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MMAD: Multi-label Micro-Action Detection in Videos by Li, Kun; Liu, Pengyu; Guo, Dan; Wang, Fei; Wu, Zhiliang; Fan, Hehe; Wang, Meng is a scholarly article available to read on EtoBox.

What is MMAD: Multi-label Micro-Action Detection in Videos about?

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.

Author

Li, Kun; Liu, Pengyu; Guo, Dan; Wang, Fei; Wu, Zhiliang; Fan, Hehe; Wang, Meng

Published

2024

Language

EN