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Mamba Capsule Routing Towards Part-Whole Relational Camouflaged Object Detection

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Abstract

The part-whole relational property endowed by Capsule Networks (CapsNets) has been known successful for camouflaged object detection due to its segmentation integrity. However, the previous Expectation Maximization (EM) capsule routing algorithm with heavy computation and large parameters obstructs this trend. The primary attribution behind lies in the pixel-level capsule routing. Alternatively, in this paper, we propose a novel mamba capsule routing at the type level. Specifically, we first extract the implicit latent state in mamba as capsule vectors, which abstract type-level capsules from pixel-level versions. These type-level mamba capsules are fed into the EM routing algorithm to get the high-layer mamba capsules, which greatly reduce the computation and parameters caused by the pixel-level capsule routing for part-whole relationships exploration. On top of that, to retrieve the pixel-level capsule features for further camouflaged prediction, we achieve this on the basis of the low-layer pixel-level capsules with the guidance of the correlations from adjacent-layer type-level mamba capsules. Extensive experiments on three widely used COD benchmark datasets demonstrate that our method significantly outperforms state-of-the-arts. Code has been available on https://github.com/Liangbo-Cheng/mamba_capsule.

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Data Availability

The datasets used and analyzed during the current study are available in the following public domain resources: - CAMO: https://github.com/ondyari/FaceForensics - COD10K: https://github.com/DengPingFan/SINet - NC4K: https://github.com/JingZhang617/COD-Rank-Localize-and-Segment The models and source data generated and analyzed during the current study are available from the corresponding author upon reasonable request.

Notes

  1. High-layer and whole-level can be applied in an interleaved manner.

  2. Low-layer and part-level can be applied in an interleaved manner.

References

  • Achanta, R., Hemami, S., Estrada, F., & Susstrunk, S. (2009). Frequency-tuned salient region detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 1597–1604.

  • Bideau, P., Learned-Miller, E., Schmid, C., & Alahari, K. (2024). The right spin: Learning object motion from rotation-compensated flow fields. International Journal of Computer Vision, 132(1), 40–55.

    Article  Google Scholar 

  • Cai, L., McGuire, N. E., Hanlon, R., Mooney, T. A., & Girdhar, Y. (2023). Semi-supervised visual tracking of marine animals using autonomous underwater vehicles. International Journal of Computer Vision, 131(6), 1406–1427.

    Article  Google Scholar 

  • Chen, R., Shen, H., Zhao, Z. Q., Yang, Y., & Zhang, Z. (2024). Global routing between capsules. Pattern Recognition,148, Article 110142.

  • Chen, X., & Schmitt, F. (1993). Vision-based construction of cad models from range images. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp 129–136.

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

  • Fan, D. P., Cheng, M. M., Liu, Y., Li, T., & Borji, A. (2017). Structure-measure: A new way to evaluate foreground maps. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp 4558–4567

  • Fan, D. P., Gong, C., Cao, Y., Ren, B., Cheng, M. M., & Borji, A. (2018). Enhanced-alignment measure for binary foreground map evaluation. In: Proceedings of the International Joint Conference on Artificial Intelligence, pp 698–704.

  • Fan, D. P., Ji, G. P., Sun, G., Cheng, M. M., Shen, J., & Shao, L. (2020). Camouflaged object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 2774–2784.

  • Fan, D. P., Ji, G. P., Cheng, M. M., & Shao, L. (2022). Concealed object detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(10), 6024–6042.

    Article  Google Scholar 

  • Fazekas, S., Amiaz, T., Chetverikov, D., & Kiryati, N. (2009). Dynamic texture detection based on motion analysis. International Journal of Computer Vision, 82(1), 48–63.

    Article  Google Scholar 

  • Gan, D., Chang, M., & Chen, J. (2024). 3d-effivitcaps: 3d efficient vision transformer with capsule for medical image segmentation. In: International Conference on Pattern Recognition, pp 141–156.

  • Geng, X., Wang, J., Gong, J., Xue, Y., Xu, J., Chen, F., & Huang, X. (2024). Orthcaps: An orthogonal capsnet with sparse attention routing and pruning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 6037–6046.

  • Girshick, R., Iandola, F., Darrell, T., & Malik, J. (2015). Deformable part models are convolutional neural networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 437–446.

  • Gu, A., & Dao, T. (2024). Mamba: Linear-time sequence modeling with selective state spaces. arXiv:2312.00752

  • Gu, A., Goel, K., & Re, C. (2022). Efficiently modeling long sequences with structured state spaces. In: Proceedings of the International Conference on Learning Representations.

  • Gu, A., Johnson, I., Goel, K., Saab, K., Dao, T., Rudra, A., & Ré, C. (2024). Combining recurrent, convolutional, and continuous-time models with linear state-space layers. In: Advances in Neural Information Processing Systems.

