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JointContrast: Skeleton-Based Mutual Action Recognition with Contrastive Learning

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PRICAI 2022: Trends in Artificial Intelligence (PRICAI 2022)

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Abstract

Skeleton-based action recognition relies on skeleton sequences to detect certain categories of human actions. In skeleton-based action recognition, it is observed that many scenes are mutual actions characterized by more than one subject, and the existing works deal with subjects independently or use the pooling layer for feature fusion leading to ineffective learning and fusion of different subjects. In this paper, we propose a novel framework, JointContrast, for Skeleton-based action recognition to deal with these challenges. Our JointContrast includes two innovative components. One is the pre-training process with a fine-grained contrastive loss that effectively enhances the representation ability of the model, and the other is an Interactive Graph (IG) representation for skeletal sequences that contributes to the fusion of features between subjects. We validate our JointContrast in the popular SBU and NTU RGB-D datasets, and experimental results show that our model outperforms other baseline methods in terms of recognition accuracy.

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References

  1. Liu, J., Shahroudy, A., Xu, D., Wang, G.: Spatio-temporal lstm with trust gates for 3d human action recognition. In: European Conference on Computer Vision, pp. 816–833. Springer, Cham (2016)

    Google Scholar 

  2. Ke, Q., Bennamoun, M., An, S., Sohel, F., Boussaid, F.: A new representation of skeleton sequences for 3d action recognition. In: Proceedings of The IEEE Conference on Computer Vision and Pattern Recognition, pp. 3288–3297. IEEE, Honolulu (2017)

    Google Scholar 

  3. Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition. In: Thirty-second AAAI Conference on Artificial Intelligence. AAAI, New Orleans (2018)

    Google Scholar 

  4. Li, M., Chen, S., Chen, X., Zhang, Y., Wang, Y., Tian, Q.: Actional-structural graph convolutional networks for skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3595–3603. IEEE, Long Beach (2019)

    Google Scholar 

  5. Shi, L., Zhang, Y., Cheng, J., Lu, H.: Skeleton-based action recognition with directed graph neural networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7912–7921. IEEE, Long Beach (2019)

    Google Scholar 

  6. Devlin, J., Chang, M. W., Lee, K., Toutanova, K.: Bert: pre-training of deep bidirectional transformers for language understanding. arXiv preprint, arXiv:1810.04805 (2018)

  7. Bachman, P., Hjelm, R.D., Buchwalter, W.: Learning representations by maximizing mutual information across views. Advances in neural information processing systems, 32 (2019)

    Google Scholar 

  8. Liu, J., Shahroudy, A., Perez, M., Wang, G., Duan, L.Y., Kot, A.C.: Ntu rgb+ d 120: a large-scale benchmark for 3d human activity understanding. IEEE Trans. Pattern Anal. Mach. Intell. 42(10), 2684–2701 (2019)

    Article  Google Scholar 

  9. Si, C., Chen, W., Wang, W., Wang, L., Tan, T.: An attention enhanced graph convolutional lstm network for skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1227–1236. IEEE, Long Beach (2019)

    Google Scholar 

  10. Shi, L., Zhang, Y., Cheng, J., Lu, H.: Decoupled spatial-temporal attention network for skeleton-based action-gesture recognition. In: Proceedings of the Asian Conference on Computer Vision (2020)

    Google Scholar 

  11. Liu, Y., Zhang, H., Xu, D., He, K.: Graph transformer network with Temporal Kernel Attention for skeleton-based action recognition. Knowledge-Based Syst. 240, 108146 (2022)

    Article  Google Scholar 

  12. Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607. PMLR (2020)

    Google Scholar 

  13. Singh, A., Chakraborty, O., Varshney, A., Panda, R., Feris, R., Saenko, K., Das, A.: Semi-supervised action recognition with temporal contrastive learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10389–10399. IEEE (2021)

    Google Scholar 

  14. Van den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding. arXiv e-prints, arXiv-1807 (2018)

    Google Scholar 

  15. Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sutskever, I.: Learning transferable visual models from natural language supervision. In: International Conference on Machine Learning, pp. 8748–8763. PMLR (2021)

    Google Scholar 

  16. He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.: Momentum contrast for unsupervised visual representation learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9729–9738. IEEE (2020)

    Google Scholar 

  17. Yun, K., Honorio, J., Chattopadhyay, D., Berg, T. L., Samaras, D.: Two-person interaction detection using body-pose features and multiple instance learning. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp. 28–35. IEEE (2012)

    Google Scholar 

  18. Shahroudy, A., Liu, J., Ng, T. T., Wang, G.: Ntu rgb+ d: A large scale dataset for 3d human activity analysis. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1010–1019. IEEE, Las Vegas (2016)

