发表机构
School of Artificial Intelligence & Data Science, USTC; Suzhou Institute for Advanced Research, USTC（中国科学技术大学人工智能与数据科学学院; 中国科学技术大学苏州高等研究院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对以球员为中心的球动作检测任务，提出ME-DST模型，通过保留角色槽维度等设计提升性能，在FOOTPASS数据集上较TAAD+DST基线提升10.3个百分点，验证了显式实体建模的有效性。
AI 中文摘要
以球员为中心的球动作检测需要在拥挤、部分可观测的多智能体体育视频中实现时间精确的事件检测以及参与者归属。现有去噪序列转换（DST）基线方法将球员角色维度视为扁平化帧级表示的一部分，这削弱了对球员特定时间演化和球员间交互进行建模的归纳偏置。为解决这一局限，我们提出多实体去噪序列转换（ME-DST）。ME-DST在整个编码过程中保留角色槽维度，使用时间注意力对每个角色槽的历史进行建模，并使用空间注意力在每帧的角色槽之间交换信息。这种分解设计为模型提供了将球员内部演化与球员间上下文分离的直接结构。我们还添加了可学习角色嵌入、源自轨迹的战术特征，以及X3D-L和Swin3D-S的融合视觉预测。在FOOTPASS数据集上的实验表明，ME-DST达到了0.778的Micro F1值，较最强的官方TAAD+DST基线提升了10.3个百分点。受控消融实验显示，保留实体轴和编码角色身份是该性能提升的关键。这些结果表明，显式实体建模是面向以球员为中心的体育事件理解的有效归纳偏置。
英文摘要
Player-centric ball action spotting requires temporally precise event detection together with actor attribution in crowded, partially observed multi-agent sports videos. Existing Denoising Sequence Transduction (DST) baselines treat the player-role dimension as part of a flattened frame-level representation, which weakens the inductive bias for modeling player-specific temporal evolution and inter-player interactions. To address this limitation, we propose Multi-Entity Denoising Sequence Transduction (ME-DST). ME-DST keeps the role-slot dimension throughout encoding. It uses temporal attention to model the history of each role slot, and spatial attention to exchange information across role slots at each frame. This factorized design gives the model a direct structure for separating within-player evolution from inter-player context. We also add learnable role embeddings, tracking-derived tactical features, and fused visual predictions from X3D-L and Swin3D-S. Experiments on the FOOTPASS dataset show that ME-DST reaches a Micro F1 of 0.778. This improves the strongest official TAAD+DST baseline by 10.3 percentage points. Controlled ablations show that preserving the entity axis and encoding role identity are central to this gain. These results suggest that explicit entity modeling is an effective inductive bias for player-centric sports event understanding.
