Seeing in Flowing: Adapting CLIP for Action Recognition with Motion Prompts Learning

• URL: http://arxiv.org/abs/2308.04828v1
• Date: Wed, 9 Aug 2023 09:33:45 GMT
• Title: Seeing in Flowing: Adapting CLIP for Action Recognition with Motion Prompts Learning
• Authors: Qiang Wang, Junlong Du, Ke Yan, Shouhong Ding
• Abstract summary: Contrastive Language-Image Pre-training (CLIP) has recently shown remarkable generalization on "zero-shot" training. We explore the adaptation of CLIP to achieve a more efficient and generalized action recognition method. Our method outperforms most existing state-of-the-art methods by a significant margin on "few-shot" and "zero-shot" training.
• Score: 14.292812802621707
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The Contrastive Language-Image Pre-training (CLIP) has recently shown remarkable generalization on "zero-shot" training and has applied to many downstream tasks. We explore the adaptation of CLIP to achieve a more efficient and generalized action recognition method. We propose that the key lies in explicitly modeling the motion cues flowing in video frames. To that end, we design a two-stream motion modeling block to capture motion and spatial information at the same time. And then, the obtained motion cues are utilized to drive a dynamic prompts learner to generate motion-aware prompts, which contain much semantic information concerning human actions. In addition, we propose a multimodal communication block to achieve a collaborative learning and further improve the performance. We conduct extensive experiments on HMDB-51, UCF-101, and Kinetics-400 datasets. Our method outperforms most existing state-of-the-art methods by a significant margin on "few-shot" and "zero-shot" training. We also achieve competitive performance on "closed-set" training with extremely few trainable parameters and additional computational costs.

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