, 2020

    2020

  17. [25]

    Learning transferable visual models from natural language supervision

    Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International conference on machine learning , p...

    2021

  18. [26]

    Visual semantic segmentation based on few/zero-shot learning: An overview

    Wenqi Ren, Yang Tang, Qiyu Sun, Chaoqiang Zhao, and Qing-Long Han. Visual semantic segmentation based on few/zero-shot learning: An overview. IEEE/CAA Journal of Automatica Sinica , 2023

    2023

  19. [27]

    Mm-camobj: A comprehensive multimodal dataset for camouflaged object scenarios

    Jiacheng Ruan, Wenzhen Yuan, Zehao Lin, Ning Liao, Zhiyu Li, Feiyu Xiong, Ting Liu, and Yuzhuo Fu. Mm-camobj: A comprehensive multimodal dataset for camouflaged object scenarios. arXiv preprint arXiv:2409.16084 , 2024

    2024 arXiv

  20. [28]

    Application of an image and environmental sensor network for automated greenhouse insect pest monitoring

    Dan Jeric Arcega Rustia, Chien Erh Lin, Jui-Yung Chung, Yi-Ji Zhuang, Ju-Chun Hsu, and Ta-Te Lin. Application of an image and environmental sensor network for automated greenhouse insect pest monitoring. Journal of Asia-Pacific Entomology , 23(1):17--28, 2020

    2020

  21. [29]

    Fsnet: Focus scanning network for camouflaged object detection

    Ze Song, Xudong Kang, Xiaohui Wei, Haibo Liu, Renwei Dian, and Shutao Li. Fsnet: Focus scanning network for camouflaged object detection. IEEE Trans. Image Process. , 32:2267--2278, 2023

    2023

  22. [30]

    Boundary-guided camouflaged object detection

    Yujia Sun, Shuo Wang, Chenglizhao Chen, and Tian-Zhu Xiang. Boundary-guided camouflaged object detection. In Proc. Int. Joint Conf. Artif. Intell. , pages 1335--1341, 2022

    2022

  23. [31]

    Frequency-spatial entanglement learning for camouflaged object detection

    Yanguang Sun, Chunyan Xu, Jian Yang, Hanyu Xuan, and Lei Luo. Frequency-spatial entanglement learning for camouflaged object detection. In European Conference on Computer Vision , pages 343--360, 2024

    2024

  24. [32]

    Lxmert: Learning cross-modality encoder representations from transformers

    Hao Tan and Mohit Bansal. Lxmert: Learning cross-modality encoder representations from transformers. arXiv preprint arXiv:1908.07490 , 2019

    1908 arXiv

  25. [33]

    Contrastive grouping with transformer for referring image segmentation

    Jiajin Tang, Ge Zheng, Cheng Shi, and Sibei Yang. Contrastive grouping with transformer for referring image segmentation. In Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , pages 23570--23580, 2023

    2023

  26. [34]

    Pyramid vision transformer: A versatile backbone for dense prediction without convolutions

    Wenhai Wang, Enze Xie, Xiang Li, Deng-Ping Fan, Kaitao Song, Ding Liang, Tong Lu, Ping Luo, and Ling Shao. Pyramid vision transformer: A versatile backbone for dense prediction without convolutions. In Proceedings of the IEEE/CVF international conference on computer vision , p...

    2021

  27. [35]

    Cris: Clip-driven referring image segmentation

    Zhaoqing Wang, Yu Lu, Qiang Li, Xunqiang Tao, Yandong Guo, Mingming Gong, and Tongliang Liu. Cris: Clip-driven referring image segmentation. In Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , pages 11686--11695, 2022

    2022

  28. [36]

    Depth-aware concealed crop detection in dense agricultural scenes

    Liqiong Wang, Jinyu Yang, Yanfu Zhang, Fangyi Wang, and Feng Zheng. Depth-aware concealed crop detection in dense agricultural scenes. In Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , pages 17201--17211, 2024

    2024

  29. [37]

    F ^3 net: fusion, feedback and focus for salient object detection

    Jun Wei, Shuhui Wang, and Qingming Huang. F ^3 net: fusion, feedback and focus for salient object detection. In Proceedings of the AAAI conference on artificial intelligence , volume 34, pages 12321--12328, 2020

    2020

  30. [38]

    Side adapter network for open-vocabulary semantic segmentation

    Mengde Xu, Zheng Zhang, Fangyun Wei, Han Hu, and Xiang Bai. Side adapter network for open-vocabulary semantic segmentation. In Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , pages 2945--2954, 2023

    2023

  31. [39]

    Camoformer: Masked separable attention for camouflaged object detection

    Bowen Yin, Xuying Zhang, Deng-Ping Fan, Shaohui Jiao, Ming-Ming Cheng, Luc Van Gool, and Qibin Hou. Camoformer: Masked separable attention for camouflaged object detection. IEEE Transactions on Pattern Analysis and Machine Intelligence , 2024

    2024

  32. [40]

    Exploring deeper! segment anything model with depth perception for camouflaged object detection

    Zhenni Yu, Xiaoqin Zhang, LiZhao, Yi Bin, and Guobao Xiao. Exploring deeper! segment anything model with depth perception for camouflaged object detection. In ACM Multimedia 2024 , 2024

    2024

  33. [41]

    Adaptive selection based referring image segmentation

    Pengfei Yue, Jianghang Lin, Shengchuan Zhang, Jie Hu, Yilin Lu, Hongwei Niu, Haixin Ding, Yan Zhang, Guannan Jiang, Liujuan Cao, et al. Adaptive selection based referring image segmentation. In Proceedings of the 32nd ACM International Conference on Multimedia , pages 1101--1110, 2024

    2024

  34. [42]

    A bayesian approach to camouflaged moving object detection

    Xiang Zhang, Ce Zhu, Shuai Wang, Yipeng Liu, and Mao Ye. A bayesian approach to camouflaged moving object detection. IEEE Trans. Circuits Syst. Video Technol. , 27(9):2001--2013, 2016

    2001

  35. [43]

    Collaborative camouflaged object detection: A large-scale dataset and benchmark

    Cong Zhang, Hongbo Bi, Tian-Zhu Xiang, Ranwan Wu, Jinghui Tong, and Xiufang Wang. Collaborative camouflaged object detection: A large-scale dataset and benchmark. IEEE Transactions on Neural Networks and Learning Systems , 2023

    2023

  36. [44]

    Unlocking attributes' contribution to successful camouflage: A combined textual and visual analysis strategy

    Hong Zhang, Yixuan Lyu, Qian Yu, Hanyang Liu, Huimin Ma, Ding Yuan, and Yifan Yang. Unlocking attributes' contribution to successful camouflage: A combined textual and visual analysis strategy. In Computer Vision -- ECCV 2024 , pages 315--331, Cham, 2025. Springer Nature Switzerland

    2024

  37. [45]

    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 11, 2026 · model on record in the stance chip above.