  • Guo, G., Zhang, D., Han, L., Liu, N., Cheng, M. M., & Han, J. (2024). Pixel distillation: Cost-flexible distillation across image sizes and heterogeneous networks. IEEE Transactions on Pattern Analysis and Machine Intelligence.

  • Hahn, T., Pyeon, M., & Kim, G. (2019). Self-routing capsule networks. Advances in Neural Information Processing Systems 32.

  • Han, Y., Meng, W., & Tang, W. (2023). Capsule-inferenced object detection for remote sensing images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 16, 5260–5270.

    Article  Google Scholar 

  • Hao, C., Yu, Z., Liu, X., Xu, J., Yue, H., & Yang, J. (2025). A simple yet effective network based on vision transformer for camouflaged object and salient object detection. Processing: IEEE Transactions on Image.

    Book  Google Scholar 

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

    Google Scholar 

  • He, C., Li, K., Zhang, Y., Zhang, Y., You, C., Guo, Z., Li, X., Danelljan, M., & Yu, F. (2024). Strategic preys make acute predators: Enhancing camouflaged object detectors by generating camouflaged objects. In: Proceedings of the International Conference on Learning Representations.

  • Hinton, G. (2019). Talk on capsule networks. https://www.youtube.com/watch?v=x5Vxk9twXlE, york University.

  • Hinton, G. E., Krizhevsky, A., & Wang, S. D. (2011). Transforming auto-encoders. In: Proceedings of the International Conference on Artificial Neural Networks, pp 44–51.

  • Hinton, G. E., Sabour, S., & Frosst, N. (2018). Matrix capsules with em routing. In: Proceedings of the International Conference on Learning Representations, pp 3856–3866.

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

    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. Proceedings of the AAAI Conference on Artificial Intelligence, 37, 881–889.

    Article  Google Scholar 

  • Huang, P., Zhang, D., Cheng, D., Han, L., Zhu, P., & Han, J. (2024). M-rrfs: A memory-based robust region feature synthesizer for zero-shot object detection. International Journal of Computer Vision pp 1–22.

  • Huerta, I., Rowe, D., Mozerov, M., & Gonzàlez, J. (2007). Improving background subtraction based on a casuistry of colour-motion segmentation problems. In: Pattern Recognition and Image Analysis, pp 475–482.

  • Jia, Q., Yao, S., Liu, Y., Fan, X., Liu, R., & Luo, Z. (2022). Segment, magnify and reiterate: Detecting camouflaged objects the hard way. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 4703–4712.

  • Kalman, R. E. (1960). A New Approach to Linear Filtering and Prediction Problems. Journal of Basic Engineering, 82(1), 35–45.

    Article  MathSciNet  Google Scholar 

  • Kingma, D. P., & Ba, J. (2015). Adam: A method for stochastic optimization. In: Proceedings of the International Conference on Learning Representations.

  • Krivic, J., & Solina, F. (2004). Part-level object recognition using superquadrics. Computer Vision and Image Understanding, 95(1), 105–126.

    Article  Google Scholar 

  • LaLonde, R., Khosravan, N., & Bagci, U. (2024). Deformable capsules for object detection. Advanced Intelligent Systems, 6(9), 2400044.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Liang, D., Zhou, X., Xu, W., Zhu, X., Zou, Z., Ye, X., Tan, X., & Bai, X. (2024). Pointmamba: A simple state space model for point cloud analysis. In: Advances in Neural Information Processing Systems.

  • Liu, J., Lin, R., Wu, G., Liu, R., Luo, Z., & Fan, X. (2024). Coconet: Coupled contrastive learning network with multi-level feature ensemble for multi-modality image fusion. International Journal of Computer Vision, 132(5), 1748–1775.

    Article  Google Scholar 

  • Liu, N., Zhang, N., Wan, K., Shao, L., & Han, J. (2021). Visual saliency transformer. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp 4702–4712.

  • Liu, W., Shen, X., Pun, C. M., & Cun, X. (2023). Explicit visual prompting for low-level structure segmentations. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 19434–19445.

  • Liu, Y., Zhang, Q., Zhang, D., & Han, J. (2019). Employing deep part-object relationships for salient object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp 1232–1241.

  • Liu, Y., Zhang, D., Zhang, Q., & Han, J. (2021). Integrating part-object relationship and contrast for camouflaged object detection. IEEE Transactions on Information Forensics and Security, 16, 5154–5166.

    Article  Google Scholar 

  • Liu, Y., Zhang, D., Liu, N., Xu, S., & Han, J. (2022). Disentangled capsule routing for fast part-object relational saliency. IEEE Transactions on Image Processing, 31, 6719–6732.

    Article  Google Scholar 

  • Liu, Y., Zhang, D., Zhang, Q., & Han, J. (2022). Part-object relational visual saliency. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(7), 3688–3704.

