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Camouflaged Object Detection with Adaptive Partition and Background Retrieval

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Abstract

Recent works confirm the importance of local details for identifying camouflaged objects. However, how to identify the details around the target objects via background cues lacks in-depth study. In this paper, we take this into account and present a novel learning framework for camouflaged object detection, called AdaptCOD. To be specific, our method decouples the detection process into three parts, namely localization, segmentation, and retrieval. We design a context adaptive partition strategy to dynamically select a reasonable context region for local segmentation and a background retrieval module to further polish the camouflaged object boundaries. Despite the simplicity, our method enables even a simple COD model to achieve great performance. Extensive experiments show that AdaptCOD surpasses all existing state-of-the-art methods on three widely-used camouflaged object detection benchmarks. Code is publicly available at https://github.com/HVision-NKU/AdaptCOD.

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References

  • Bernal, J., Sánchez, F. J., Fernández-Esparrach, G., Gil, D., Rodríguez, C., & Vilariño, F. (2015). Wm-dova maps for accurate polyp highlighting in colonoscopy: Validation vs. saliency maps from physicians. CMIG, 43, 99–111.

    Google Scholar 

  • Bernal, J., Sánchez, J., & Vilarino, F. (2012). Towards automatic polyp detection with a polyp appearance model. Pattern Recognition, 45(9), 3166–3182.

    Article  Google Scholar 

  • Chen, G., Liu, S. J., Sun, Y. J., Ji, G. P., Wu, Y. F., & Zhou, T. (2022). Camouflaged object detection via context-aware cross-level fusion. IEEE Transactions on Circuits and Systems for Video Technology, 32(10), 6981–6993.

    Article  Google Scholar 

  • Cheng, X., Xiong, H., Fan, D.P., Zhong, Y., Harandi, M., Drummond, T., & Ge, Z. (2022). Implicit motion handling for video camouflaged object detection. In: CVPR

  • Chu, X., Tian, Z., Wang, Y., Zhang, B., Ren, H., Wei, X., Xia, H., Shen, C. (2021). Twins: Revisiting the design of spatial attention in vision transformers. In: NeurIPS

  • De Boer, P. T., Kroese, D. P., Mannor, S., & Rubinstein, R. Y. (2005). A tutorial on the cross-entropy method. Annals of operations research, 134(1), 19–67.

    Article  MathSciNet  Google Scholar 

  • Devlin, J., Chang, M.W., Lee, K., & Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. In: NAACL

  • Fan, D.P., Cheng, M.M., Liu, Y., Li, T., & Borji, A. (2017). Structure-measure: A new way to evaluate foreground maps. In: IEEE ICCV

  • Fan, D.P., Gong, C., Cao, Y., Ren, B., Cheng, M.M., & Borji, A. (2018). Enhanced-alignment measure for binary foreground map evaluation. In: IJCAI

  • Fan, D. P., Ji, G. P., Cheng, M. M., & Shao, L. (2022). Concealed object detection. IEEE TPAMI, 44(10), 6024–6042.

    Article  Google Scholar 

  • Fan, D.P., Ji, G.P., Sun, G., Cheng, M.M., Shen, J., & Shao, L. (2020). Camouflaged object detection. In: IEEE CVPR

  • Fan, D.P., Ji, G.P., Zhou, T., Chen, G., Fu, H., Shen, J., & Shao, L. (2020). Pranet: Parallel reverse attention network for polyp segmentation. In: MICCAI

  • Fan, D. P., Zhang, J., Xu, G., Cheng, M. M., & Shao, L. (2023). Salient objects in clutter. IEEE TPAMI, 45(2), 2344–2366.

    Article  Google Scholar 

  • Pérez-de la Fuente, R., Delclòs, X., Peñalver, E., Speranza, M., Wierzchos, J., Ascaso, C., & Engel, M. S. (2012). Early evolution and ecology of camouflage in insects. Proceedings of the National Academy of Sciences, 109(52), 21414–21419.

