Pith. sign in

REVIEW 2 major objections 2 minor 86 references

CamoNAS: Neural Architecture Search for Enhanced Camouflaged Object Detection

T0 review · 2 major / 2 minor · reviewed 2026-07-03 · grok-4.3

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

Pith's one-line read CamoNAS applies neural architecture search over a hierarchical space with a frequency-aware dual-stream design to reach state-of-the-art results on camouflaged object detection.

desk verdict CamoNAS adds a hierarchical NAS search plus a learnable wavelet stream to COD but the abstract supplies no controls to show those choices, rather than tuning, produce the SOTA numbers. read the letter →

arxiv 2607.01870 v1 pith:BKQPIYIF submitted 2026-07-02 cs.AI

Dawei Ren , Yan Zhang , Hongying Tang , Qiaoling Zhou , Jianpo Liu This is my paper · ORCID
classification cs.AI
keywords camouflagedobjectdetectionneuralarchitecturesearchfrequencydomainwavelettransformdual-streamnetworkhierarchicalspacebenchmarkevaluation
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 replaces hand-designed architectures for camouflaged object detection with an automated search process that jointly optimizes cell operations and network downsampling paths. It adds a dual-stream backbone in which a learnable wavelet transform supplies frequency information alongside the usual RGB spatial stream. A reader would care because camouflaged objects present weak edges and ill-defined boundaries that intuition-based multi-scale fusion often fails to handle well. If the approach works, it demonstrates that domain-specific NAS can systematically discover better feature combinations than manual design for tasks where visual cues are subtle.

What carries the argument

Hierarchical search space over cell operations and downsampling paths together with an RGB frequency dual-stream architecture driven by a learnable wavelet transform.

What would settle it

A controlled comparison in which a manually designed dual-stream network with matched compute budget and similar hyper-parameter search effort is evaluated on the same four benchmarks and matches or exceeds CamoNAS scores.

Watch

Extended reading notes

Core claim

CamoNAS builds a hierarchical search space that searches both cell-level operations and network-level downsampling paths, then trains an RGB frequency dual-stream network whose second stream uses a learnable wavelet transform; the resulting architectures reach state-of-the-art performance on the CAMO, COD10K, NC4K, and CHAMELEON benchmarks.

Load-bearing premise

The performance gains arise from the frequency-aware hierarchical NAS design rather than from hyper-parameter tuning volume or from particular properties of the four benchmarks.

Editorial extensions

If this is right

  • The searched architectures outperform prior hand-designed COD models on all four standard benchmarks.
  • Frequency information from the wavelet stream improves handling of weak boundary cues.
  • Joint search over cell operations and downsampling paths produces task-specific multi-resolution structures.
  • NAS can replace intuition-driven multi-scale fusion in domains with ill-defined object boundaries.

Reading between the lines

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

  • The same frequency-augmented search could be tested on texture-blending problems outside natural images, such as anomaly detection in medical scans.
  • If the hierarchical space proves reusable, it could reduce the need for repeated manual redesign when applying NAS to new detection tasks with scale variation.
  • The dual-stream pattern suggests that explicit separation of spatial and frequency processing may be worth testing in other low-contrast segmentation settings.
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, simulated authors' rebuttal, and a circularity audit.

Referee Report

2 major / 2 minor

Summary. The paper introduces CamoNAS, a frequency-aware multi-resolution Neural Architecture Search (NAS) framework for Camouflaged Object Detection (COD). It automatically searches cell-level operations and network-level downsampling paths in a hierarchical space, combined with an RGB-frequency dual-stream architecture that incorporates a learnable wavelet transform. The central claim is that this yields state-of-the-art performance on the CAMO, COD10K, NC4K, and CHAMELEON benchmarks, with code released publicly.