    Google Scholar 

  • Liu, Y., Cheng, D., Zhang, D., Xu, S., & Han, J. (2024). Capsule networks with residual pose routing. IEEE Transactions on Neural Networks and Learning Systems pp 1–14.

  • Liu, Y., Tian, Y., Zhao, Y., Yu, H., Xie, L., Wang, Y., Ye, Q., Jiao, J., & Liu, Y. (2024). Vmamba: Visual state space model. Advances in Neural Information Processing Systems, 37, 103031–103063.

    Google Scholar 

  • Liu, Y., Li, C., Dong, X., Li, L., Zhang, D., Xu, S., & Han, J. (2025). Seamless detection: Unifying salient object detection and camouflaged object detection. Expert Systems with Applications,274, Article 126912.

  • Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., & Guo, B. (2021). Swin transformer: Hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp 9992–10002.

  • Liu, Z., Zhang, Z., Tan, Y., & Wu, W. (2022). Boosting camouflaged object detection with dual-task interactive transformer. In: Proceedings of the International Conference on Pattern Recognition, pp 140–146.

  • Liu, Z., Deng, X., Jiang, P., Lv, C., Min, G., & Wang, X. (2024). Edge perception camouflaged object detection under frequency domain reconstruction. IEEE Transactions on Circuits and Systems for Video Technology, 34(10), 10194–10207.

    Article  Google Scholar 

  • Luo, N., Pan, Y., Sun, R., Zhang, T., Xiong, Z., & Wu, F. (2023). Camouflaged instance segmentation via explicit de-camouflaging. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 17918–17927.

  • Luo, Z., Liu, N., Zhao, W., Yang, X., Zhang, D., Fan, D. P., Khan, F., & Han, J. (2024). Vscode: General visual salient and camouflaged object detection with 2d prompt learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 17169–17180.

  • 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: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 11586–11596.

  • Ma, J., Li, F., & Wang, B. (2024). U-mamba: Enhancing long-range dependency for biomedical image segmentation. arXiv:2401.04722

  • Margolin, R., Zelnik-Manor, L., & Tal, A. (2014). How to evaluate foreground maps. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 248–255.

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

  • 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(11), 3019–3034.

    Article  Google Scholar 

  • Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C. L., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., Schulman, J., Hilton, J., Kelton, F., Miller, L., Simens, M., Askell, A., Welinder, P., Christiano, P. F., Leike, J., & Lowe, R. (2022). Training language models to follow instructions with human feedback. In: Advances in Neural Information Processing Systems.

  • Pan, Y., Chen, Y., Fu, Q., Zhang, P., & Xu, X. (2011). Study on the camouflaged target detection method based on 3d convexity. Mathematical Models and Methods in Applied Sciences, 5, 152.

    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: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 2160–2170.

  • Rahmon, G., Palaniappan, K., Toubal, I. E., Bunyak, F., Rao, R., & Seetharaman, G. (2024). Deepftsg: Multi-stream asymmetric use-net trellis encoders with shared decoder feature fusion architecture for video motion segmentation. International Journal of Computer Vision, 132(3), 776–804.

    Article  Google Scholar 

  • Sabour, S., Frosst, N., & Hinton, G. E. (2017). Dynamic routing between capsules. In: Advances in Neural Information Processing Systems, pp 3859–3869.

  • Sengottuvelan, P., Wahi, A., & Shanmugam, A. (2008). Performance of decamouflaging through exploratory image analysis. In: International Conference on Emerging Trends in Engineering and Technology, pp 6–10.

  • Singh, S. K., Dhawale, C. A., & Misra, S. (2013). Survey of object detection methods in camouflaged image. Ieri Procedia, 4, 351–357.

    Article  Google Scholar 

  • Sun, Y., Chen, G., Zhou, T., Zhang, Y., & Liu, N. (2021). Context-aware cross-level fusion network for camouflaged object detection. In: Proceedings of the International Joint Conference on Artificial Intelligence, pp 1025–1031.

  • Sun, Y., Wang, S., Chen, C., & Xiang, T. Z. (2022). Boundary-guided camouflaged object detection. In: Proceedings of the International Joint Conference on Artificial Intelligence, pp 1335–1341.

  • Sun, Y., Xu, C., Yang, J., Xuan, H., & Luo, L. (2024). Frequency-spatial entanglement learning for camouflaged object detection. In: Proceedings of the European Conference on Computer Vision, pp 343–360.

  • Tsai, Y. H. H., Srivastava, N., Goh, H., & Salakhutdinov, R. (2020). Capsules with inverted dot-product attention routing. In: Proceedings of the International Conference on Learning Representations.

  • Wang, J., Liu, X., Yin, Z., Wang, Y., Guo, J., Qin, H., Wu, Q., & Liu, A. (2024). Generate transferable adversarial physical camouflages via triplet attention suppression. International Journal of Computer Vision.

