Skip to main content
SpringerLink
Log in

Zero-Shot Temporal Action Detection via Vision-Language Prompting

  • Conference paper
  • First Online:
Computer Vision – ECCV 2022 (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13663))

Included in the following conference series:

Abstract

Existing temporal action detection (TAD) methods rely on large training data including segment-level annotations, limited to recognizing previously seen classes alone during inference. Collecting and annotating a large training set for each class of interest is costly and hence unscalable. Zero-shot TAD (ZS-TAD) resolves this obstacle by enabling a pre-trained model to recognize any unseen action classes. Meanwhile, ZS-TAD is also much more challenging with significantly less investigation. Inspired by the success of zero-shot image classification aided by vision-language (ViL) models such as CLIP, we aim to tackle the more complex TAD task. An intuitive method is to integrate an off-the-shelf proposal detector with CLIP style classification. However, due to the sequential localization (e.g., proposal generation) and classification design, it is prone to localization error propagation. To overcome this problem, in this paper we propose a novel zero-\(\underline{S}\)hot \(\underline{T}\)emporal \(\underline{A}\)ction detection model via Vision-\(\underline{L}\)anguag\(\underline{E}\) prompting (STALE). Such a novel design effectively eliminates the dependence between localization and classification by breaking the route for error propagation in-between. We further introduce an interaction mechanism between classification and localization for improved optimization. Extensive experiments on standard ZS-TAD video benchmarks show that our STALE significantly outperforms state-of-the-art alternatives. Besides, our model also yields superior results on supervised TAD over recent strong competitors. The PyTorch implementation of STALE is available on https://github.com/sauradip/STALE.

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 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
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. Alwassel, H., Caba Heilbron, F., Escorcia, V., Ghanem, B.: Diagnosing error in temporal action detectors. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11207, pp. 264–280. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01219-9_16

    Chapter  Google Scholar 

  2. Antol, S., et al.: Vqa: visual question answering. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2425–2433 (2015)

    Google Scholar 

  3. Bodla, N., Singh, B., Chellappa, R., Davis, L.S.: Soft-nms-improving object detection with one line of code. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 5561–5569 (2017)

    Google Scholar 

  4. Caba Heilbron, F., Escorcia, V., Ghanem, B., Carlos Niebles, J.: Activitynet: a large-scale video benchmark for human activity understanding. In: CVPR, pp. 961–970 (2015)

    Google Scholar 

  5. 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 

  6. Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017)

    Google Scholar 

  7. Chen, Y., Dai, X., Liu, M., Chen, D., Yuan, L., Liu, Z.: Dynamic convolution: attention over convolution kernels. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11030–11039 (2020)

    Google Scholar 

  8. Cheng, B., Schwing, A., Kirillov, A.: Per-pixel classification is not all you need for semantic segmentation. In: Advances in Neural Information Processing Systems, vol. 34 (2021)

    Google Scholar 

  9. Dosovitskiy, A., et al.: An image is worth 16x16 words: transformers for image recognition at scale. In: ICLR (2020)

    Google Scholar 

  10. Farha, Y.A., Gall, J.: Ms-tcn: multi-stage temporal convolutional network for action segmentation. In: CVPR, pp. 3575–3584 (2019)

    Google Scholar 

  11. Gao, J., Yang, Z., Chen, K., Sun, C., Nevatia, R.: Turn tap: temporal unit regression network for temporal action proposals. In: ICCV (2017)

    Google Scholar 

  12. Gao, P., et al.: Clip-adapter: better vision-language models with feature adapters. arXiv preprint. arXiv:2110.04544 (2021)

  13. Goldberg, Y., Levy, O.: word2vec explained: deriving mikolov et al’.s negative-sampling word-embedding method. arXiv preprint. arXiv:1402.3722 (2014)

  14. 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 

  15. Idrees, H.: The thumos challenge on action recognition for videos “in the wild’’. Comput. Vis. Image Underst. 155, 1–23 (2017)

    Article  Google Scholar 

  16. Jia, C., et al.: Scaling up visual and vision-language representation learning with noisy text supervision. In: International Conference on Machine Learning, pp. 4904–4916. PMLR (2021)