Significance. If the reported gains are shown to stem specifically from the hierarchical NAS and dual-stream design rather than generic tuning, the work would demonstrate that systematic architecture search can improve upon hand-designed models in a domain characterized by weak boundaries and low-contrast cues. The public code release supports reproducibility and is a clear strength.

major comments (2)
  1. [§4] §4 (Experiments): No control experiment is reported in which a fixed, hand-designed backbone (e.g., a standard ResNet or the authors' own dual-stream RGB baseline) receives an equivalent hyperparameter and augmentation search budget; without this, the necessity of the proposed cell-level and network-level search space for the SOTA margins cannot be isolated from tuning effects.
  2. [Table 2] Table 2 (main results): Performance deltas versus prior SOTA are presented as single-point estimates without standard deviations across multiple random seeds or statistical significance tests; given the small margins typical in COD benchmarks, this weakens the claim that CamoNAS is reliably superior.
minor comments (2)
  1. [§3.2] §3.2: The description of the learnable wavelet transform lacks an explicit equation for the frequency decomposition; adding this would clarify how the dual-stream fusion is implemented.
  2. [Figure 3] Figure 3: The diagram of the hierarchical search space would benefit from clearer labeling of the cell-level versus network-level search dimensions.

Simulated Author's Rebuttal

2 responses · 0 unresolved

Thank you for the detailed review. We have carefully considered the major comments and provide point-by-point responses below. We propose partial revisions to address the concerns where feasible.

read point-by-point responses

  1. Referee: [§4] §4 (Experiments): No control experiment is reported in which a fixed, hand-designed backbone (e.g., a standard ResNet or the authors' own dual-stream RGB baseline) receives an equivalent hyperparameter and augmentation search budget; without this, the necessity of the proposed cell-level and network-level search space for the SOTA margins cannot be isolated from tuning effects.

    Authors: We appreciate this point. Our primary contribution is the introduction of the hierarchical NAS framework tailored for COD, and the comparisons are made against published SOTA methods using their reported settings. To better isolate the effect, we will add a discussion in the revised manuscript explaining that the dual-stream baseline is our own design without NAS, and the performance gains are attributed to the searched architectures. However, conducting a full hyperparameter search on a fixed backbone would require substantial additional resources beyond the scope of this work. revision: partial

  2. Referee: [Table 2] Table 2 (main results): Performance deltas versus prior SOTA are presented as single-point estimates without standard deviations across multiple random seeds or statistical significance tests; given the small margins typical in COD benchmarks, this weakens the claim that CamoNAS is reliably superior.

    Authors: We acknowledge that reporting standard deviations would provide stronger evidence of reliability. In the COD literature, it is common to report single-run results due to the high computational demands of training deep models on these benchmarks. Our results show consistent improvements across four different datasets, which supports the robustness. We will add a note in the revised paper acknowledging this limitation and suggesting future work on statistical validation. revision: partial

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity; empirical NAS results with no derived quantities

full rationale

The paper describes an empirical NAS method (CamoNAS) that searches cell-level operations and network-level paths in a frequency-aware dual-stream architecture, then reports benchmark performance. No equations, first-principles derivations, or predictions appear in the provided text. Performance numbers are presented as search outcomes rather than quantities fitted or defined in terms of themselves. No self-citation load-bearing steps, ansatz smuggling, or renaming of known results are evident. The derivation chain is self-contained as an engineering search procedure.

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

The central claim rests on the existence of a well-behaved search space whose optimal architecture yields measurable gains on the chosen benchmarks; no explicit free parameters, axioms, or invented entities are stated in the abstract.

how reviews work

0 comments
Cite this review

Pith. "Pith review of CamoNAS: Neural Architecture Search for Enhanced Camouflaged Object Detection." pith.science (2026). https://pith.science/paper/BKQPIYIF

@misc{pith2026260701870,
  author       = {Pith},
  title        = {Pith review of: CamoNAS: Neural Architecture Search for Enhanced Camouflaged Object Detection},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/BKQPIYIF}},
  note         = {Machine review of arXiv:2607.01870}
}
read the original abstract

Camouflaged Object Detection (COD) aims to locate and segment objects that blend into their surroundings, presenting challenges due to weak edge cues and ill-defined boundaries. Traditional COD models rely on hand-designed architectures and multi-scale feature fusion, which are often guided by intuition rather than systematic search. This paper introduces CamoNAS, a frequency-aware multi-resolution Neural Architecture Search (NAS) framework for COD. CamoNAS automatically searches both cell-level operations and network-level downsampling paths, forming a hierarchical search space tailored to detect camouflaged objects. Additionally, it adopts an RGB frequency dual-stream architecture, where a learnable wavelet transform complements the RGB spatial stream. CamoNAS achieves state-of-the-art performance on four COD benchmarks (CAMO, COD10K, NC4K, CHAMELEON), highlighting the effectiveness of NAS for COD. Our code is available at https://github.com/rendaweiSIMIT/CamoNAS.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

