Skip to main content
SpringerLink
Log in

SCGTS: Semantic Content Guiding Teacher-Student Network for Group Activity Recognition

  • Conference paper
  • First Online:
Image and Graphics Technologies and Applications (IGTA 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1910))

Included in the following conference series:

Abstract

Group activity recognition refers to the process of comprehending the activity performed by multi-person in a video. However, most methods need predefined individual labels during training or testing, which is impractical and lacks intelligence. Moreover, they only consider visual features and ignore corresponding semantic information. To address these issues, a Semantic Content Guiding Teacher-Student (SCGTS) network is developed. SCGTS depends neither on predefined individual labels nor on any detection methods. It utilizes a large-scale language model as the teacher network to extract content features from textual descriptions of labels. The semantic content features are then used to supervise the training of the baseline network which serves as the student network. In this way, the student network is enforced to mimic the teacher network to extract visual features with semantic information. Experiments on 2 challenging benchmarks, including Volleyball and NBA, demonstrate SCGTS outperforms the baseline network and achieves the leading performance.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    https://jr.nba.com.

  2. 2.

    https://www.fivb.com/en/volleyball.

  3. 3.

    https://en.wikipedia.org/wiki/main_page.

References

  1. Fan, L., Huang, W.: Identification of common molecular subsequences. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 6016–6025 (2001)

    Google Scholar 

  2. Girdhar, R., Carreira, J.: Video action transformer network. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 244–253 (2019)

    Google Scholar 

  3. Kwon, H., Kim, M., Kwak, S., Cho, M.: Motionsqueeze: neural motion feature learning for video understanding. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12361, pp. 345–362. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58517-4_21

    Chapter  Google Scholar 

  4. Piergiovanni, A.J., Ryoo, M.S.: Representation flow for action recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 9945–9953 (2019)

    Google Scholar 

  5. Wang, X., Girshick, R., Gupta, A.: Non-local neural networks. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 7794–7803 (2018)

    Google Scholar 

  6. Ehsanpour, M., Abedin, A., Saleh, F., Shi, J., Reid, I., Rezatofighi, H.: Joint learning of social groups, individuals action and sub-group activities in videos. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12354, pp. 177–195. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58545-7_11

    Chapter  Google Scholar 

  7. Gavrilyuk, K., Sanford, R., Javan, M.: Actor-transformers for group activity recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 839–848 (2020)

    Google Scholar 

  8. Hu, G., Cui, B., He, Y.: Progressive relation learning for group activity recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 980–989 (2020)

    Google Scholar 

  9. Li, S., Cao, Q.: GroupFormer: group activity recognition with clustered spatial-temporal transformer. In: IEEE International Conference on Computer Vision, pp. 13668–13677 (2021)

    Google Scholar 

  10. Yuan, H., Ni, D.: Learning visual context for group activity recognition. In: AAAI Conference on Artificial Intelligence, vol.35, pp. 3261–3269 (2021)

    Google Scholar 

  11. Wu, J., Wang, L.: Learning actor relation graphs for group activity recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 9964–9974 (2019)

    Google Scholar 

  12. Vaswani, A., Shazeer, N., Parmar, N.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)

    Google Scholar 

  13. Li, X., Choo Chuah, M.: SBGAR: semantics based group activity recognition. In: IEEE International Conference on Computer Vision, pp. 2876–2885 (2017)

    Google Scholar 

  14. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9, 1735–1780 (1997)

    Article  Google Scholar 

  15. Zhang, P., Tang, Y.: Fast collective activity recognition under weak supervision. IEEE Trans. Image Process. 29, 29–43 (2019)

    Article  MathSciNet  MATH  Google Scholar 

  16. Yan, R., Xie, L., Tang, J., Shu, X., Tian, Q.: Social adaptive module for weakly-supervised group activity recognition. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12353, pp. 208–224. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58598-3_13

    Chapter  Google Scholar 

  17. Kim, D., Lee, J.: Detector-free weakly supervised group activity recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 20083–20093 (2022)

    Google Scholar 

  18. Devlin, J., Chang, M.W.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018)

  19. Wu, L., Lang, X., Xiang, Y.: Active spatial positions based hierarchical relation inference for group activity recognition. IEEE Trans. Circuits Syst. Video Technol. 33(6), 2839–2851 (2023). https://doi.org/10.1109/TCSVT.2022.3228731

    Article  Google Scholar 

  20. Wu, L., Wang, Q., Jian, M.: A comprehensive review of group activity recognition in videos. Int. J. Autom. Comput. 18, 334–350 (2021)

