MMAD: Multi-label Micro-Action Detection in Videos
<|reference_start|>MMAD: Multi-label Micro-Action Detection in Videos: Human body actions are an important form of non-verbal communication in social interactions. This paper focuses on a specific subset of body actions known as micro-actions, which are subtle, low-intensity body movements that provide a deeper understanding of inner human feelings. In real-world scenarios, human micro-actions often co-occur, with multiple micro-actions overlapping in time, such as simultaneous head and hand movements. However, current research primarily focuses on recognizing individual micro-actions while overlooking their co-occurring nature. To narrow 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. Achieving this requires a model capable of accurately capturing both long-term and short-term action relationships to locate and classify multiple micro-actions. To support the MMAD task, we introduce a new dataset named Multi-label Micro-Action-52 (MMA-52), specifically designed to facilitate the detailed analysis and exploration of complex human micro-actions. The proposed MMA-52 dataset is available at: https://github.com/VUT-HFUT/Micro-Action.<|reference_end|>
@article{li2024mmad:,
    title={MMAD: Multi-label Micro-Action Detection in Videos},
    author={Kun Li, Dan Guo, Pengyu Liu, Guoliang Chen, Meng Wang},
    journal={arXiv preprint arXiv:2407.05311},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.05311},
    primaryClass={cs.CV}
}