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Doubly Deformable Aggregation of Covariance Matrices for Few-Shot Segmentation

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Computer Vision – ECCV 2022 (ECCV 2022)

Abstract

Training semantic segmentation models with few annotated samples has great potential in various real-world applications. For the few-shot segmentation task, the main challenge is how to accurately measure the semantic correspondence between the support and query samples with limited training data. To address this problem, we propose to aggregate the learnable covariance matrices with a deformable 4D Transformer to effectively predict the segmentation map. Specifically, in this work, we first devise a novel hard example mining mechanism to learn covariance kernels for the Gaussian process. The learned covariance kernel functions have great advantages over existing cosine similarity-based methods in correspondence measurement. Based on the learned covariance kernels, an efficient doubly deformable 4D Transformer module is designed to adaptively aggregate feature similarity maps into segmentation results. By combining these two designs, the proposed method can not only set new state-of-the-art performance on public benchmarks, but also converge extremely faster than existing methods. Experiments on three public datasets have demonstrated the effectiveness of our method. (Code: https://github.com/ShadowXZT/DACM-Few-shot.pytorch)

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References

  1. Boudiaf, M., Kervadec, H., Masud, Z.I., Piantanida, P., Ben Ayed, I., Dolz, J.: Few-shot segmentation without meta-learning: A good transductive inference is all you need? In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 13979–13988 (2021)

    Google Scholar 

  2. Dong, N., Xing, E.P.: Few-shot semantic segmentation with prototype learning. In: BMVC, vol. 3 (2018)

    Google Scholar 

  3. Dosovitskiy, A., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)

  4. Everingham, M., et al.: The pascal visual object classes challenge: A retrospective. Int. J. Comput. Vision 111(1), 98–136 (2015)

    Article  Google Scholar 

  5. Gardner, J.R., Pleiss, G., Bindel, D., Weinberger, K.Q., Wilson, A.G.: Gpytorch: Blackbox matrix-matrix gaussian process inference with gpu acceleration. arXiv preprint arXiv:1809.11165 (2018)

  6. Hao, S., Zhou, Y., Guo, Y.: A brief survey on semantic segmentation with deep learning. Neurocomputing 406, 302–321 (2020)

    Article  Google Scholar 

  7. Hariharan, B., Arbeláez, P., Girshick, R., Malik, J.: imultaneous detection and segmentation. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8695, pp. 297–312. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10584-0_20

    Chapter  Google Scholar 

  8. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

    Google Scholar 

  9. Hong, S., Cho, S., Nam, J., Kim, S.: Cost aggregation is all you need for few-shot segmentation. arXiv preprint arXiv:2112.11685 (2021)

  10. Hu, T., Yang, P., Zhang, C., Yu, G., Mu, Y., Snoek, C.G.: Attention-based multi-context guiding for few-shot semantic segmentation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 8441–8448 (2019)

    Google Scholar 

  11. Johnander, J., Edstedt, J., Felsberg, M., Khan, F.S., Danelljan, M.: Dense gaussian processes for few-shot segmentation. arXiv preprint arXiv:2110.03674 (2021)

  12. Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)

  13. Li, X., Wei, T., Chen, Y.P., Tai, Y.W., Tang, C.K.: Fss-1000: A 1000-class dataset for few-shot segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2869–2878 (2020)

    Google Scholar 

  14. Lin, T.-Y., et al.: Microsoft COCO: Common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_48

    Chapter  Google Scholar 

  15. Liu, H., Dai, Z., So, D., Le, Q.: Pay attention to mlps. In: Advances in Neural Information Processing Systems, vol. 34 (2021)

    Google Scholar 

  16. Liu, W., Zhang, C., Ding, H., Hung, T.Y., Lin, G.: Few-shot segmentation with optimal transport matching and message flow. arXiv preprint arXiv:2108.08518 (2021)

  17. Liu, Y., Zhang, X., Zhang, S., He, X.: Part-aware prototype network for few-shot semantic segmentation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12354, pp. 142–158. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58545-7_9

    Chapter  Google Scholar 

  18. Liu, Z., et al.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 10012–10022 (2021)

    Google Scholar 

  19. Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)

    Google Scholar 

  20. Min, J., Kang, D., Cho, M.: Hypercorrelation squeeze for few-shot segmentation. arXiv preprint arXiv:2104.01538 (2021)

  21. Nguyen, K., Todorovic, S.: Feature weighting and boosting for few-shot segmentation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 622–631 (2019)

    Google Scholar 

  22. Paszke, A., et al.: Pytorch: An imperative style, high-performance deep learning library. Adv. Neural. Inf. Process. Syst. 32, 8026–8037 (2019)

    Google Scholar 

  23. Patacchiola, M., Turner, J., Crowley, E.J., O’Boyle, M., Storkey, A.J.: Bayesian meta-learning for the few-shot setting via deep kernels. In: Advances in Neural Information Processing Systems, vol. 33 (2020)

    Google Scholar 

  24. Rakelly, K., Shelhamer, E., Darrell, T., Efros, A., Levine, S.: Conditional networks for few-shot semantic segmentation (2018)

    Google Scholar 

  25. Russakovsky, O., et al.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vision 115(3), 211–252 (2015)

    Article  MathSciNet  Google Scholar 

  26. Shaban, A., Bansal, S., Liu, Z., Essa, I., Boots, B.: One-shot learning for semantic segmentation. arXiv preprint arXiv:1709.03410 (2017)

  27. Siam, M., Oreshkin, B., Jagersand, M.: Adaptive masked proxies for few-shot segmentation. arXiv preprint arXiv:1902.11123 (2019)

  28. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)

  29. Snell, J., Swersky, K., Zemel, R.S.: Prototypical networks for few-shot learning. arXiv preprint arXiv:1703.05175 (2017)

  30. Snell, J., Zemel, R.: Bayesian few-shot classification with one-vs-each pólya-gamma augmented gaussian processes. arXiv preprint arXiv:2007.10417 (2020)

  31. Sun, H., et al.: Attentional prototype inference for few-shot semantic segmentation. arXiv preprint arXiv:2105.06668 (2021)

  32. Tian, Z., Zhao, H., Shu, M., Yang, Z., Li, R., Jia, J.: Prior guided feature enrichment network for few-shot segmentation. IEEE Trans. Pattern Anal. Mach. Intel. 01, 1–1 (2020)

    Article  Google Scholar 

  33. Tossou, P., Dura, B., Laviolette, F., Marchand, M., Lacoste, A.: Adaptive deep kernel learning. arXiv preprint arXiv:1905.12131 (2019)

  34. Vinyals, O., Blundell, C., Lillicrap, T., Wierstra, D., et al.: Matching networks for one shot learning. Adv. Neural. Inf. Process. Syst. 29, 3630–3638 (2016)

    Google Scholar 

  35. Wang, H., Yang, Y., Cao, X., Zhen, X., Snoek, C., Shao, L.: Variational prototype inference for few-shot semantic segmentation. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 525–534 (2021)

    Google Scholar 

  36. Wang, H., Zhang, X., Hu, Y., Yang, Y., Cao, X., Zhen, X.: Few-shot semantic segmentation with democratic attention networks. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12358, pp. 730–746. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58601-0_43

    Chapter  Google Scholar 

  37. Wang, K., Liew, J.H., Zou, Y., Zhou, D., Feng, J.: PANet: Few-shot image semantic segmentation with prototype alignment. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9197–9206 (2019)

    Google Scholar 

  38. Wilson, A.G., Hu, Z., Salakhutdinov, R., Xing, E.P.: Deep kernel learning. In: Artificial Intelligence and Statistics, pp. 370–378. PMLR (2016)

    Google Scholar 

  39. Xia, Z., Pan, X., Song, S., Li, L.E., Huang, G.: Vision transformer with deformable attention. arXiv preprint arXiv:2201.00520 (2022)

  40. Yang, B., Liu, C., Li, B., Jiao, J., Ye, Q.: Prototype mixture models for few-shot semantic segmentation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12353, pp. 763–778. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58598-3_45

    Chapter  Google Scholar 

  41. Yang, Y., Soatto, S.: Fda: Fourier domain adaptation for semantic segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4085–4095 (2020)

    Google Scholar 

  42. Yang, Y., Meng, F., Li, H., Wu, Q., Xu, X., Chen, S.: A new local transformation module for few-shot segmentation. In: Ro, Y.M., et al. (eds.) MMM 2020. LNCS, vol. 11962, pp. 76–87. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-37734-2_7

    Chapter  Google Scholar 

  43. Zhang, C., Lin, G., Liu, F., Guo, J., Wu, Q., Yao, R.: Pyramid graph networks with connection attentions for region-based one-shot semantic segmentation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9587–9595 (2019)

    Google Scholar 

  44. Zhang, G., Kang, G., Yang, Y., Wei, Y.: Few-shot segmentation via cycle-consistent transformer. In: Advances in Neural Information Processing Systems, vol. 34 (2021)

    Google Scholar 

  45. Zhang, X., Wei, Y., Yang, Y., Huang, T.S.: Sg-one: Similarity guidance network for one-shot semantic segmentation. IEEE Trans. Cybern. 50(9), 3855–3865 (2020)

    Article  Google Scholar 

  46. Zheng, S., et al.: Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6881–6890 (2021)

    Google Scholar 

  47. Zhu, F., Zhu, Y., Zhang, L., Wu, C., Fu, Y., Li, M.: A unified efficient pyramid transformer for semantic segmentation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 2667–2677 (2021)

    Google Scholar 

  48. Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: Deformable transformers for end-to-end object detection. arXiv preprint arXiv:2010.04159 (2020)

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Acknowledgement

This work is jointly supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. [ERC-2016-StG-714087], Acronym: So2Sat), by the Helmholtz Association through the Framework of Helmholtz AI (grant number: ZT-I-PF-5-01) - Local Unit “Munich Unit @Aeronautics, Space and Transport (MASTr)” and Helmholtz Excellent Professorship “Data Science in Earth Observation - Big Data Fusion for Urban Research”(grant number: W2-W3-100), by the German Federal Ministry of Education and Research (BMBF) in the framework of the international future AI lab "AI4EO – Artificial Intelligence for Earth Observation: Reasoning, Uncertainties, Ethics and Beyond" (grant number: 01DD20001) and by German Federal Ministry of Economics and Technology in the framework of the "national center of excellence ML4Earth" (grant number: 50EE2201C).

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Xiong, Z., Li, H., Zhu, X.X. (2022). Doubly Deformable Aggregation of Covariance Matrices for Few-Shot Segmentation. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13680. Springer, Cham. https://doi.org/10.1007/978-3-031-20044-1_8

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