    Article  Google Scholar 

  • Han, K., Xiao, A., Wu, E., Guo, J., Xu, C., & Wang, Y. (2021) Transformer in transformer. In: NeurIPS

  • He, C., Li, K., Zhang, Y., Tang, L., Zhang, Y., Guo, Z., & Li, X. (2023) Camouflaged object detection with feature decomposition and edge reconstruction. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 22046–22055

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

  • Hou, Q., Cheng, M. M., Hu, X., Borji, A., Tu, Z., & Torr, P. (2019). Deeply supervised salient object detection with short connections. IEEE TPAMI, 41(4), 815–828.

    Article  Google Scholar 

  • Hu, X., Wang, S., Qin, X., Dai, H., Ren, W., Luo, D., Tai, Y., & Shao, L. (2023) High-resolution iterative feedback network for camouflaged object detection. In: AAAI

  • Huang, Z., Dai, H., Xiang, T.Z., Wang, S., Chen, H.X., Qin, J., & Xiong, H. (2023) Feature shrinkage pyramid for camouflaged object detection with transformers. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 5557–5566

  • Jha, D., Smedsrud, P.H., Riegler, M.A., Halvorsen, P., Lange, T.d., Johansen, D., & Johansen, H.D. (2020) Kvasir-seg: A segmented polyp dataset. In: MMM

  • Ji, G. P., Fan, D. P., Chou, Y. C., Dai, D., Liniger, A., & Van Gool, L. (2022). Deep gradient learning for efficient camouflaged object detection. MIR. https://doi.org/10.1007/s11633-022-1365-9

    Article  Google Scholar 

  • Ji, G.P., Zhu, L., Zhuge, M., & Fu, K. (2022) Fast camouflaged object detection via edge-based reversible re-calibration network. PR 123, 108414

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

  • Lamdouar, H., Yang, C., Xie, W., & Zisserman, A. (2020) Betrayed by motion: Camouflaged object discovery via motion segmentation. In: ACCV

  • Le, T. N., Cao, Y., Nguyen, T. C., Le, M. Q., Nguyen, K. D., Do, T. T., Tran, M. T., & Nguyen, T. V. (2022). Camouflaged instance segmentation in-the-wild: Dataset, method, and benchmark suite. IEEE TIP, 31, 287–300.

    Google Scholar 

  • Le, T. N., Nguyen, T. V., Nie, Z., Tran, M. T., & Sugimoto, A. (2019). Anabranch network for camouflaged object segmentation. CVIU, 184, 45–56.

    Google Scholar 

  • Le, X., Mei, J., Zhang, H., Zhou, B., & Xi, J. (2020). A learning-based approach for surface defect detection using small image datasets. Neurocomputing

  • Li, A., Zhang, J., Lv, Y., Liu, B., Zhang, T., & Dai, Y. (2021) Uncertainty-aware joint salient object and camouflaged object detection. In: IEEE CVPR

  • Li, C., & Jiao, G. (2022). Einet: camouflaged object detection with pyramid vision transformer. JEI, 31(5), 053002.

    Google Scholar 

  • Li, Z.Y., Gao, S., & Cheng, M.M. (2022) Exploring feature self-relation for self-supervised transformer. arXiv preprint arXiv:2206.05184

  • Lin, J., Tan, X., Xu, K., Ma, L., & Lau, R. W. (2023). Frequency-aware camouflaged object detection. ACM TMCCA, 19(2), 1–16.

    Google Scholar 

  • Lin, T. Y., Dollár, P., Girshick, R., He, K., Hariharan, B., & Belongie, S. (2017) Feature pyramid networks for object detection. In: IEEE CVPR

  • Liu, Y., Cheng, M. M., Fan, D. P., Zhang, L., Bian, J. W., & Tao, D. (2022). Semantic edge detection with diverse deep supervision. International Journal of Computer Vision, 130(1), 179–198.

