# Few-Shot Precise Event Spotting via Unified Multi-Entity Graph and Distillation

arXiv: https://arxiv.org/abs/2511.14186 · HTML: https://mlsanity.com/paper/2511.14186/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Nov 2025.

## Built on by (most cited first, 3 of 3)

- [AdaSpot: Spend Resolution Where It Matters for Precise Event Spotting](https://mlsanity.com/paper/2602.22073/) (2026)
- [From Skeletons to Pixels: Few-Shot Precise Event Spotting via Representation and Prediction Distillation](https://mlsanity.com/paper/2604.22839/) (2026)
- [Temporal Feature Distillation for Label-Efficient Precise Event Spotting in Sports Videos](https://mlsanity.com/paper/2607.10998/) (2026)

## Abstract (arXiv)

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.