86 extracted references · 86 canonical work pages

  1. [1]

    IEEE transactions on pattern analysis and machine intelligence44(10), 6024–6042 (2021)

    Fan, D.-P., Ji, G.-P., Cheng, M.-M., Shao, L.: Concealed object detection. IEEE transactions on pattern analysis and machine intelligence44(10), 6024–6042 (2021)

    work page 2021

  2. [2]

    In: CVPR, pp

    Fan, D.-P., Ji, G.-P., Sun, G., Cheng, M.-M., Shen, J., Shao, L.: Camouflaged object detection. In: CVPR, pp. 2777–2787 (2020) 15

    work page 2020

  3. [3]

    In: ICCV, pp

    Yang, F., Zhai, Q., Li, X., Huang, R., Luo, A., Cheng, H., Fan, D.-P.: Uncertainty- guided transformer reasoning for camouflaged object detection. In: ICCV, pp. 4146–4155 (2021)

    work page 2021

  4. [4]

    In: MICCAI, pp

    Fan, D.-P., Ji, G.-P., Zhou, T., Chen, G., Fu, H., Shen, J., Shao, L.: Pranet: Parallel reverse attention network for polyp segmentation. In: MICCAI, pp. 263– 273 (2020). Springer

    work page 2020

  5. [5]

    In: MICCAI, pp

    Zhang, R., Li, G., Li, Z., Cui, S., Qian, D., Yu, Y.: Adaptive context selection for polyp segmentation. In: MICCAI, pp. 253–262 (2020). Springer

    work page 2020

  6. [6]

    In: CVPR, pp

    Mei, H., Yang, X., Wang, Y., Liu, Y., He, S., Zhang, Q., Wei, X., Lau, R.W.: Don’t hit me! glass detection in real-world scenes. In: CVPR, pp. 3687–3696 (2020)

    work page 2020

  7. [7]

    In: ECCV (2020)

    Xie, E., Wang, W., Wang, W., Ding, M., Shen, C., Luo, P.: Segmenting transparent objects in the wild. In: ECCV (2020). Springer

    work page 2020

  8. [8]

    In: ACM MM, pp

    Zhang, M., Xu, S., Piao, Y., Shi, D., Lin, S., Lu, H.: Preynet: Preying on camouflaged objects. In: ACM MM, pp. 5323–5332 (2022)

    work page 2022

Show all 86 references
  1. [9]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp

    Mei, H., Ji, G.-P., Wei, Z., Yang, X., Wei, X., Fan, D.-P.: Camouflaged object seg- mentation with distraction mining. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8772–8781 (2021)

    2021

  2. [10]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp

    Pang, Y., Zhao, X., Xiang, T.-Z., Zhang, L., Lu, H.: Zoom in and out: A mixed- scale triplet network for camouflaged object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2160– 2170 (2022)

    2022

  3. [11]

    Scientific Reports15(1), 2735 (2025)

    Cheng, A., Wu, S., Liu, X., Lu, H.: Enhancing concealed object detection in active thz security images with adaptation-yolo. Scientific Reports15(1), 2735 (2025)

    2025

  4. [12]

    Engineering Applications of Artificial Intelligence158, 111462 (2025)

    Kowalski, M., Mierzejewski, K., Pa lys, T.: Bi-spectral concealed object detection with attention-based fusion of passive thermal infrared and terahertz imaging. Engineering Applications of Artificial Intelligence158, 111462 (2025)

    2025

  5. [13]

    In: 2022 26th International Conference on Pattern Recognition (ICPR), pp

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

    2022

  6. [14]

    In: CVPR, pp

    Zhong, Y., Li, B., Tang, L., Kuang, S., Wu, S., Ding, S.: Detecting camouflaged object in frequency domain. In: CVPR, pp. 4504–4513 (2022)

    2022

  7. [15]