    Article  Google Scholar 

  • Liu, Z., Zhang, Z., & Wu, W. (2022) Boosting camouflaged object detection with dual-task interactive transformer. ICPR

  • Loshchilov, I., & Hutter, F. (2017) Sgdr: Stochastic gradient descent with warm restarts. ICLR

  • Lv, Y., Zhang, J., Dai, Y., Li, A., Barnes, N., & Fan, D. P. (2023) Towards deeper understanding of camouflaged object detection. IEEE TCSVT

  • Lv, Y., Zhang, J., Dai, Y., Li, A., Liu, B., Barnes, N., & Fan, D. P. (2021) Simultaneously localize, segment and rank the camouflaged objects. In: IEEE CVPR

  • Margolin, R., Zelnik-Manor, L., & Tal, A. (2014) How to evaluate foreground maps? In: IEEE CVPR

  • Máttyus, G., Luo, W., & Urtasun, R. (2017) Deeproadmapper: Extracting road topology from aerial images. In: IEEE ICCV

  • Mei, H., Ji, G.P., Wei, Z., Yang, X., Wei, X., & Fan, D. P. (2021) Camouflaged object segmentation with distraction mining. In: IEEE CVPR

  • Mei, H., Xu, K., Zhou, Y., Wang, Y., Piao, H., Wei, X., & Yang, X. (2023). Camouflaged object segmentation with omni perception. International Journal of Computer Vision, 131, 1–16.

    Article  Google Scholar 

  • Mei, H., Yang, X., Zhou, Y., Ji, G.P., Wei, X., & Fan, D.P. (2023) Distraction-aware camouflaged object segmentation. SCIS

  • Mondal, A., Ghosh, S., & Ghosh, A. (2017). Partially camouflaged object tracking using modified probabilistic neural network and fuzzy energy based active contour. International Journal of Computer Vision, 122, 116–148.

    Article  MathSciNet  Google Scholar 

  • Pang, Y., Zhao, X., Xiang, T.Z., Zhang, L., & Lu, H. (2022) Zoom in and out: A mixed-scale triplet network for camouflaged object detection. In: IEEE CVPR

  • Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., & Antiga, L., et al. (2019) Pytorch: An imperative style, high-performance deep learning library. In: NeurIPS

  • Perazzi, F., Krähenbühl, P., Pritch, Y., & Hornung, A. (2012) Saliency filters: Contrast based filtering for salient region detection. In: IEEE CVPR

  • Rahman, M. M., & Marculescu, R. (2023) Medical image segmentation via cascaded attention decoding. In: IEEE WACV

  • Ren, J., Hu, X., Zhu, L., Xu, X., Xu, Y., Wang, W., Deng, Z., & Heng, P. A. (2023). Deep texture-aware features for camouflaged object detection. IEEE TCSVT, 33(3), 1157–1167.

    Google Scholar 

  • Silva, J., Histace, A., Romain, O., Dray, X., & Granado, B. (2014). Toward embedded detection of polyps in wce images for early diagnosis of colorectal cancer. International Journal of Computer Assisted Radiology and Surgery, 9, 283–293.

    Article  Google Scholar 

  • Srinivas, A., Lin, T.Y., Parmar, N., Shlens, J., Abbeel, P., & Vaswani, A. (2021) Bottleneck transformers for visual recognition. In: IEEE CVPR

  • Sun, Y., Chen, G., Zhou, T., Zhang, Y., & Liu, N. (2021) Context-aware cross-level fusion network for camouflaged object detection. In: IJCAI

  • Sun, Y., Wang, S., Chen, C., & Xiang, T. Z. (2022) Boundary-guided camouflaged object detection. In: IJCAI

  • Tabernik, D., Šela, S., Skvarč, J., & Skočaj, D. (2020). Segmentation-based deep-learning approach for surface-defect detection. Journal of Intelligent Manufacturing, 31(3), 759–776.

    Article  Google Scholar 

  • Tajbakhsh, N., Gurudu, S. R., & Liang, J. (2015). Automated polyp detection in colonoscopy videos using shape and context information. IEEE TMI, 35(2), 630–644.

    Google Scholar 

  • Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., & Polosukhin, I. (2017) Attention is all you need. In: NeurIPS

  • Wang, H., Wang, X., Sun, F., & Song, Y. (2021) Camouflaged object segmentation with transformer. In: ICCSIP

  • Wang, W., Xie, E., Li, X., Fan, D. P., Song, K., Liang, D., Lu, T., Luo, P., & Shao, L. (2022). Pvt v2: Improved baselines with pyramid vision transformer. CVMJ, 8(3), 415–424.

