MMAD: Multi-label Micro-Action Detection in Videos

Clicks: 31
ID: 282432
2024
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Abstract
Human body actions are an important form of non-verbal communication in social interactions. This paper specifically focuses on a subset of body actions known as micro-actions, which are subtle, low-intensity body movements with promising applications in human emotion analysis. In real-world scenarios, human micro-actions often temporally co-occur, with multiple micro-actions overlapping in time, such as concurrent head and hand movements. However, current research primarily focuses on recognizing individual micro-actions while overlooking their co-occurring nature. To address this gap, we propose a new task named Multi-label Micro-Action Detection (MMAD), which involves identifying all micro-actions in a given short video, determining their start and end times, and categorizing them. Accomplishing this requires a model capable of accurately capturing both long-term and short-term action relationships to detect multiple overlapping micro-actions. To facilitate the MMAD task, we introduce a new dataset named Multi-label Micro-Action-52 (MMA-52) and propose a baseline method equipped with a dual-path spatial-temporal adapter to address the challenges of subtle visual change in MMAD. We hope that MMA-52 can stimulate research on micro-action analysis in videos and prompt the development of spatio-temporal modeling in human-centric video understanding. The proposed MMA-52 dataset is available at: https://github.com/VUT-HFUT/Micro-Action.
Reference Key
wang2024mmad Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Kun Li; Pengyu Liu; Dan Guo; Fei Wang; Zhiliang Wu; Hehe Fan; Meng Wang
Journal arXiv
Year 2024
DOI
DOI not found
URL
http://arxiv.org/abs/2407.05311v2
Keywords
cs.cv

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