Efficient Video Action Detection with Token Dropout and Context Refinement

• URL: http://arxiv.org/abs/2304.08451v3
• Date: Mon, 28 Aug 2023 10:22:23 GMT
• Title: Efficient Video Action Detection with Token Dropout and Context Refinement
• Authors: Lei Chen, Zhan Tong, Yibing Song, Gangshan Wu, Limin Wang
• Abstract summary: We propose an end-to-end framework for efficient video action detection (ViTs) In a video clip, we maintain tokens from its perspective while preserving tokens relevant to actor motions from other frames. Second, we refine scene context by leveraging remaining tokens for better recognizing actor identities.
• Score: 67.10895416008911
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Streaming video clips with large-scale video tokens impede vision transformers (ViTs) for efficient recognition, especially in video action detection where sufficient spatiotemporal representations are required for precise actor identification. In this work, we propose an end-to-end framework for efficient video action detection (EVAD) based on vanilla ViTs. Our EVAD consists of two specialized designs for video action detection. First, we propose a spatiotemporal token dropout from a keyframe-centric perspective. In a video clip, we maintain all tokens from its keyframe, preserve tokens relevant to actor motions from other frames, and drop out the remaining tokens in this clip. Second, we refine scene context by leveraging remaining tokens for better recognizing actor identities. The region of interest (RoI) in our action detector is expanded into temporal domain. The captured spatiotemporal actor identity representations are refined via scene context in a decoder with the attention mechanism. These two designs make our EVAD efficient while maintaining accuracy, which is validated on three benchmark datasets (i.e., AVA, UCF101-24, JHMDB). Compared to the vanilla ViT backbone, our EVAD reduces the overall GFLOPs by 43% and improves real-time inference speed by 40% with no performance degradation. Moreover, even at similar computational costs, our EVAD can improve the performance by 1.1 mAP with higher resolution inputs. Code is available at https://github.com/MCG-NJU/EVAD.

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