Nothing Special   »   [go: up one dir, main page]

Skip to main content

ExpT: Online Action Detection via Exemplar-Enhanced Transformer for Secondary School Experimental Evaluation

  • Conference paper
  • First Online:
Computer Science and Education. Teaching and Curriculum (ICCSE 2023)

Abstract

Secondary school experimental evaluation is an essential component of secondary school science education. However, it faces several challenges, including obstacles to precise assessment within limited time and the presence of inconsistent evaluation criteria. Hence, it has become imperative to explore and harness artificial intelligence technology to improve secondary school experimental evaluation. Yet existing applicable online action detection (OAD) algorithms are hindered by limitation to historical context and inefficiency, leading to setbacks in realistic experimental evaluations. Based on this, we present Exemplar-enhanced Transformer (ExpT), a real-time mechanism for online action detection that more accurately and efficiently assesses the experiments conducted by students. By leveraging exemplars through temporal cross attention, the ExpT model provides complementary guidance for modeling temporal dependencies, along with the reduction of excessive attention. We evaluate ExpT on two realistic chemistry experiment datasets for online action detection, and it significantly outperforms all existing methods.

Supported by the Natural Science Foundation of China (Nos. 62377029, 22033002).

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 64.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 84.99
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

References

  1. Arnab, A., Dehghani, M., Heigold, G., Sun, C., Lučić, M., Schmid, C.: Vivit: a video vision transformer. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 6836–6846 (2021)

    Google Scholar 

  2. Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016)

  3. Cao, S., Luo, W., Wang, B., Zhang, W., Ma, L.: E2e-load: end-to-end long-form online action detection. arXiv preprint arXiv:2306.07703 (2023)

  4. Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-End object detection with transformers. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12346, pp. 213–229. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58452-8_13

    Chapter  Google Scholar 

  5. Chen, G., et al.: Videollm: modeling video sequence with large language models. arXiv preprint arXiv:2305.13292 (2023)

  6. Chen, J., Mittal, G., Yu, Y., Kong, Y., Chen, M.: Gatehub: gated history unit with background suppression for online action detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19925–19934 (2022)

    Google Scholar 

  7. De Geest, R., Gavves, E., Ghodrati, A., Li, Z., Snoek, C., Tuytelaars, T.: Online action detection. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9909, pp. 269–284. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46454-1_17

    Chapter  Google Scholar 

  8. 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)

  9. Dosovitskiy, A., et al.: An image is worth 16\(\times \)16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)

  10. Eun, H., Moon, J., Park, J., Jung, C., Kim, C.: Learning to discriminate information for online action detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 809–818 (2020)

    Google Scholar 

  11. Gao, M., Zhou, Y., Xu, R., Socher, R., Xiong, C.: Woad: weakly supervised online action detection in untrimmed videos. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1915–1923 (2021)

    Google Scholar 

  12. 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 

  13. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)

    Article  Google Scholar 

  14. Johnson, J., Douze, M., Jégou, H.: Billion-scale similarity search with gpus. IEEE Trans. Big Data 7(3), 535–547 (2019)

    Article  Google Scholar 

  15. Kim, J., Misu, T., Chen, Y.T., Tawari, A., Canny, J.: Grounding human-to-vehicle advice for self-driving vehicles. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10591–10599 (2019)

    Google Scholar 

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

  17. Nawhal, M., Mori, G.: Activity graph transformer for temporal action localization. arXiv preprint arXiv:2101.08540 (2021)

  18. Neimark, D., Bar, O., Zohar, M., Asselmann, D.: Video transformer network. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3163–3172 (2021)

    Google Scholar 

  19. Pang, G., Yan, C., Shen, C., Hengel, A.V.D., Bai, X.: Self-trained deep ordinal regression for end-to-end video anomaly detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 12173–12182 (2020)

    Google Scholar 

  20. Park, H., Noh, J., Ham, B.: Learning memory-guided normality for anomaly detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 14372–14381 (2020)

    Google Scholar 

  21. Radford, A., Narasimhan, K., Salimans, T., Sutskever, I., et al.: Improving language understanding by generative pre-training (2018)

    Google Scholar 

  22. Sharir, G., Noy, A., Zelnik-Manor, L.: An image is worth 16\(\times \)16 words, what is a video worth? arXiv preprint arXiv:2103.13915 (2021)

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

    Google Scholar 

  24. Tan, J., Tang, J., Wang, L., Wu, G.: Relaxed transformer decoders for direct action proposal generation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 13526–13535 (2021)

    Google Scholar 

  25. Tong, Z., Song, Y., Wang, J., Wang, L.: Videomae: masked autoencoders are data-efficient learners for self-supervised video pre-training. Adv. Neural. Inf. Process. Syst. 35, 10078–10093 (2022)

    Google Scholar 

  26. Touvron, H., Cord, M., Douze, M., Massa, F., Sablayrolles, A., Jégou, H.: Training data-efficient image transformers & distillation through attention. In: International Conference on Machine Learning, pp. 10347–10357. PMLR (2021)

    Google Scholar 

  27. Vaswani, A., et al.: Attention is all you need. Adv. Neural Inf. Process. Syst. 30 (2017)

    Google Scholar 

  28. Wang, X., et al.: OADTR: online action detection with transformers. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 7565–7575 (2021)

    Google Scholar 

  29. Xu, M., Gao, M., Chen, Y.T., Davis, L.S., Crandall, D.J.: Temporal recurrent networks for online action detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 5532–5541 (2019)

    Google Scholar 

  30. Xu, M., Xiong, Y., Chen, H., Li, X., Xia, W., Tu, Z., Soatto, S.: Long short-term transformer for online action detection. Adv. Neural. Inf. Process. Syst. 34, 1086–1099 (2021)

    Google Scholar 

  31. Yang, L., Han, J., Zhang, D.: Colar: effective and efficient online action detection by consulting exemplars. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3160–3169 (2022)

    Google Scholar 

  32. Yu, C., Ma, X., Ren, J., Zhao, H., Yi, S.: Spatio-temporal graph transformer networks for pedestrian trajectory prediction. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12357, pp. 507–523. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58610-2_30

    Chapter  Google Scholar 

  33. Zhao, Y., Krähenbühl, P.: Real-time online video detection with temporal smoothing transformers. In: Avidan, S., Brostow, G., Cisse, M., Farinella, G.M., Hassner, T. (eds.) European Conference on Computer Vision, pp. 485–502. Springer, Heidelberg (2022). https://doi.org/10.1007/978-3-031-19830-4_28

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yi Chen .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2024 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

Yuan, H., Zheng, Z., Gu, Y., Zhou, J., Chen, Y. (2024). ExpT: Online Action Detection via Exemplar-Enhanced Transformer for Secondary School Experimental Evaluation. In: Hong, W., Kanaparan, G. (eds) Computer Science and Education. Teaching and Curriculum. ICCSE 2023. Communications in Computer and Information Science, vol 2024. Springer, Singapore. https://doi.org/10.1007/978-981-97-0791-1_30

Download citation

  • DOI: https://doi.org/10.1007/978-981-97-0791-1_30

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-97-0790-4

  • Online ISBN: 978-981-97-0791-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics