Knowledge Prompting for Few-shot Action Recognition

• URL: http://arxiv.org/abs/2211.12030v1
• Date: Tue, 22 Nov 2022 06:05:17 GMT
• Title: Knowledge Prompting for Few-shot Action Recognition
• Authors: Yuheng Shi, Xinxiao Wu, Hanxi Lin
• Abstract summary: We propose a simple yet effective method, called knowledge prompting, to prompt a powerful vision-language model for few-shot classification. We first collect large-scale language descriptions of actions, defined as text proposals, to build an action knowledge base. We feed these text proposals into the pre-trained vision-language model along with video frames to generate matching scores of the proposals to each frame. Extensive experiments on six benchmark datasets demonstrate that our method generally achieves the state-of-the-art performance while reducing the training overhead to 0.001 of existing methods.
• Score: 20.973999078271483
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Few-shot action recognition in videos is challenging for its lack of supervision and difficulty in generalizing to unseen actions. To address this task, we propose a simple yet effective method, called knowledge prompting, which leverages commonsense knowledge of actions from external resources to prompt a powerful pre-trained vision-language model for few-shot classification. We first collect large-scale language descriptions of actions, defined as text proposals, to build an action knowledge base. The collection of text proposals is done by filling in handcraft sentence templates with external action-related corpus or by extracting action-related phrases from captions of Web instruction videos.Then we feed these text proposals into the pre-trained vision-language model along with video frames to generate matching scores of the proposals to each frame, and the scores can be treated as action semantics with strong generalization. Finally, we design a lightweight temporal modeling network to capture the temporal evolution of action semantics for classification.Extensive experiments on six benchmark datasets demonstrate that our method generally achieves the state-of-the-art performance while reducing the training overhead to 0.001 of existing methods.

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