    Google Scholar 

  19. Ji, Y., Cheng, H., Zheng, Y., Li, H.: Learning contrastive feature distribution model for interaction recognition. J. Vis. Commun. Image Represent. 33, 340–349 (2015)

    Article  Google Scholar 

  20. Zhu, W., Lan, C., Xing, J., Zeng, W., Li, Y., Shen, L., Xie, X.: Co-occurrence feature learning for skeleton based action recognition using regularized deep LSTM networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 30, No. 1. AAAI, Phoenix (2016)

    Google Scholar 

  21. Liu, J., Wang, G., Duan, L.Y., Abdiyeva, K., Kot, A.C.: Skeleton-based human action recognition with global context-aware attention LSTM networks. IEEE Trans. Image Process. 27(4), 1586–1599 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  22. Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View adaptive recurrent neural networks for high performance human action recognition from skeleton data. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2117–2126. IEEE, Honolulu (2017)

    Google Scholar 

  23. Perez, M., Liu, J., Kot, A.C.: Interaction relational network for mutual action recognition. IEEE Trans. Multimed. 24, 366–376 (2021)

    Article  Google Scholar 

  24. Wu, F., Souza, A., Zhang, T., Fifty, C., Yu, T., Weinberger, K.: Simplifying graph convolutional networks. In: International Conference on Machine Learning, pp. 6861–6871. PMLR. Long Beach (2019)

    Google Scholar 

  25. Cho, S., Maqbool, M., Liu, F., Foroosh, H.: Self-attention network for skeleton-based human action recognition. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 635–644. IEEE, Snowmass Village (2020)

    Google Scholar 

  26. Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N.: View adaptive neural networks for high performance skeleton-based human action recognition. IEEE Trans. Pattern Anal. Mach. Intell. 41(8), 1963–1978 (2019)

    Article  Google Scholar 

  27. Li, M., Chen, S., Chen, X., Zhang, Y., Wang, Y., Tian, Q.: Actional-structural graph convolutional networks for skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3595–3603. IEEE, Long Beach (2019)

    Google Scholar 

  28. Plizzari, C., Cannici, M., Matteucci, M.: Spatial temporal transformer network for skeleton-based action recognition. In International Conference on Pattern Recognition, pp. 694–701. Springer, Cham (2021)

    Google Scholar 

  29. Cheng, K., Zhang, Y., He, X., Chen, W., Cheng, J., Lu, H.: Skeleton-based action recognition with shift graph convolutional network. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 183–192. IEEE (2020)

    Google Scholar 

  30. Liu, Z., Zhang, H., Chen, Z., Wang, Z., Ouyang, W.: Disentangling and unifying graph convolutions for skeleton-based action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 143–152. IEEE (2020)

    Google Scholar 

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Acknowledgment

This research is partially supported by Zhejiang Lab (No. 2022PI0AC03 and No. 111010-AN2201) and National Natural Science Foundation of China (61972438).

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Authors and Affiliations

  1. Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Xiangze Jia

  2. University of Southern Queensland, Darling Heights, Australia

    Ji Zhang

  3. Zhejiang Lab, Hangzhou City, China

    Zhen Wang

  4. Anhui Normal University, Wuhu, China

    Yonglong Luo & Fulong Chen

  5. South China Normal University, Guangzhou, China

    Jing Xiao

Authors
  1. Xiangze Jia
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  2. Ji Zhang
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  3. Zhen Wang
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  4. Yonglong Luo
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  5. Fulong Chen
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  6. Jing Xiao
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Corresponding author

Correspondence to Ji Zhang .

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Editors and Affiliations

  1. CSIRO Australian e-Health Research Centre, Brisbane, QLD, Australia

    Sankalp Khanna

  2. Shanghai Jiao Tong University, Shanghai, China

    Jian Cao

  3. University of Tasmania, Hobart, TAS, Australia

    Quan Bai

  4. University of Technology Sydney, Sydney, NSW, Australia

    Guandong Xu

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Jia, X., Zhang, J., Wang, Z., Luo, Y., Chen, F., Xiao, J. (2022). JointContrast: Skeleton-Based Mutual Action Recognition with Contrastive Learning. In: Khanna, S., Cao, J., Bai, Q., Xu, G. (eds) PRICAI 2022: Trends in Artificial Intelligence. PRICAI 2022. Lecture Notes in Computer Science, vol 13631. Springer, Cham. https://doi.org/10.1007/978-3-031-20868-3_35

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  • DOI: https://doi.org/10.1007/978-3-031-20868-3_35

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-20867-6

  • Online ISBN: 978-3-031-20868-3

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