Online Micro-gesture Recognition Using Data Augmentation and Spatial-Temporal Attention
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866916917202124800 |
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| author | Liu, Pengyu Li, Kun Wang, Fei Wei, Yanyan She, Junhui Guo, Dan |
| author_facet | Liu, Pengyu Li, Kun Wang, Fei Wei, Yanyan She, Junhui Guo, Dan |
| contents | In this paper, we introduce the latest solution developed by our team, HFUT-VUT, for the Micro-gesture Online Recognition track of the IJCAI 2025 MiGA Challenge. The Micro-gesture Online Recognition task is a highly challenging problem that aims to locate the temporal positions and recognize the categories of multiple micro-gesture instances in untrimmed videos. Compared to traditional temporal action detection, this task places greater emphasis on distinguishing between micro-gesture categories and precisely identifying the start and end times of each instance. Moreover, micro-gestures are typically spontaneous human actions, with greater differences than those found in other human actions. To address these challenges, we propose hand-crafted data augmentation and spatial-temporal attention to enhance the model's ability to classify and localize micro-gestures more accurately. Our solution achieved an F1 score of 38.03, outperforming the previous state-of-the-art by 37.9%. As a result, our method ranked first in the Micro-gesture Online Recognition track. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_09512 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Online Micro-gesture Recognition Using Data Augmentation and Spatial-Temporal Attention Liu, Pengyu Li, Kun Wang, Fei Wei, Yanyan She, Junhui Guo, Dan Computer Vision and Pattern Recognition In this paper, we introduce the latest solution developed by our team, HFUT-VUT, for the Micro-gesture Online Recognition track of the IJCAI 2025 MiGA Challenge. The Micro-gesture Online Recognition task is a highly challenging problem that aims to locate the temporal positions and recognize the categories of multiple micro-gesture instances in untrimmed videos. Compared to traditional temporal action detection, this task places greater emphasis on distinguishing between micro-gesture categories and precisely identifying the start and end times of each instance. Moreover, micro-gestures are typically spontaneous human actions, with greater differences than those found in other human actions. To address these challenges, we propose hand-crafted data augmentation and spatial-temporal attention to enhance the model's ability to classify and localize micro-gestures more accurately. Our solution achieved an F1 score of 38.03, outperforming the previous state-of-the-art by 37.9%. As a result, our method ranked first in the Micro-gesture Online Recognition track. |
| title | Online Micro-gesture Recognition Using Data Augmentation and Spatial-Temporal Attention |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2507.09512 |