    Google Scholar 

  • Wu, F., Li, X., Zhang, Y., & Hu, K. (2022) Transcoop: Cooperation of transformers and cnns for camouflaged object segmentation. In: IEEE ICME

  • Wu, H., Xiao, B., Codella, N., Liu, M., Dai, X., Yuan, L., & Zhang, L. (2021) Cvt: Introducing convolutions to vision transformers. In: IEEE ICCV

  • Wu, M., Zhang, X., Sun, X., Zhou, Y., Chen, C., Gu, J., Sun, X., & Ji, R. (2022) Difnet: Boosting visual information flow for image captioning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 18020–18029

  • Xie, S., & Tu, Z. (2015) Holistically-nested edge detection. In: IEEE ICCV

  • Xu, B., Liang, H., Liang, R., & Chen, P. (2021) Locate globally, segment locally: A progressive architecture with knowledge review network for salient object detection. In: AAAI

  • Xu, W., Xu, Y., Chang, T., & Tu, Z. (2021) Co-scale conv-attentional image transformers. In: IEEE ICCV

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

  • Yang, J., Li, C., Zhang, P., Dai, X., Xiao, B., Yuan, L., & Gao, J. (2021) Focal attention for long-range interactions in vision transformers. In: NeurIPS

  • Yin, B., Zhang, X., Hou, Q., Sun, B. Y., Fan, D. P., & Van Gool, L. (2022) Camoformer: Masked separable attention for camouflaged object detection. arXiv preprint arXiv:2212.06570

  • Zhai, Q., Li, X., Yang, F., Chen, C., Cheng, H., & Fan, D. P. (2021) Mutual graph learning for camouflaged object detection. In: CVPR

  • Zhai, Q., Li, X., Yang, F., Jiao, Z., Luo, P., Cheng, H., & Liu, Z. (2023). Mgl: Mutual graph learning for camouflaged object detection. IEEE TIP, 32, 1897–1910.

    Google Scholar 

  • Zhai, W., Cao, Y., Xie, H., & Zha, Z. J. (2022) Deep texton-coherence network for camouflaged object detection. IEEE TMM

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

  • Zhang, Q., Ge, Y., Zhang, C., & Bi, H. (2022) Tprnet: camouflaged object detection via transformer-induced progressive refinement network. TVCJ pp. 1–15

  • Zhang, X., Sun, X., Luo, Y., Ji, J., Zhou, Y., Wu, Y., Huang, F., & Ji, R. (2021) Rstnet: Captioning with adaptive attention on visual and non-visual words. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 15465–15474

  • Zheng, D., Zheng, X., Yang, L.T., Gao, Y., Zhu, C., & Ruan, Y. (2023) Mffn: Multi-view feature fusion network for camouflaged object detection. In: WACV

  • Zheng, H., Fu, J., Zha, Z.J., & Luo, J. (2019) Looking for the devil in the details: Learning trilinear attention sampling network for fine-grained image recognition. In: IEEE CVPR

  • Zhong, Y., Li, B., Tang, L., Kuang, S., Wu, S., & Ding, S. (2022) Detecting camouflaged object in frequency domain. In: IEEE CVPR

  • Zhou, Y., Li, Z., Guo, C. L., Bai, S., Cheng, M. M., & Hou, Q. (2023) Srformer: Permuted self-attention for single image super-resolution. In: ICCV

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

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Authors and Affiliations

  1. VCIP, CS Nankai University, Tianjin, China

    Bowen Yin, Xuying Zhang, Ming-Ming Cheng & Qibin Hou

  2. College of Electronic Science and Technology, NUDT, Changsha, China

    Li Liu & Yongxiang Liu

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  1. Bowen Yin
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  2. Xuying Zhang
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  3. Li Liu
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Yin, B., Zhang, X., Liu, L. et al. Camouflaged Object Detection with Adaptive Partition and Background Retrieval. Int J Comput Vis 133, 4877–4893 (2025). https://doi.org/10.1007/s11263-025-02406-6

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