    In: CVPR (2023)

    He, C., Li, K., Zhang, Y., Tang, L., Zhang, Y., Guo, Z., Li, X.: Camouflaged object detection with feature decomposition and edge reconstruction. In: CVPR (2023)

    2023

  8. [16]

    In: CVPR, pp

    Zhai, Q., Li, X., Yang, F., Chen, C., Cheng, H., Fan, D.-P.: Mutual graph learning 16 for camouflaged object detection. In: CVPR, pp. 12997–13007 (2021)

    2021

  9. [17]

    arXiv preprint arXiv:2207.00794 (2022)

    Sun, Y., Wang, S., Chen, C., Xiang, T.-Z.: Boundary-guided camouflaged object detection. arXiv preprint arXiv:2207.00794 (2022)

    2022

  10. [18]

    In: NIPS, vol

    Zhai, W., Cao, Y., Zhang, J., Zha, Z.-J.: Exploring figure-ground assignment mechanism in perceptual organization. In: NIPS, vol. 35 (2022)

    2022

  11. [19]

    In: CVPR, pp

    Lv, Y., Zhang, J., Dai, Y., Li, A., Liu, B., Barnes, N., Fan, D.-P.: Simultaneously localize, segment and rank the camouflaged objects. In: CVPR, pp. 11591–11601 (2021)

    2021

  12. [20]

    In: CVPR, pp

    Jia, Q., Yao, S., Liu, Y., Fan, X., Liu, R., Luo, Z.: Segment, magnify and reiterate: Detecting camouflaged objects the hard way. In: CVPR, pp. 4713–4722 (2022)

    2022

  13. [21]

    In: AAAI, vol

    Zhu, H., Li, P., Xie, H., Yan, X., Liang, D., Chen, D., Wei, M., Qin, J.: I can find you! boundary-guided separated attention network for camouflaged object detection. In: AAAI, vol. 36, pp. 3608–3616 (2022)

    2022

  14. [22]

    Journal of Machine Learning Research20(55), 1–21 (2019)

    Elsken, T., Metzen, J.H., Hutter, F.: Neural architecture search: A survey. Journal of Machine Learning Research20(55), 1–21 (2019)

    2019

  15. [23]

    arXiv preprint arXiv:1611.01578 (2016)

    Zoph, B., Le, Q.V.: Neural architecture search with reinforcement learning. arXiv preprint arXiv:1611.01578 (2016)

    2016 arXiv

  16. [24]

    In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp

    Zoph, B., Vasudevan, V., Shlens, J., Le, Q.V.: Learning transferable architec- tures for scalable image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8697–8710 (2018)

    2018

  17. [25]

    In: Proceedings of the European Conference on Computer Vision (ECCV), pp

    Liu, C., Zoph, B., Neumann, M., Shlens, J., Hua, W., Li, L.-J., Fei-Fei, L., Yuille, A., Huang, J., Murphy, K.: Progressive neural architecture search. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 19–34 (2018)

    2018

  18. [26]

    In: Proceedings of the Aaai Conference on Artificial Intelligence, vol

    Real, E., Aggarwal, A., Huang, Y., Le, Q.V.: Regularized evolution for image classifier architecture search. In: Proceedings of the Aaai Conference on Artificial Intelligence, vol. 33, pp. 4780–4789 (2019)

    2019

  19. [27]

    In: International Conference on Machine Learning, pp

    Pham, H., Guan, M., Zoph, B., Le, Q., Dean, J.: Efficient neural architecture search via parameters sharing. In: International Conference on Machine Learning, pp. 4095–4104 (2018). PMLR

    2018

  20. [29]

    In: Proceedings of the IEEE/CVF Conference 17 on Computer Vision and Pattern Recognition, pp

    Zhang, Y., Qiu, Z., Liu, J., Yao, T., Liu, D., Mei, T.: Customizable architecture search for semantic segmentation. In: Proceedings of the IEEE/CVF Conference 17 on Computer Vision and Pattern Recognition, pp. 11641–11650 (2019)

    2019

  21. [30]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp

    Nekrasov, V., Chen, H., Shen, C., Reid, I.: Fast neural architecture search of compact semantic segmentation models via auxiliary cells. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9126–9135 (2019)