    Google Scholar 

  17. Ju, C., Han, T., Zheng, K., Zhang, Y., Xie, W.: A simple baseline on prompt learning for efficient video understanding (2022)

    Google Scholar 

  18. Lampert, C.H., Nickisch, H., Harmeling, S.: Attribute-based classification for zero-shot visual object categorization. IEEE Trans. Pattern Anal. Mach. Intell. 36(3), 453–465 (2013)

    Article  Google Scholar 

  19. Lei, J., et al.: Less is more: clipbert for video-and-language learning via sparse sampling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7331–7341 (2021)

    Google Scholar 

  20. Lin, T., Liu, X., Li, X., Ding, E., Wen, S.: Bmn: boundary-matching network for temporal action proposal generation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3889–3898 (2019)

    Google Scholar 

  21. Lin, T., Zhao, X., Su, H., Wang, C., Yang, M.: BSN: boundary sensitive network for temporal action proposal generation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11208, pp. 3–21. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01225-0_1

    Chapter  Google Scholar 

  22. Long, F., Yao, T., Qiu, Z., Tian, X., Luo, J., Mei, T.: Gaussian temporal awareness networks for action localization. In: CVPR (2019)

    Google Scholar 

  23. Lu, J., Batra, D., Parikh, D., Lee, S.: Vilbert: pretraining task-agnostic visiolinguistic representations for vision-and-language tasks. In: Advances in Neural Information Processing Systems, vol. 32 (2019)

    Google Scholar 

  24. Miech, A., Alayrac, J.B., Smaira, L., Laptev, I., Sivic, J., Zisserman, A.: End-to-end learning of visual representations from uncurated instructional videos. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9879–9889 (2020)

    Google Scholar 

  25. Milletari, F., Navab, N., Ahmadi, S.A.: V-net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 4th International Conference on 3D Vision (3DV), pp. 565–571. IEEE (2016)

    Google Scholar 

  26. Nag, S., Zhu, X., Song, Y.Z., Xiang, T.: Temporal action localization with global segmentation mask transformers (2021)

    Google Scholar 

  27. Nag, S., Zhu, X., Song, Y.z., Xiang, T.: Proposal-free temporal action detection via global segmentation mask learning. In: ECCV (2022)

    Google Scholar 

  28. Nag, S., Zhu, X., Song, Y.z., Xiang, T.: Semi-supervised temporal action detection with proposal-free masking. In: ECCV (2022)

    Google Scholar 

  29. Nag, S., Zhu, X., Xiang, T.: Few-shot temporal action localization with query adaptive transformer. arXiv preprint. arXiv:2110.10552 (2021)

  30. Niu, L., Cai, J., Veeraraghavan, A., Zhang, L.: Zero-shot learning via category-specific visual-semantic mapping and label refinement. IEEE Trans. Image Process. 28(2), 965–979 (2018)

    Article  MathSciNet  Google Scholar 

  31. Pan, J., Lin, Z., Zhu, X., Shao, J., Li, H.: Parameter-efficient image-to-video transfer learning. arXiv preprint. arXiv:2206.13559 (2022)

  32. Parikh, D., Grauman, K.: Relative attributes. In: 2011 International Conference on Computer Vision, pp. 503–510. IEEE (2011)

    Google Scholar 

  33. Paul, S., Mithun, N.C., Roy-Chowdhury, A.K.: Text-based localization of moments in a video corpus. IEEE Trans. Image Process. 30, 8886–8899 (2021)

    Article  Google Scholar 

  34. Pennington, J., Socher, R., Manning, C.D.: Glove: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014)

    Google Scholar 

  35. Qin, J., et al.: Zero-shot action recognition with error-correcting output codes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2833–2842 (2017)

    Google Scholar 

  36. Radford, A., et al.: Learning transferable visual models from natural language supervision. In: International Conference on Machine Learning, pp. 8748–8763. PMLR (2021)

    Google Scholar 

  37. Rao, Y., et al.: Denseclip: language-guided dense prediction with context-aware prompting. arXiv preprint. arXiv:2112.01518 (2021)

  38. Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: towards real-time object detection with region proposal networks. In: TPAMI, vol. 39, no. 6, pp. 1137–1149 (2016)

    Google Scholar 

  39. Sridhar, D., Quader, N., Muralidharan, S., Li, Y., Dai, P., Lu, J.: Class semantics-based attention for action detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 13739–13748 (2021)

