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Guyue Hu, Bin He, Hanwang Zhang. Compositional Prompting Video-language Models to Understand Procedure in Instructional Videos. Machine Intelligence Research. https://doi.org/10.1007/s11633-022-1409-1
Citation: Guyue Hu, Bin He, Hanwang Zhang. Compositional Prompting Video-language Models to Understand Procedure in Instructional Videos. Machine Intelligence Research. https://doi.org/10.1007/s11633-022-1409-1
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Compositional Prompting Video-language Models to Understand Procedure in Instructional Videos

doi: 10.1007/s11633-022-1409-1
  • 1.

    Nanyang Technological University, Singapore 639798, Singapore

  • 2.

    The 15th Research Institute of China Electronics Technology Group Corporation, Beijing 100083, China

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  • Author Bio:

    Guyue Hu received the B. Eng. degree in automation from Hefei University of Technology, China in 2016, and the Ph. D. degree in pattern recognition and intelligent systems from National Laboratory of Pattern Recognition (NLPR), Institute of Automation, Chinese Academy of Sciences (CASIA), China in 2021. He was also a research fellow with School of Computing, National University of Singapore, Singapore from 2021 to 2022. He is currently a research fellow with School of Computer Science and Engineering, Nanyang Technological University, Singapore. He serves as a regular reviewer for a number of international journals and conferences, such as TPAMI, TMM, TCSVT, CVPR, ICCV, ECCV.His research interests include computer vision, pattern recognition, and computational neuroscience, especially in multi-modal learning, video understanding, and human activity analysis.E-mail: guyue.hu@ntu.edu.sg (Corresponding author)ORCID iD: 0000-0002-6198-8230

    Bin He received the B. Eng. degree in automation from Harbin University of Science and Technology, China in 2014, and the Ph. D. degree in mechanical and electronic engineering from Harbin University of Science and Technology, China in 2020. As a joint Ph. D. student, he finished the entire doctoral research at National Laboratory of Pattern Recognition (NLPR), Institute of Automation, Chinese Academy of Sciences (CASIA), China. He is currently an engineer at North China Computing Technology Institute (alias The 15th Research Institute of China Electronics Technology Group Corporation), China.His research interests include computer vision, pattern recognition, and intelligent decision-making, especially lie in military intelligence.E-mail: binhe.cas@foxmail.comORCID iD: 0000-0002-3845-7335

    Hanwang Zhang received the B. Eng. degree in computer science from Zhejiang University, China in 2009, and the Ph. D. degree in computer science from National University of Singapore, Singapore in 2014. He was a research scientist with Department of Computer Science, Columbia University, USA from 2017 to 2018, and a research fellow with National University of Singapore from 2014 to 2016. He is currently an assistant professor at School of Computer Science and Engineering, Nanyang Technological University, Singapore. He has authored more than 150 scientific papers in these areas in top journals and conferences, including TPAMI, TIP, ICLR, NeurIPS, CVPR, ICCV, ECCV, ACL, EMNLP, etc. He is the recipient of the Best Demo Runner-up Award in ACM MM 2012, the Best Student Paper Award in ACM MM 2013, the Best Paper Honorable Mention in ACM SIGIR 2016, and TOMM Best Paper Award 2018. He is also the Winner of the Best Ph. D. Thesis Award of School of Computing, National University of Singapore, Singapure in 2014.His research interests include computer vision and multimedia, especially focusing on the fusion of deep learning and reasoning for these fields.E-mail: hanwangzhang@ntu.edu.sg ORCID iD: 0000-0001-7374-8739

  • Received Date: 2022-06-30
  • Accepted Date: 2022-12-13
  • Publish Online: 2023-03-02
    Abstract
  • Instructional videos are very useful for completing complex daily tasks, which naturally contain abundant clip-narration pairs. Existing works for procedure understanding are keen on pretraining various video-language models with these pairs and then finetuning downstream classifiers and localizers in predetermined category space. These video-language models are proficient at representing short-term actions, basic objects, and their combinations, but they are still far from understanding long-term procedures. In addition, the predetermined procedure category faces the problem of combination disaster and is inherently inapt to unseen procedures. Therefore, we propose a novel compositional prompt learning (CPL) framework to understand long-term procedures by prompting short-term video-language models and reformulating several classical procedure understanding tasks into general video-text matching problems. Specifically, the proposed CPL consists of one visual prompt and three compositional textual prompts (including the action prompt, object prompt, and procedure prompt), which could compositionally distill knowledge from short-term video-language models to facilitate long-term procedure understanding. Besides, the task reformulation enables our CPL to perform well in all zero-shot, few-shot, and fully-supervised settings. Extensive experiments on two widely-used datasets for procedure understanding demonstrate the effectiveness of the proposed approach.

     

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