    2019

  22. [31]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp

    Lin, P., Sun, P., Cheng, G., Xie, S., Li, X., Shi, J.: Graph-guided architecture search for real-time semantic segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4203–4212 (2020)

    2020

  23. [32]

    arXiv preprint arXiv:1806.09055 (2018)

    Liu, H., Simonyan, K., Yang, Y.: Darts: Differentiable architecture search. arXiv preprint arXiv:1806.09055 (2018)

    2018 arXiv

  24. [33]

    In: ICASSP 2025-2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp

    Li, X., Fu, K., Zhao, Q.: Camouflaged object detection via neural architec- ture search. In: ICASSP 2025-2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2025). IEEE

    2025

  25. [34]

    Neurocomputing 566, 127050 (2024)

    Liang, Y., Qin, G., Sun, M., Wang, X., Yan, J., Zhang, Z.: A systematic review of image-level camouflaged object detection with deep learning. Neurocomputing 566, 127050 (2024)

    2024

  26. [35]

    In: Proceedings of the 31st ACM International Conference on Multimedia, pp

    Cong, R., Sun, M., Zhang, S., Zhou, X., Zhang, W., Zhao, Y.: Frequency percep- tion network for camouflaged object detection. In: Proceedings of the 31st ACM International Conference on Multimedia, pp. 1179–1189 (2023)

    2023

  27. [36]

    In: Proceedings of the 31st ACM International Conference on Multimedia, pp

    Xie, C., Xia, C., Yu, T., Li, J.: Frequency representation integration for camou- flaged object detection. In: Proceedings of the 31st ACM International Conference on Multimedia, pp. 1789–1797 (2023)

    2023

  28. [37]

    ACM Computing Surveys (CSUR)47(2), 1–33 (2014)

    Zhou, X., Yang, C., Zhao, H., Yu, W.: Low-rank modeling and its applications in image analysis. ACM Computing Surveys (CSUR)47(2), 1–33 (2014)

    2014

  29. [38]

    IEEE transactions on circuits and systems for video technology32(9), 5708–5724 (2021)

    Bi, H., Zhang, C., Wang, K., Tong, J., Zheng, F.: Rethinking camouflaged object detection: Models and datasets. IEEE transactions on circuits and systems for video technology32(9), 5708–5724 (2021)

    2021

  30. [39]

    International Journal of Computer Vision122(1), 116–148 (2017)

    Mondal, A., Ghosh, S., Ghosh, A.: Partially camouflaged object tracking using modified probabilistic neural network and fuzzy energy based active contour. International Journal of Computer Vision122(1), 116–148 (2017)

    2017

  31. [40]

    IEEE Transactions on Image Processing27(8), 3918–3930 (2018)

    Li, S., Florencio, D., Li, W., Zhao, Y., Cook, C.: A fusion framework for cam- ouflaged moving foreground detection in the wavelet domain. IEEE Transactions on Image Processing27(8), 3918–3930 (2018)

    2018

  32. [41]

    Computer vision and image understanding 18 184, 45–56 (2019)

    Le, T.-N., Nguyen, T.V., Nie, Z., Tran, M.-T., Sugimoto, A.: Anabranch network for camouflaged object segmentation. Computer vision and image understanding 18 184, 45–56 (2019)

    2019

  33. [42]

    IEEE Transactions on Pattern Analysis and Machine Intelligence (2024)

    Yin, B., Zhang, X., Fan, D.-P., Jiao, S., Cheng, M.-M., Van Gool, L., Hou, Q.: Camoformer: Masked separable attention for camouflaged object detection. IEEE Transactions on Pattern Analysis and Machine Intelligence (2024)

    2024

  34. [43]

    The Visual Computer41(6), 3957–3972 (2025)

    Yue, G., Jiao, G., Li, C., Xiang, J.: When cnn meet with vit: decision-level feature fusion for camouflaged object detection. The Visual Computer41(6), 3957–3972 (2025)

    2025

  35. [44]

    The Visual Computer 41(7), 4741–4758 (2025)

