Movatterモバイル変換


[0]ホーム

URL:


Skip to main content

Advertisement

Springer Nature Link
Log in

EA-VTR: Event-Aware Video-Text Retrieval

  • Conference paper
  • First Online:

Abstract

Understanding the content of events occurring in the video and their inherent temporal logic is crucial for video-text retrieval. However, web-crawled pre-training datasets often lack sufficient event information, and the widely adopted video-level cross-modal contrastive learning also struggles to capture detailed and complex video-text event alignment. To address these challenges, we make improvements from both data and model perspectives. In terms of pre-training data, we focus on supplementing the missing specific event content and event temporal transitions with the proposed event augmentation strategies. Based on the event-augmented data, we construct a novel Event-Aware Video-Text Retrieval model,i.e., EA-VTR, which achieves powerful video-text retrieval ability through superior video event awareness. EA-VTR can efficiently encode frame-level and video-level visual representations simultaneously, enabling detailed event content and complex event temporal cross-modal alignment, ultimately enhancing the comprehensive understanding of video events. Our method not only significantly outperforms existing approaches on multiple datasets for Text-to-Video Retrieval and Video Action Recognition tasks, but also demonstrates superior event content perceive ability on Multi-event Video-Text Retrieval and Video Moment Retrieval tasks, as well as outstanding event temporal logic understanding ability on Test of Time task.

Z. Ma, Z. Zhang and Y. Chen—Equal contribution.

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

Access this chapter

Subscribe and save

Springer+ Basic
¥17,985 /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
JPY 3498
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
JPY 17159
Price includes VAT (Japan)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
JPY 21449
Price includes VAT (Japan)
  • 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

Similar content being viewed by others

References

  1. Anne Hendricks, L., Wang, O., Shechtman, E., Sivic, J., Darrell, T., Russell, B.: Localizing moments in video with natural language. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 5803–5812 (2017)

    Google Scholar 

  2. Bagad, P., Tapaswi, M., Snoek, C.G.: Test of time: instilling video-language models with a sense of time. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2503–2516 (2023)

    Google Scholar 

  3. Bai, J., et al.: Lat: latent translation with cycle-consistency for video-text retrieval. arXiv preprintarXiv:2207.04858 (2022)

  4. Bain, M., Nagrani, A., Varol, G., Zisserman, A.: Frozen in time: a joint video and image encoder for end-to-end retrieval. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 1728–1738 (2021)

    Google Scholar 

  5. Bertasius, G., Wang, H., Torresani, L.: Is space-time attention all you need for video understanding? In: ICML, vol. 2, p. 4 (2021)

    Google Scholar 

  6. Caba Heilbron, F., Escorcia, V., Ghanem, B., Carlos Niebles, J.: Activitynet: a large-scale video benchmark for human activity understanding. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 961–970 (2015)

    Google Scholar 

  7. Cao, M., Yang, T., Weng, J., Zhang, C., Wang, J., Zou, Y.: LocVTP: video-text pre-training for temporal localization. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, pp. 38–56. Springer, Cham (2022).https://doi.org/10.1007/978-3-031-19809-0_3

    Chapter  Google Scholar 

  8. Chen, D., Dolan, W.B.: Collecting highly parallel data for paraphrase evaluation. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 190–200 (2011)

    Google Scholar 

  9. Chen, Y., et al.: Vilem: visual-language error modeling for image-text retrieval. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 11018–11027 (2023)

    Google Scholar 

  10. Ego4D Consortium: Egocentric live 4D perception (Ego4D) database: a large-scale first-person video database, supporting research in multi-modal machine perception for daily life activity

    Google Scholar 

  11. Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255. IEEE (2009)

    Google Scholar 

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

  13. Dosovitskiy, A., et al.: An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprintarXiv:2010.11929 (2020)

  14. Freitag, M., Al-Onaizan, Y.: Beam search strategies for neural machine translation. arXiv preprintarXiv:1702.01806 (2017)

  15. Fu, T.J., et al.: Violet: end-to-end video-language transformers with masked visual-token modeling. arXiv preprintarXiv:2111.12681 (2021)

  16. Gabeur, V., Sun, C., Alahari, K., Schmid, C.: Multi-modal transformer for video retrieval. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12349, pp. 214–229. Springer, Cham (2020).https://doi.org/10.1007/978-3-030-58548-8_13

    Chapter  Google Scholar 

  17. Ge, Y., et al.: Bridging video-text retrieval with multiple choice questions. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 16167–16176 (2022)

    Google Scholar 

  18. Ge, Y., et al.: MILES: visual BERT pre-training with injected language semantics for video-text retrieval. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, vol. 13695, pp. 691–708. Springer, Cham (2022).https://doi.org/10.1007/978-3-031-19833-5_40

    Chapter  Google Scholar 

  19. Ging, S., Zolfaghari, M., Pirsiavash, H., Brox, T.: Coot: cooperative hierarchical transformer for video-text representation learning. Adv. Neural. Inf. Process. Syst.33, 22605–22618 (2020)

    Google Scholar 

  20. Grauman, K., et al.: Ego4d: around the world in 3,000 hours of egocentric video. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 18995–19012 (2022)

    Google Scholar 

  21. Holtzman, A., Buys, J., Du, L., Forbes, M., Choi, Y.: The curious case of neural text degeneration. arXiv preprintarXiv:1904.09751 (2019)

  22. Huang, J., Li, Y., Feng, J., Wu, X., Sun, X., Ji, R.: Clover: towards a unified video-language alignment and fusion model. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 14856–14866 (2023)

    Google Scholar 

  23. Jozefowicz, R., Vinyals, O., Schuster, M., Shazeer, N., Wu, Y.: Exploring the limits of language modeling. arXiv preprintarXiv:1602.02410 (2016)

  24. Kuehne, H., Jhuang, H., Garrote, E., Poggio, T., Serre, T.: HMDB: a large video database for human motion recognition. In: 2011 International Conference on Computer Vision, pp. 2556–2563. IEEE (2011)

    Google Scholar 

  25. Lei, J., Berg, T.L., Bansal, M.: Revealing single frame bias for video-and-language learning. arXiv preprintarXiv:2206.03428 (2022)

  26. 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 

  27. Li, D., Li, J., Li, H., Niebles, J.C., Hoi, S.C.: Align and prompt: video-and-language pre-training with entity prompts. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4953–4963 (2022)

    Google Scholar 

  28. Li, J., Li, D., Savarese, S., Hoi, S.: Blip-2: bootstrapping language-image pre-training with frozen image encoders and large language models. arXiv preprintarXiv:2301.12597 (2023)

  29. Li, J., Li, D., Xiong, C., Hoi, S.: Blip: bootstrapping language-image pre-training for unified vision-language understanding and generation. In: International Conference on Machine Learning, pp. 12888–12900. PMLR (2022)

    Google Scholar 

  30. Li, L., Chen, Y.C., Cheng, Y., Gan, Z., Yu, L., Liu, J.: Hero: hierarchical encoder for video+ language omni-representation pre-training. arXiv preprintarXiv:2005.00200 (2020)

  31. Li, L., et al.: Lavender: unifying video-language understanding as masked language modeling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 23119–23129 (2023)

    Google Scholar 

  32. Li, Y., Min, K., Tripathi, S., Vasconcelos, N.: Svitt: temporal learning of sparse video-text transformers. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 18919–18929 (2023)

    Google Scholar 

  33. Liu, Y., Albanie, S., Nagrani, A., Zisserman, A.: Use what you have: video retrieval using representations from collaborative experts. arXiv preprintarXiv:1907.13487 (2019)

  34. Liu, Z., et al.: Video swin transformer. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3202–3211 (2022)

    Google Scholar 

  35. Lu, H., Fei, N., Huo, Y., Gao, Y., Lu, Z., Wen, J.R.: Cots: collaborative two-stream vision-language pre-training model for cross-modal retrieval. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15692–15701 (2022)

    Google Scholar 

  36. 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 

  37. Miech, A., Zhukov, D., Alayrac, J.B., Tapaswi, M., Laptev, I., Sivic, J.: Howto100m: learning a text-video embedding by watching hundred million narrated video clips. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 2630–2640 (2019)

    Google Scholar 

  38. Oord, A.V.D., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding. arXiv preprintarXiv:1807.03748 (2018)

  39. Patrick, M., et al.: Support-set bottlenecks for video-text representation learning. arXiv preprintarXiv:2010.02824 (2020)

  40. 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 

  41. Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. OpenAI Blog1(8), 9 (2019)

    Google Scholar 

  42. Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, vol. 28 (2015)

    Google Scholar 

  43. Rohrbach, A., Rohrbach, M., Tandon, N., Schiele, B.: A dataset for movie description. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3202–3212 (2015)

    Google Scholar 

  44. Rouditchenko, A., et al.: Avlnet: learning audio-visual language representations from instructional videos. arXiv preprintarXiv:2006.09199 (2020)

  45. Sanh, V., Debut, L., Chaumond, J., Wolf, T.: Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter. arXiv preprintarXiv:1910.01108 (2019)

  46. Shao, D., Xiong, Y., Zhao, Y., Huang, Q., Qiao, Y., Lin, D.: Find and focus: retrieve and localize video events with natural language queries. In: Proceedings of the European Conference on Computer Vision (ECCV) (2018)

    Google Scholar 

  47. Sharma, P., Ding, N., Goodman, S., Soricut, R.: Conceptual captions: a cleaned, hypernymed, image alt-text dataset for automatic image captioning. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 2556–2565 (2018)

    Google Scholar 

  48. Shi, Y., et al.: Learning semantics-grounded vocabulary representation for video-text retrieval. In: Proceedings of the 31st ACM International Conference on Multimedia, pp. 4460–4470 (2023)

    Google Scholar 

  49. Soomro, K., Zamir, A.R., Shah, M.: UCF101: a dataset of 101 human actions classes from videos in the wild. arXiv preprintarXiv:1212.0402 (2012)

  50. Tewel, Y., Shalev, Y., Schwartz, I., Wolf, L.: Zerocap: zero-shot image-to-text generation for visual-semantic arithmetic. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 17918–17928 (2022)

    Google Scholar 

  51. Wang, J., et al.: Object-aware video-language pre-training for retrieval. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3313–3322 (2022)

    Google Scholar 

  52. Wang, J., et al.: All in one: exploring unified video-language pre-training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6598–6608 (2023)

    Google Scholar 

  53. Wang, P., et al.: OFA: unifying architectures, tasks, and modalities through a simple sequence-to-sequence learning framework. In: International Conference on Machine Learning, pp. 23318–23340. PMLR (2022)

    Google Scholar 

  54. Wu, W., Luo, H., Fang, B., Wang, J., Ouyang, W.: Cap4video: what can auxiliary captions do for text-video retrieval? In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10704–10713 (2023)

    Google Scholar 

  55. Wu, X., Gao, C., Lin, Z., Wang, Z., Han, J., Hu, S.: Rap: redundancy-aware video-language pre-training for text-video retrieval. arXiv preprintarXiv:2210.06881 (2022)

  56. Xu, H., et al.: Videoclip: contrastive pre-training for zero-shot video-text understanding. arXiv preprintarXiv:2109.14084 (2021)

  57. Xu, J., Mei, T., Yao, T., Rui, Y.: MSR-VTT: a large video description dataset for bridging video and language. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5288–5296 (2016)

    Google Scholar 

  58. Xu, M., et al.: Boundary-sensitive pre-training for temporal localization in videos. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 7220–7230 (2021)

    Google Scholar 

  59. Xue, H., et al.: CLIP-ViP: adapting pre-trained image-text model to video-language representation alignment. arXiv preprintarXiv:2209.06430 (2022)

  60. Yan, R., et al.: Video-text pre-training with learned regions for retrieval. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, pp. 3100–3108 (2023)

    Google Scholar 

  61. Yang, J., Bisk, Y., Gao, J.: Taco: token-aware cascade contrastive learning for video-text alignment. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 11562–11572 (2021)

    Google Scholar 

  62. Yang, X., et al.: Learning trajectory-word alignments for video-language tasks. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp. 2504–2514 (2023)

    Google Scholar 

  63. Ye, Q., et al.: Hitea: hierarchical temporal-aware video-language pre-training. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 15405–15416 (2023)

    Google Scholar 

  64. Zhang, G., Ren, J., Gu, J., Tresp, V.: Multi-event video-text retrieval. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 22113–22123 (2023)

    Google Scholar 

  65. Zhang, H., Liu, D., Lv, Z., Su, B., Tao, D.: Exploring temporal concurrency for video-language representation learning. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp. 15568–15578 (2023)

    Google Scholar 

  66. Zhao, Y., Misra, I., Krähenbühl, P., Girdhar, R.: Learning video representations from large language models. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6586–6597 (2023)

    Google Scholar 

  67. Zhu, D., Chen, J., Shen, X., Li, X., Elhoseiny, M.: Minigpt-4: enhancing vision-language understanding with advanced large language models. arXiv preprintarXiv:2304.10592 (2023)

  68. Zhu, L., Yang, Y.: Actbert: learning global-local video-text representations. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8746–8755 (2020)

    Google Scholar 

Download references

Acknowledgments

This work is supported by the Key Research and Development Program of Xinjiang Urumqi Autonomous Region under Grant No. 2023B01005, the Natural Science Foundation of China (Grants 62302501, 62036011, 62122086, 62192782, 61721004, U2033210 and 62372451), Beijing Natural Science Foundation (JQ21017, JQ24022, L243015), CCF-Tencent Rhino-Bird Open Research Fund.

Author information

Authors and Affiliations

  1. MAIS, Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Zongyang Ma, Ziqi Zhang, Yuxin Chen, Chunfeng Yuan, Bing Li & Weiming Hu

  2. ARC Lab, Tencent PCG, Shenzhen, China

    Zongyang Ma, Yuxin Chen, Zhongang Qi, Yingmin Luo, Xu Li & Ying Shan

  3. School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China

    Zongyang Ma, Yuxin Chen & Weiming Hu

  4. School of Information Science and Technology, ShanghaiTech University, Shanghai, China

    Weiming Hu

  5. The University of Hong Kong, Pokfulam, Hong Kong

    Xiaojuan Qi

Authors
  1. Zongyang Ma

    You can also search for this author inPubMed Google Scholar

  2. Ziqi Zhang

    You can also search for this author inPubMed Google Scholar

  3. Yuxin Chen

    You can also search for this author inPubMed Google Scholar

  4. Zhongang Qi

    You can also search for this author inPubMed Google Scholar

  5. Chunfeng Yuan

    You can also search for this author inPubMed Google Scholar

  6. Bing Li

    You can also search for this author inPubMed Google Scholar

  7. Yingmin Luo

    You can also search for this author inPubMed Google Scholar

  8. Xu Li

    You can also search for this author inPubMed Google Scholar

  9. Xiaojuan Qi

    You can also search for this author inPubMed Google Scholar

  10. Ying Shan

    You can also search for this author inPubMed Google Scholar

  11. Weiming Hu

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence toZiqi Zhang.

Editor information

Editors and Affiliations

  1. University of Birmingham, Birmingham, UK

    Aleš Leonardis

  2. University of Trento, Trento, Italy

    Elisa Ricci

  3. Technical University of Darmstadt, Darmstadt, Germany

    Stefan Roth

  4. Princeton University, Princeton, NJ, USA

    Olga Russakovsky

  5. Czech Technical University in Prague, Prague, Czech Republic

    Torsten Sattler

  6. École des Ponts ParisTech, Marne-la-Vallée, France

    Gül Varol

1Electronic supplementary material

Below is the link to the electronic supplementary material.

Rights and permissions

Copyright information

© 2025 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

Ma, Z.et al. (2025). EA-VTR: Event-Aware Video-Text Retrieval. In: Leonardis, A., Ricci, E., Roth, S., Russakovsky, O., Sattler, T., Varol, G. (eds) Computer Vision – ECCV 2024. ECCV 2024. Lecture Notes in Computer Science, vol 15110. Springer, Cham. https://doi.org/10.1007/978-3-031-72943-0_5

Download citation

Publish with us

Access this chapter

Subscribe and save

Springer+ Basic
¥17,985 /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
JPY 3498
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
JPY 17159
Price includes VAT (Japan)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
JPY 21449
Price includes VAT (Japan)
  • 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


[8]ページ先頭

©2009-2025 Movatter.jp