{"ID":2839883,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.14186","arxiv_id":"2511.14186","title":"Few-Shot Precise Event Spotting via Unified Multi-Entity Graph and Distillation","abstract":"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.","short_abstract":"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 train...","url_abs":"https://arxiv.org/abs/2511.14186","url_pdf":"https://arxiv.org/pdf/2511.14186v1","authors":"[\"Zhaoyu Liu\",\"Kan Jiang\",\"Murong Ma\",\"Zhe Hou\",\"Yun Lin\",\"Jin Song Dong\"]","published":"2025-11-18T06:45:42Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.AI\"]","methods":"[]","has_code":false,"code_links":[{"ID":606927,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2839883,"paper_url":"https://arxiv.org/abs/2511.14186","paper_title":"Few-Shot Precise Event Spotting via Unified Multi-Entity Graph and Distillation","repo_url":"https://github.com/LZYAndy/UMEG-Net","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}