    Ge, Y., Ren, J., Zhang, C., He, M., Bi, H., Zhang, Q.: Feature-aware and itera- tive refinement network for camouflaged object detection. The Visual Computer 41(7), 4741–4758 (2025)

    2025

  36. [45]

    Unpublished manuscript 2(6), 7 (2018)

    Skurowski, P., Abdulameer, H., B laszczyk, J., Depta, T., Kornacki, A., Kozie l, P.: Animal camouflage analysis: Chameleon database. Unpublished manuscript 2(6), 7 (2018)

    2018

  37. [46]

    In: CVPR, pp

    Lin, J., He, Z., Lau, R.W.: Rich context aggregation with reflection prior for glass surface detection. In: CVPR, pp. 13415–13424 (2021)

    2021

  38. [47]

    In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp

    Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234–241 (2015). Springer

    2015

  39. [48]

    IEEE transactions on pattern analysis and machine intelligence40(4), 834–848 (2017)

    Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE transactions on pattern analysis and machine intelligence40(4), 834–848 (2017)

    2017

  40. [49]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp

    Kirillov, A., Wu, Y., He, K., Girshick, R.: Pointrend: Image segmentation as rendering. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9799–9808 (2020)

    2020

  41. [50]

    In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp

    Lin, T.-Y., Doll´ ar, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017)

    2017

  42. [51]

    Virtual Reality & Intelligent Hardware5(1), 57–67 (2023) https://doi.org/10.1016/j.vrih.2022.07.006

    Zhang, M., Tian, X.: Transformer architecture based on mutual attention for image-anomaly detection. Virtual Reality & Intelligent Hardware5(1), 57–67 (2023) https://doi.org/10.1016/j.vrih.2022.07.006

    2023 doi

  43. [52]

    Computer Animation and Virtual Worlds36(3), 70030 (2025) https://doi.org/10.1002/cav.70030

    Lin, C., Zou, C., Xu, H.: Scnet: A dual-branch network for strong noisy image denoising based on swin transformer and convnext. Computer Animation and Virtual Worlds36(3), 70030 (2025) https://doi.org/10.1002/cav.70030

    2025 doi

  44. [53]

    Computer Animation and Virtual Worlds36(3), 70038 (2025) https://doi.org/10.1002/cav.70038

    Zhang, M., Zhou, J., Miao, T., Zhao, Y., Si, X., Zhang, J.: Joint-learning: A robust 19 segmentation method for 3d point clouds under label noise. Computer Animation and Virtual Worlds36(3), 70038 (2025) https://doi.org/10.1002/cav.70038

    2025 doi

  45. [54]

    In: Proceedings of the AAAI Conference on Artificial Intelligence, vol

    Yao, L., Xu, H., Zhang, W., Liang, X., Li, Z.: Sm-nas: Structural-to-modular neural architecture search for object detection. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 12661–12668 (2020)

    2020

  46. [55]

    Advances in neural information processing systems 32(2019)

    Chen, Y., Yang, T., Zhang, X., Meng, G., Xiao, X., Sun, J.: Detnas: Backbone search for object detection. Advances in neural information processing systems 32(2019)

    2019

  47. [56]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp

    Guo, J., Han, K., Wang, Y., Zhang, C., Yang, Z., Wu, H., Chen, X., Xu, C.: Hit-detector: Hierarchical trinity architecture search for object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11405–11414 (2020)

    2020

  48. [57]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp

    Wang, N., Gao, Y., Chen, H., Wang, P., Tian, Z., Shen, C., Zhang, Y.: Nas- fcos: Fast neural architecture search for object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11943– 11951 (2020)

    2020

  49. [58]

    In: International Confer- ence on Machine Learning, pp

    Real, E., Moore, S., Selle, A., Saxena, S., Suematsu, Y.L., Tan, J., Le, Q.V., Kurakin, A.: Large-scale evolution of image classifiers. In: International Confer- ence on Machine Learning, pp. 2902–2911 (2017). PMLR

    2017

  50. [59]

    arXiv preprint arXiv:1907.05737 (2019)

    Xu, Y., Xie, L., Zhang, X., Chen, X., Qi, G.-J., Tian, Q., Xiong, H.: Pc-darts: Par- tial channel connections for memory-efficient architecture search. arXiv preprint arXiv:1907.05737 (2019)

