Few-Shot Precise Event Spotting via Unified Multi-Entity Graph and Distillation
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866908662020177920 |
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| author | Liu, Zhaoyu Jiang, Kan Ma, Murong Hou, Zhe Lin, Yun Dong, Jin Song |
| author_facet | Liu, Zhaoyu Jiang, Kan Ma, Murong Hou, Zhe Lin, Yun Dong, Jin Song |
| contents | Precise event spotting (PES) aims to recognize fine-grained events at exact moments and has become a key component of sports analytics. This task is particularly challenging due to rapid succession, motion blur, and subtle visual differences. Consequently, most existing methods rely on domain-specific, end-to-end training with large labeled datasets and often struggle in few-shot conditions due to their dependence on pixel- or pose-based inputs alone. However, obtaining large labeled datasets is practically hard. We propose a Unified Multi-Entity Graph Network (UMEG-Net) for few-shot PES. UMEG-Net integrates human skeletons and sport-specific object keypoints into a unified graph and features an efficient spatio-temporal extraction module based on advanced GCN and multi-scale temporal shift. To further enhance performance, we employ multimodal distillation to transfer knowledge from keypoint-based graphs to visual representations. Our approach achieves robust performance with limited labeled data and significantly outperforms baseline models in few-shot settings, providing a scalable and effective solution for few-shot PES. Code is publicly available at https://github.com/LZYAndy/UMEG-Net. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_14186 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Few-Shot Precise Event Spotting via Unified Multi-Entity Graph and Distillation Liu, Zhaoyu Jiang, Kan Ma, Murong Hou, Zhe Lin, Yun Dong, Jin Song Computer Vision and Pattern Recognition Artificial Intelligence Precise event spotting (PES) aims to recognize fine-grained events at exact moments and has become a key component of sports analytics. This task is particularly challenging due to rapid succession, motion blur, and subtle visual differences. Consequently, most existing methods rely on domain-specific, end-to-end training with large labeled datasets and often struggle in few-shot conditions due to their dependence on pixel- or pose-based inputs alone. However, obtaining large labeled datasets is practically hard. We propose a Unified Multi-Entity Graph Network (UMEG-Net) for few-shot PES. UMEG-Net integrates human skeletons and sport-specific object keypoints into a unified graph and features an efficient spatio-temporal extraction module based on advanced GCN and multi-scale temporal shift. To further enhance performance, we employ multimodal distillation to transfer knowledge from keypoint-based graphs to visual representations. Our approach achieves robust performance with limited labeled data and significantly outperforms baseline models in few-shot settings, providing a scalable and effective solution for few-shot PES. Code is publicly available at https://github.com/LZYAndy/UMEG-Net. |
| title | Few-Shot Precise Event Spotting via Unified Multi-Entity Graph and Distillation |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2511.14186 |