    Article  Google Scholar 

  21. Lan, T., Wang, Y., Yang, W.: Discriminative latent models for recognizing contextual group activities. IEEE Trans. Pattern Anal. Mach. Intell. 34, 1549–1562 (2011)

    Article  Google Scholar 

  22. Hajimirsadeghi, H., Yan, W., Vahdat, A.: Visual recognition by counting instances: a multi-instance cardinality potential kernel. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2596–2605 (2015)

    Google Scholar 

  23. Shu, T., Xie, D., Rothrock, B.: Joint inference of groups, events and human roles in aerial videos. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 4576–4584 (2015)

    Google Scholar 

  24. Amer, M.R., Xie, D., Zhao, M., Todorovic, S., Zhu, S.-C.: Cost-sensitive top-down/bottom-up inference for multiscale activity recognition. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7575, pp. 187–200. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33765-9_14

    Chapter  Google Scholar 

  25. Shu, T., Todorovic, S., Zhu, S.C.: CERN: confidence-energy recurrent network for group activity recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 5523–5531 (2017)

    Google Scholar 

  26. Wang, M., Ni, B., Yang, X.: Recurrent modeling of interaction context for collective activity recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 3048–3056 (2017)

    Google Scholar 

  27. Yan, R., Xie, L., Tang, J.: HiGCIN: hierarchical graph-based cross inference network for group activity recognition. IEEE Trans. Pattern Anal. Mach. Intell. (2020)

    Google Scholar 

  28. Yuan, H., Ni, D., Wang, M.: Spatio-temporal dynamic inference network for group activity recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 7476–7485 (2021)

    Google Scholar 

  29. Pramono, R.R.A., Chen, Y.T., Fang, W.H.: Empowering relational network by self-attention augmented conditional random fields for group activity recognition. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12346, pp. 71–90. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58452-8_5

    Chapter  Google Scholar 

  30. Yang, Z., Dai, Z., Yang, Y.: XLNet: generalized autoregressive pretraining for language understanding. In: Advances in Neural Information Processing Systems, vol. 32 (2019)

    Google Scholar 

  31. Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. arXiv:1503.02531 (2015)

  32. Hao, W., Zhang, Z.: Spatiotemporal distilled dense-connectivity network for video action recognition. Pattern Recogn. 92, 13–23 (2019)

    Article  Google Scholar 

  33. Wu, M.C., Chiu, C.T.: Multi-teacher knowledge distillation for compressed video action recognition based on deep learning. J. Syst. Archit. 103, 101695 (2020). https://doi.org/10.1016/j.sysarc.2019.101695

    Article  Google Scholar 

  34. Bian, C., Feng, W., Wan, L.: Structural knowledge distillation for efficient skeleton-based action recognition. IEEE Trans. Image Process. 30, 2963–2976 (2021)

    Article  Google Scholar 

  35. Tang, Y., Wang, Z., Li, P.: Mining semantics-preserving attention for group activity recognition. In: 26th ACM International Conference on Multimedia, pp. 1283–1291 (2018)

    Google Scholar 

  36. Wang, L., Xiong, Y., Wang, Z.: Temporal segment networks: towards good practices for deep action recognition. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9912, pp. 20–36. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46484-8_2

    Chapter  Google Scholar 

  37. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv:1412.6980 (2014)

  38. Lin, J., Gan, C., Han, S.: TSM: temporal shift module for efficient video understanding. In: IEEE International Conference on Computer Vision, pp. 7083–9093 (2019)

    Google Scholar 

  39. Lin, Z., Ning, J., Cao, Y.: Video swin transformer. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 3202–3211 (2022)

    Google Scholar 

Download references

Acknowledgment

This work was supported in part by the National Natural Science Foundation of China under Grant NO. 62236010, 61976010, 62106011, 62106010, 62176011.

Author information

Authors and Affiliations

  1. Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China

    Zeyu Xi, Ge Shi, Lifang Wu & Xuefen Li

Authors
  1. Zeyu Xi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ge Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lifang Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xuefen Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lifang Wu .

Editor information

Editors and Affiliations

  1. Beijing Institute of Technology, Beijing, China

    Wang Yongtian

  2. Beijing University of Technology, Beijing, China

    Wu Lifang

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Xi, Z., Shi, G., Wu, L., Li, X. (2023). SCGTS: Semantic Content Guiding Teacher-Student Network for Group Activity Recognition. In: Yongtian, W., Lifang, W. (eds) Image and Graphics Technologies and Applications. IGTA 2023. Communications in Computer and Information Science, vol 1910. Springer, Singapore. https://doi.org/10.1007/978-981-99-7549-5_10

Download citation

  • DOI: https://doi.org/10.1007/978-981-99-7549-5_10

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-7548-8

  • Online ISBN: 978-981-99-7549-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation