首个足球视觉基础模型，统一处理从识别到推理的多种任务。
SoccerMaster: A Vision Foundation Model for Soccer Understanding
- 用多任务监督预训练构建统一框架，整合多样足球理解任务。
- 自动生成空间标注，融合多个数据集形成大规模预训练资源。
- 在下游任务中超越专用模型，适合需要多任务理解的场景。
足球理解因领域特异性复杂性和独特挑战近年受到越来越多关注。与以往依赖孤立、任务特定专家模型的研究不同，本文提出SoccerMaster——首个针对足球领域的视觉基础模型，通过监督多任务预训练，在单一框架内统一处理从细粒度感知（如运动员检测与识别）到高层语义推理（如事件分类）的多样化任务。具体贡献包括：(i) 提出SoccerMaster，首个基于监督多任务预训练的足球专用视觉基础模型；(ii) 开发自动化数据整理流水线SoccerFactory，生成可扩展的空间标注，并整合多个现有足球视频数据集，构建全面的多任务预训练数据资源；(iii) 通过广泛评估证明，SoccerMaster在多种下游任务中持续优于任务特定专家模型，展现出卓越的泛化能力与优势。相关数据、代码与模型将公开发布。
原文摘要 · Abstract (English)
Soccer understanding has recently garnered growing research interest due to its domain-specific complexity and unique challenges. Unlike prior works that typically rely on isolated, task-specific expert models, this work aims to propose a unified model to handle diverse soccer visual understanding tasks, ranging from fine-grained perception (e.g., athlete detection and identification) to high-level semantic reasoning (e.g., event classification). Concretely, our contributions are threefold: (i) we present SoccerMaster, the first soccer-specific vision foundation model that unifies diverse tasks within a single framework via supervised multi-task pretraining; (ii) we develop an automated data curation pipeline, SoccerFactory, to generate scalable spatial annotations, and integrate multiple existing soccer video datasets as a comprehensive pretraining data resource for multi-task pretraining; and (iii) we conduct extensive evaluations demonstrating that SoccerMaster consistently outperforms task-specific expert models across diverse downstream tasks, highlighting its breadth and superiority. The data, code, and model will be publicly available.
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