- Zongyang Ma13,14,15,
- Ziqi Zhang13,
- Yuxin Chen13,14,15,
- Zhongang Qi14,
- Chunfeng Yuan13,
- Bing Li13,
- Yingmin Luo14,
- Xu Li14,
- Xiaojuan Qi17,
- Ying Shan14 &
- …
- Weiming Hu13,15,16
Part of the book series:Lecture Notes in Computer Science ((LNCS,volume 15110))
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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.
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References
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)
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)
Bai, J., et al.: Lat: latent translation with cycle-consistency for video-text retrieval. arXiv preprintarXiv:2207.04858 (2022)
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)
Bertasius, G., Wang, H., Torresani, L.: Is space-time attention all you need for video understanding? In: ICML, vol. 2, p. 4 (2021)
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)
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
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)
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)
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
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)
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: pre-training of deep bidirectional transformers for language understanding. arXiv preprintarXiv:1810.04805 (2018)
Dosovitskiy, A., et al.: An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprintarXiv:2010.11929 (2020)
Freitag, M., Al-Onaizan, Y.: Beam search strategies for neural machine translation. arXiv preprintarXiv:1702.01806 (2017)
Fu, T.J., et al.: Violet: end-to-end video-language transformers with masked visual-token modeling. arXiv preprintarXiv:2111.12681 (2021)
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
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)
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
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)
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)
Holtzman, A., Buys, J., Du, L., Forbes, M., Choi, Y.: The curious case of neural text degeneration. arXiv preprintarXiv:1904.09751 (2019)
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)
Jozefowicz, R., Vinyals, O., Schuster, M., Shazeer, N., Wu, Y.: Exploring the limits of language modeling. arXiv preprintarXiv:1602.02410 (2016)
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)
Lei, J., Berg, T.L., Bansal, M.: Revealing single frame bias for video-and-language learning. arXiv preprintarXiv:2206.03428 (2022)
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)
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)
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)
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)
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)
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)
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)
Liu, Y., Albanie, S., Nagrani, A., Zisserman, A.: Use what you have: video retrieval using representations from collaborative experts. arXiv preprintarXiv:1907.13487 (2019)
Liu, Z., et al.: Video swin transformer. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3202–3211 (2022)
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)
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)
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)
Oord, A.V.D., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding. arXiv preprintarXiv:1807.03748 (2018)
Patrick, M., et al.: Support-set bottlenecks for video-text representation learning. arXiv preprintarXiv:2010.02824 (2020)
Radford, A., et al.: Learning transferable visual models from natural language supervision. In: International Conference on Machine Learning, pp. 8748–8763. PMLR (2021)
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)
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)
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)
Rouditchenko, A., et al.: Avlnet: learning audio-visual language representations from instructional videos. arXiv preprintarXiv:2006.09199 (2020)
Sanh, V., Debut, L., Chaumond, J., Wolf, T.: Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter. arXiv preprintarXiv:1910.01108 (2019)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
Xu, H., et al.: Videoclip: contrastive pre-training for zero-shot video-text understanding. arXiv preprintarXiv:2109.14084 (2021)
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)
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)
Xue, H., et al.: CLIP-ViP: adapting pre-trained image-text model to video-language representation alignment. arXiv preprintarXiv:2209.06430 (2022)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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.
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Authors and Affiliations
MAIS, Institute of Automation, Chinese Academy of Sciences, Beijing, China
Zongyang Ma, Ziqi Zhang, Yuxin Chen, Chunfeng Yuan, Bing Li & Weiming Hu
ARC Lab, Tencent PCG, Shenzhen, China
Zongyang Ma, Yuxin Chen, Zhongang Qi, Yingmin Luo, Xu Li & Ying Shan
School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
Zongyang Ma, Yuxin Chen & Weiming Hu
School of Information Science and Technology, ShanghaiTech University, Shanghai, China
Weiming Hu
The University of Hong Kong, Pokfulam, Hong Kong
Xiaojuan Qi
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University of Birmingham, Birmingham, UK
Aleš Leonardis
University of Trento, Trento, Italy
Elisa Ricci
Technical University of Darmstadt, Darmstadt, Germany
Stefan Roth
Princeton University, Princeton, NJ, USA
Olga Russakovsky
Czech Technical University in Prague, Prague, Czech Republic
Torsten Sattler
École des Ponts ParisTech, Marne-la-Vallée, France
Gül Varol
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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
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