群体动力学感知的DINO：将人群动态纳入DINO以实现自监督群体活动特征学习
Group-DINOmics: Incorporating People Dynamics into DINO for Self-supervised Group Activity Feature Learning
AI总结：
本文提出无需群体活动标注的群体活动特征学习方法，通过动态感知和群体感知的预训练任务，结合DINO提供的局部和全局特征，提升群体活动识别与检索性能。
摘要：
本文提出Group Activity Feature (GAF)学习方法，无需群体活动标注。与以往使用低级静态局部特征学习GAFs不同，我们提出利用动态感知和群体感知的预训练任务，结合DINO提供的局部和全局特征，实现动态感知的GAF学习。为适应局部动态和全局群体特征，我们的预训练任务分别使用人群流估计和与群体相关的物体位置估计。人群流估计用于表示每个人局部运动，是理解群体活动的重要线索。相反，群体相关的物体位置估计促使GAFs学习场景上下文（例如，人物和物体的空间关系）作为全局特征。在公共数据集上的全面实验表明，我们的方法在群体活动检索和识别中达到最先进的性能。我们的消融研究验证了我们方法中每个组件的有效性。代码：https://github.com/tezuka0001/Group-DINOmics.
英文摘要：
This paper proposes Group Activity Feature (GAF) learning without group activity annotations. Unlike prior work, which uses low-level static local features to learn GAFs, we propose leveraging dynamics-aware and group-aware pretext tasks, along with local and global features provided by DINO, for group-dynamics-aware GAF learning. To adapt DINO and GAF learning to local dynamics and global group features, our pretext tasks use person flow estimation and group-relevant object location estimation, respectively. Person flow estimation is used to represent the local motion of each person, which is an important cue for understanding group activities. In contrast, group-relevant object location estimation encourages GAFs to learn scene context (e.g., spatial relations of people and objects) as global features. Comprehensive experiments on public datasets demonstrate the state-of-the-art performance of our method in group activity retrieval and recognition. Our ablation studies verify the effectiveness of each component in our method. Code: https://github.com/tezuka0001/Group-DINOmics.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