    Google Scholar 

  40. Su, H., Gan, W., Wu, W., Qiao, Y., Yan, J.: Bsn++: complementary boundary regressor with scale-balanced relation modeling for temporal action proposal generation. arXiv preprint. arXiv:2009.07641 (2020)

  41. Vaswani, A., et al.: Attention is all you need. arXiv preprint. arXiv:1706.03762 (2017)

  42. Wang, M., Xing, J., Liu, Y.: Actionclip: a new paradigm for video action recognition. arXiv preprint. arXiv:2109.08472 (2021)

  43. Wang, Z., et al.: Camp: cross-modal adaptive message passing for text-image retrieval. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 5764–5773 (2019)

    Google Scholar 

  44. Xian, Y., Lampert, C.H., Schiele, B., Akata, Z.: Zero-shot learning-a comprehensive evaluation of the good, the bad and the ugly. IEEE Trans. Pattern Anal. Mach. Intell. 41(9), 2251–2265 (2018)

    Article  Google Scholar 

  45. Xu, H., Das, A., Saenko, K.: R-c3d: region convolutional 3d network for temporal activity detection. In: ICCV (2017)

    Google Scholar 

  46. Xu, K., et al.: Show, attend and tell: neural image caption generation with visual attention. In: International Conference on Machine Learning, pp. 2048–2057. PMLR (2015)

    Google Scholar 

  47. Xu, M., Zhao, C., Rojas, D.S., Thabet, A., Ghanem, B.: G-tad: sub-graph localization for temporal action detection. In: CVPR (2020)

    Google Scholar 

  48. Xu, X., Hospedales, T., Gong, S.: Semantic embedding space for zero-shot action recognition. In: 2015 IEEE International Conference on Image Processing (ICIP), pp. 63–67. IEEE (2015)

    Google Scholar 

  49. Zhang, L., et al.: Zstad: zero-shot temporal activity detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 879–888 (2020)

    Google Scholar 

  50. Zhang, R., et al.: Tip-adapter: training-free clip-adapter for better vision-language modeling. arXiv preprint. arXiv:2111.03930 (2021)

  51. Zhao, C., Thabet, A.K., Ghanem, B.: Video self-stitching graph network for temporal action localization. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 13658–13667 (2021)

    Google Scholar 

  52. Zhao, Y., Xiong, Y., Wang, L., Wu, Z., Tang, X., Lin, D.: Temporal action detection with structured segment networks. In: ICCV (2017)

    Google Scholar 

  53. Zhong, Y., et al.: Regionclip: Region-based language-image pretraining. arXiv preprint. arXiv:2112.09106 (2021)

  54. Zhou, K., Yang, J., Loy, C.C., Liu, Z.: Learning to prompt for vision-language models. arXiv preprint. arXiv:2109.01134 (2021)

Download references

Author information

Authors and Affiliations

  1. CVSSP, University of Surrey, Guildford, UK

    Sauradip Nag, Xiatian Zhu, Yi-Zhe Song & Tao Xiang

  2. iFlyTek-Surrey Joint Research Centre on Artificial Intelligence, London, UK

    Sauradip Nag, Yi-Zhe Song & Tao Xiang

  3. Surrey Institute for People-Centred Artificial Intelligence, University of Surrey, Guildford, UK

    Xiatian Zhu

Authors
  1. Sauradip Nag
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiatian Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yi-Zhe Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Tao Xiang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sauradip Nag .

Editor information

Editors and Affiliations

  1. Tel Aviv University, Tel Aviv, Israel

    Shai Avidan

  2. University College London, London, UK

    Gabriel Brostow

  3. Google AI, Accra, Ghana

    Moustapha Cissé

  4. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Facebook (United States), Menlo Park, CA, USA

    Tal Hassner

1 Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (pdf 1441 KB)

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Nag, S., Zhu, X., Song, YZ., Xiang, T. (2022). Zero-Shot Temporal Action Detection via Vision-Language Prompting. 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 13663. Springer, Cham. https://doi.org/10.1007/978-3-031-20062-5_39

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-20062-5_39

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-20061-8

  • Online ISBN: 978-3-031-20062-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation