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Prompting Visual-Language Models for Efficient Video Understanding

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Computer Vision – ECCV 2022 (ECCV 2022)

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Abstract

Image-based visual-language (I-VL) pre-training has shown great success for learning joint visual-textual representations from large-scale web data, revealing remarkable ability for “zero-shot” generalisation. This paper presents a simple but strong baseline to efficiently adapt the pre-trained I-VL model for video understanding tasks, with minimal training. Specifically, we propose to optimise a few random vectors, termed as “continuous prompt vectors”, that convert video-related tasks into the same format as the pre-training objectives. In addition, to bridge the gap between static images and videos, temporal information is encoded with lightweight Transformers stacking on top of frame-wise visual features. Experimentally, we conduct extensive ablation studies to analyse the critical components. On ten public benchmarks of action recognition, action localisation, and text-video retrieval, across closed-set, few-shot, and zero-shot scenarios, we achieve competitive or state-of-the-art performance to existing methods, despite optimising significantly fewer parameters. Due to space limitation, we refer the readers to the arXiv version at https://arxiv.org/abs/2112.04478.

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Acknowledgement

This work is supported by the National Key Research and Development Program of China (No. 2020YFB1406801), 111 plan (No. BP0719010), STCSM (No. 18DZ2270700), State Key Laboratory of UHD Video and Audio Production and Presentation, the UK EPSRC Programme Grant Visual AI (EP/T028572/1), and a Google-DeepMind Scholarship.

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Authors and Affiliations

  1. Cooperative Medianet Innovation Center, Shanghai Jiao Tong University, Shanghai, China

    Chen Ju, Kunhao Zheng, Ya Zhang & Weidi Xie

  2. Visual Geometry Group, University of Oxford, Oxford, England

    Tengda Han & Weidi Xie

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  1. Chen Ju
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  2. Tengda Han
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  3. Kunhao Zheng
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  4. Ya Zhang
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  5. Weidi Xie
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Correspondence to Ya Zhang or Weidi Xie .

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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

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Ju, C., Han, T., Zheng, K., Zhang, Y., Xie, W. (2022). Prompting Visual-Language Models for Efficient Video Understanding. 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 13695. Springer, Cham. https://doi.org/10.1007/978-3-031-19833-5_7

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