    1907

  51. [60]

    In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp

    Chen, X., Xie, L., Wu, J., Tian, Q.: Progressive differentiable architecture search: Bridging the depth gap between search and evaluation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 1294–1303 (2019)

    2019

  52. [61]

    arXiv preprint arXiv:2009.01027 (2020)

    Chu, X., Wang, X., Zhang, B., Lu, S., Wei, X., Yan, J.: Darts-: robustly stepping out of performance collapse without indicators. arXiv preprint arXiv:2009.01027 (2020)

    2009

  53. [62]

    IEEE Transactions on Pattern Analysis and Machine Intelligence45(11), 13489– 13508 (2023) https://doi.org/10.1109/TPAMI.2023.3293885

    Chen, Z., Qiu, G., Li, P., Zhu, L., Yang, X., Sheng, B.: Mngnas: Distilling adaptive combination of multiple searched networks for one-shot neural architecture search. IEEE Transactions on Pattern Analysis and Machine Intelligence45(11), 13489– 13508 (2023) https://doi.org/10....

    2023 doi

  54. [63]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp

    Ghiasi, G., Lin, T.-Y., Le, Q.V.: Nas-fpn: Learning scalable feature pyramid archi- tecture for object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7036–7045 (2019)

    2019

  55. [64]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp

    Tan, M., Chen, B., Pang, R., Vasudevan, V., Sandler, M., Howard, A., Le, Q.V.: 20 Mnasnet: Platform-aware neural architecture search for mobile. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2820–2828 (2019)

    2019

  56. [65]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp

    Tan, M., Pang, R., Le, Q.V.: Efficientdet: Scalable and efficient object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10781–10790 (2020)

    2020

  57. [66]

    arXiv preprint arXiv:1812.00332 (2018)

    Cai, H., Zhu, L., Han, S.: Proxylessnas: Direct neural architecture search on target task and hardware. arXiv preprint arXiv:1812.00332 (2018)

    2018 arXiv

  58. [67]

    IEEE Transactions on Multimedia25, 50–61 (2023) https://doi.org/10.1109/TMM.2021.3120873

    Lin, X., Sun, S., Huang, W., Sheng, B., Li, P., Feng, D.D.: Eapt: Efficient attention pyramid transformer for image processing. IEEE Transactions on Multimedia25, 50–61 (2023) https://doi.org/10.1109/TMM.2021.3120873

    2023 doi

  59. [68]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp

    Liu, C., Chen, L.-C., Schroff, F., Adam, H., Hua, W., Yuille, A.L., Fei-Fei, L.: Auto-deeplab: Hierarchical neural architecture search for semantic image segmen- tation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 82–92 (2019)

    2019

  60. [69]

    Advances in neural information processing systems33, 22158–22169 (2020)

    Cheng, X., Zhong, Y., Harandi, M., Dai, Y., Chang, X., Li, H., Drummond, T., Ge, Z.: Hierarchical neural architecture search for deep stereo matching. Advances in neural information processing systems33, 22158–22169 (2020)

    2020

  61. [70]

    In: International Conference on Machine Learning, pp

    Na, B., Mok, J., Park, S., Lee, D., Choe, H., Yoon, S.: Autosnn: Towards energy-efficient spiking neural networks. In: International Conference on Machine Learning, pp. 16253–16269 (2022). PMLR

    2022

  62. [71]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp

    Qin, X., Zhang, Z., Huang, C., Gao, C., Dehghan, M., Jagersand, M.: Bas- net: Boundary-aware salient object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7479–7489 (2019)

    2019

  63. [72]

    In: Proceedings of the AAAI Conference on Artificial Intelligence, vol

    Wei, J., Wang, S., Huang, Q.: F 3net: fusion, feedback and focus for salient object detection. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 12321–12328 (2020)

    2020

  64. [73]

    In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp

    Huang, Z., Zhang, Z., Lan, C., Zha, Z.-J., Lu, Y., Guo, B.: Adaptive fre- quency filters as efficient global token mixers. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 6049–6059 (2023)

    2023

  65. [74]

    arXiv preprint arXiv:2506.18437 (2025)