  • Xiao, F., Hu, S., Shen, Y., Fang, C., Huang, J., Tang, L., Yang, Z., Li, X., & He, C. (2024). A survey of camouflaged object detection and beyond. CAAI Artificial Intelligence Research, 3, 9150044.

    Article  Google Scholar 

  • 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: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp 4126–4135.

  • Yang, X., Burghardt, T., & Mirmehdi, M. (2023). Dynamic curriculum learning for great ape detection in the wild. International Journal of Computer Vision, 131(5), 1163–1181.

    Article  Google Scholar 

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

  • Yu, J., Jiang, Y., Wang, Z., Cao, Z., & Huang, T. (2016). Unitbox: An advanced object detection network. In: Proceedings of the ACM International Conference on Multimedia, pp 516–520.

  • Zhai, Q., Li, X., Yang, F., Chen, C., Cheng, H., & Fan, D. P. (2021). Mutual graph learning for camouflaged object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 12997–13007.

  • Zhai, Q., Li, X., Yang, F., Chen, C., Cheng, H., & Fan, D. P. (2021). Mutual graph learning for camouflaged object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 12997–13007.

  • Zhang, Q., Ge, Y., Zhang, C., & Bi, H. (2023). Tprnet: camouflaged object detection via transformer-induced progressive refinement network. The Visual Computer, 39(10), 4593–4607.

    Article  Google Scholar 

  • Zhang, X., Han, L., Xu, C., Zheng, Z., Ding, J., Fu, X., & Zhang, D. (2024). Mhkd: Multi-step hybrid knowledge distillation for low-resolution whole slide images glomerulus detection. IEEE Journal of Biomedical and Health Informatics.

  • Zhao, J., Li, X., Yang, F., Zhai, Q., Luo, A., Jiao, Z., & Cheng, H. (2024). Focusdiffuser: Perceiving local disparities for camouflaged object detection. In: Proceedings of the European Conference on Computer Vision, pp 181–198.

  • Zhao, X., Zhang, L., & Lu, H. (2021). Automatic polyp segmentation via multi-scale subtraction network. Medical Image Computing and Computer Assisted Intervention, 12901, 120–130.

    Google Scholar 

  • Zhao, X., Pang, Y., Ji, W., Sheng, B., Zuo, J., Zhang, L., & Lu, H. (2024). Spider: A unified framework for context-dependent concept segmentation. In: Proceedings of the International Conference on Machine Learning.

  • Zhao, X., Pang, Y., Zhang, L., Lu, H., & Zhang, L. (2024). Towards diverse binary segmentation via a simple yet general gated network. International Journal of Computer Vision, 132, 1–20.

    Article  Google Scholar 

  • Zhou, T., Zhou, Y., Gong, C., Yang, J., & Zhang, Y. (2022). Feature aggregation and propagation network for camouflaged object detection. IEEE Transactions on Image Processing, 31, 7036–7047.

    Article  Google Scholar 

  • Zhou, X., Wu, Z., & Cong, R. (2024). Decoupling and integration network for camouflaged object detection. IEEE Transactions on Multimedia, 26, 7114–7129.

    Article  Google Scholar 

  • 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. Proceedings of the AAAI Conference on Artificial Intelligence, 36, 3608–3616.

    Article  Google Scholar 

  • Zhu, L., Liao, B., Zhang, Q., Wang, X., Liu, W., & Wang, X. (2024). Vision mamba: Efficient visual representation learning with bidirectional state space model. In: Proceedings of the International Conference on Machine Learning.

  • Zhuge, M., Fan, D. P., Liu, N., Zhang, D., Xu, D., & Shao, L. (2023). Salient object detection via integrity learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(3), 3738–3752.

    Google Scholar 

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Acknowledgements

This work was supported in part by the National Science and Technology Major Project (2022ZD0119004), in part by the National Natural Science Foundation of China under Graint 62322605, in part by Jiangsu Province Qing Lan Project, in part by National Natural Science Foundation of Jiangsu Province under Grant No. BK20221379, and in part by the Jiangsu Province Youth Science and Technology Talent Support Project.

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Authors and Affiliations

  1. Northwestern Polytechnical University, Xi’an, 710129, China

    Dingwen Zhang & Liangbo Cheng

  2. Changzhou University, Changzhou, 213000, China

    Yi Liu

  3. Huazhong University of Science and Technology, Wuhan, 430074, China

    Xinggang Wang

  4. School of Artifical Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China

    Junwei Han

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Zhang, D., Cheng, L., Liu, Y. et al. Mamba Capsule Routing Towards Part-Whole Relational Camouflaged Object Detection. Int J Comput Vis 133, 7201–7221 (2025). https://doi.org/10.1007/s11263-025-02530-3

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