SoccerMaster：面向足球理解的视觉基础模型
SoccerMaster: A Vision Foundation Model for Soccer Understanding
可借鉴其「监督式多任务预训练」把检测、事件分类等异构任务统一进单个基础模型的范式，以及 SoccerFactory 这种用自动化流水线从现有视频数据集批量生成多任务标注的做法，用于构建其他垂直领域的基础模型。
原文依据 the first soccer-specific vision foundation model that unifies comprehensive understanding tasks within a single framework via supervised multi-task pretraining
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论足球理解因其领域特有的复杂性与独特挑战，近来受到越来越多的研究关注。然而，已有工作通常依赖针对特定任务的专家模型，这类模型资源开销大，且不利于对比赛形成整体性认识。本文旨在提出一个统一框架，使单一模型能够处理多样化的足球视觉理解任务，涵盖细粒度感知（如运动员检测）与语义推理（如事件分类）。具体而言，本文做出以下贡献：(i) 我们提出 SoccerMaster，这是首个足球专用的视觉基础模型，通过监督式多任务预训练在单一框架内统一了全面的理解任务；(ii) 我们整合了多个现有的足球视频数据集，并开发了一条自动化数据整理流水线 SoccerFactory，用于生成可扩展的多任务训练标注；(iii) 我们开展了大量实验，证明 SoccerMaster 在多样化的下游任务上持续优于针对特定任务的专家模型，凸显了其广度与优越性。数据、代码与模型将向研究社区公开。
英文原文摘要 1240 字符
Soccer understanding has recently garnered growing research interest due to its domain-specific complexity and unique challenges.However, prior works typically rely on task-specific expert models, which are resource-intensive and hinder a holistic view of the game.This paper aims to propose a unified framework that enables a single model to handle diverse soccer visual understanding tasks, spanning both fine-grained perception (e.g., athlete detection) and semantic reasoning (e.g., event classification).Concretely, we make the following contributions in this paper:(i) we present **SoccerMaster**, the first soccer-specific vision foundation model that unifies comprehensive understanding tasks within a single framework via **supervised multi-task pretraining**;(ii) we consolidate multiple existing soccer video datasets and develop an automated data curation pipeline, termed as **SoccerFactory**, to produce scalable multi-task training annotations;and (iii) we conduct extensive experiments demonstrating that SoccerMaster consistently outperforms task-specific expert models across diverse downstream tasks, underscoring its breadth and superiority.The data, code, and model will be publicly available to the research community.