    He, S., Lin, G., Li, T., Chen, Y.: Frequency-domain fusion transformer for image inpainting. arXiv preprint arXiv:2506.18437 (2025)

    2025

  66. [75]

    Geng, Z., Guo, M.-H., Chen, H., Li, X., Wei, K., Lin, Z.: Is attention better than matrix decomposition? arXiv preprint arXiv:2109.04553 (2021)

    2021

  67. [76]

    arXiv preprint arXiv:2304.02643 (2023)

    Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., 21 Whitehead, S., Berg, A.C., Lo, W.-Y., et al.: Segment anything. arXiv preprint arXiv:2304.02643 (2023)

    2023 arXiv

  68. [77]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp

    Li, A., Zhang, J., Lv, Y., Liu, B., Zhang, T., Dai, Y.: Uncertainty-aware joint salient object and camouflaged object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10071–10081 (2021)

    2021

  69. [78]

    Machine Intelligence Research20(1), 92–108 (2023)

    Ji, G.-P., Fan, D.-P., Chou, Y.-C., Dai, D., Liniger, A., Van Gool, L.: Deep gra- dient learning for efficient camouflaged object detection. Machine Intelligence Research20(1), 92–108 (2023)

    2023

  70. [79]

    In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp

    Wu, Z., Paudel, D.P., Fan, D.-P., Wang, J., Wang, S., Demonceaux, C., Timofte, R., Van Gool, L.: Source-free depth for object pop-out. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 1032–1042 (2023)

    2023

  71. [80]

    AAAI (2023)

    He, R., Dong, Q., Lin, J., Lau, R.W.: Weakly-supervised camouflaged object detection with scribble annotations. AAAI (2023)

    2023

  72. [81]

    Advances in Neural Information Processing Systems 36, 30726–30737 (2023)

    He, C., Li, K., Zhang, Y., Xu, G., Tang, L., Zhang, Y., Guo, Z., Li, X.: Weakly- supervised concealed object segmentation with sam-based pseudo labeling and multi-scale feature grouping. Advances in Neural Information Processing Systems 36, 30726–30737 (2023)

    2023

  73. [82]

    arXiv preprint arXiv:2308.03166 (2023)

    He, C., Li, K., Zhang, Y., Zhang, Y., Guo, Z., Li, X., Danelljan, M., Yu, F.: Strategic preys make acute predators: Enhancing camouflaged object detectors by generating camouflaged objects. arXiv preprint arXiv:2308.03166 (2023)

    2023

  74. [83]

    IEEE Transactions on Multimedia26, 7114–7129 (2024)

    Zhou, X., Wu, Z., Cong, R.: Decoupling and integration network for camouflaged object detection. IEEE Transactions on Multimedia26, 7114–7129 (2024)

    2024

  75. [84]

    IEEE transactions on pattern analysis and machine intelligence46(12), 9205–9220 (2024)

    Pang, Y., Zhao, X., Xiang, T.-Z., Zhang, L., Lu, H.: Zoomnext: A unified collab- orative pyramid network for camouflaged object detection. IEEE transactions on pattern analysis and machine intelligence46(12), 9205–9220 (2024)

    2024

  76. [85]

    In: Proceedings of the AAAI Conference on Artificial Intelligence, vol

    Hu, J., Lin, J., Gong, S., Cai, W.: Relax image-specific prompt requirement in sam: A single generic prompt for segmenting camouflaged objects. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 38, pp. 12511–12518 (2024)

    2024

  77. [86]

    In: Proceedings of the Computer Vision and Pattern Recognition Conference, pp

    Yan, W., Chen, L., Kou, H., Zhang, S., Zhang, Y., Cao, L.: Ucod-dpl: Unsu- pervised camouflaged object detection via dynamic pseudo-label learning. In: Proceedings of the Computer Vision and Pattern Recognition Conference, pp. 30365–30375 (2025)

    2025

  78. [87]

    In: 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), pp

    Gupta, A., Jerripothula, K.R., Tillo, T.: Circod: Co-saliency inspired refer- ring camouflaged object discovery. In: 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), pp. 8313–8323 (2025). IEEE 22

    2025

Pith tools

Reviewed July 3, 2026 · model on record in the stance chip above.