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PromptLearner-CLIP: Contrastive Multi-Modal Action Representation Learning with Context Optimization

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Computer Vision – ACCV 2022 (ACCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13844))

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Abstract

An action contains rich multi-modal information, and current methods generally map the action class to a digital number as supervised information to train models. However, numerical labels cannot describe the semantic content contained in the action. This paper proposes PromptLearner-CLIP for action recognition, where the text pathway uses PromptLearner to automatically learn the text content of prompt as the input and calculates the semantic features of actions, and the vision pathway takes video data as the input to learn the visual features of actions. To strengthen the interaction between features of different modalities, this paper proposes a multi-modal information interaction module that utilizes Graph Neural Network(GNN) to process both the semantic features of text content and the visual features of a video. In addition, the single-modal video classification problem is transformed into a multi-modal video-text matching problem. Multi-modal contrastive learning is used to disclose the feature distance of the same but different modalities samples. The experimental results showed that PromptLearner-CLIP could utilize the textual semantic information to significantly improve the performance of various single-modal backbone networks on action recognition and achieved top-tier results on Kinetics400, UCF101, and HMDB51 datasets. Code is available at https://github.com/ZhenxingZheng/PromptLearner.

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Acknowledgements

This work was supported in part by the National Key Research and Development Program of China under Grant 2021YFE0110500 and in part by the National Natural Science Foundation of China under Grant 62006015 and Grant 62072028.

Author information

Authors and Affiliations

  1. School of Communications and Information Engineering, Xi’an University of Posts and Telecommunications, Xi’an, 710121, China

    Zhenxing Zheng

  2. Institute of Information Science, Beijing Jiaotong University, Beijing, 100044, China

    Zhenxing Zheng, Gaoyun An, Shan Cao, Zhaoqilin Yang & Qiuqi Ruan

  3. Beijing Key Laboratory of Advanced Information Science and Network Technology, Beijing, 100044, China

    Zhenxing Zheng, Gaoyun An, Shan Cao, Zhaoqilin Yang & Qiuqi Ruan

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  1. Zhenxing Zheng
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  2. Gaoyun An
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  3. Shan Cao
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  4. Zhaoqilin Yang
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  5. Qiuqi Ruan
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Corresponding author

Correspondence to Gaoyun An .

Editor information

Editors and Affiliations

  1. University of Wollongong, Wollongong, NSW, Australia

    Lei Wang

  2. University of Bonn, Bonn, Germany

    Juergen Gall

  3. University of Adelaide, Adelaide, SA, Australia

    Tat-Jun Chin

  4. National Institute of Informatics, Tokyo, Japan

    Imari Sato

  5. Johns Hopkins University, Baltimore, MD, USA

    Rama Chellappa

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Zheng, Z., An, G., Cao, S., Yang, Z., Ruan, Q. (2023). PromptLearner-CLIP: Contrastive Multi-Modal Action Representation Learning with Context Optimization. In: Wang, L., Gall, J., Chin, TJ., Sato, I., Chellappa, R. (eds) Computer Vision – ACCV 2022. ACCV 2022. Lecture Notes in Computer Science, vol 13844. Springer, Cham. https://doi.org/10.1007/978-3-031-26316-3_33

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  • DOI: https://doi.org/10.1007/978-3-031-26316-3_33

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