提出群体意图预测新任务，用篮球视频建模多人协作目标
Beyond the Individual: Introducing Group Intention Forecasting with SHOT Dataset
- 通过多视角视频捕捉个体行为与互动，识别群体共同目标
- 构建1979段篮球视频数据集，含6类个体属性标注
- 适合研究群体智能、协作行为的算法与应用开发
意图识别传统上聚焦个体意图，忽视了群体场景中的复杂集体意图。为此，我们提出群体意图概念，即由多个个体行动共同产生的共享目标，并引入群体意图预测（GIF）这一新任务：通过分析集体目标显现前的个体行为与互动，预测群体意图何时出现。为研究此任务，我们提出SHOT数据集——首个大规模的群体意图预测数据集，包含1,979段篮球视频片段，来自5个摄像头视角，标注了6种个体属性。该数据集具备三大特征：多主体信息、多视角适应性与多层次意图，适用于研究涌现的群体意图。此外，我们提出GIFT框架，通过提取细粒度个体特征并建模动态演变的群体行为来预测意图出现。实验验证了SHOT与GIFT的有效性，为未来群体意图研究奠定坚实基础。数据集已公开：https://xinyi-hu.github.io/SHOT_DATASET。
原文摘要 · Abstract (English)
Intention recognition has traditionally focused on individual intentions, overlooking the complexities of collective intentions in group settings. To address this limitation, we introduce the concept of group intention, which represents shared goals emerging through the actions of multiple individuals, and Group Intention Forecasting (GIF), a novel task that forecasts when group intentions will occur by analyzing individual actions and interactions before the collective goal becomes apparent. To investigate GIF in a specific scenario, we propose SHOT, the first large-scale dataset for GIF, consisting of 1,979 basketball video clips captured from 5 camera views and annotated with 6 types of individual attributes. SHOT is designed with 3 key characteristics: multi-individual information, multi-view adaptability, and multi-level intention, making it well-suited for studying emerging group intentions. Furthermore, we introduce GIFT (Group Intention ForecasTer), a framework that extracts fine-grained individual features and models evolving group dynamics to forecast intention emergence. Experimental results confirm the effectiveness of SHOT and GIFT, establishing a strong foundation for future research in group intention forecasting. The dataset is available at https://xinyi-hu.github.io/SHOT_DATASET.
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