标题： 多焦点视频组活动哈希 显示英文标题

标题： Multi-Focused Video Group Activities Hashing

摘要： 随着各种复杂场景中视频数据的爆炸式增长，快速检索群体活动已成为一个紧迫的问题。 然而，许多任务只能检索整个视频，而不是活动粒度。 为了解决这个问题，我们首次提出了一种新的STVH（时空交错视频哈希）技术。 通过一个统一的框架，STVH同时建模个体对象动态和群体交互，捕捉群体视觉特征和位置特征上的时空演变。 此外，在现实生活中的视频检索场景中，有时需要活动特征，而有时则需要对象的视觉特征。 我们随后进一步提出了一种新的M-STVH（多焦点时空视频哈希）作为增强版本，以处理这个困难的任务。 该先进方法通过多焦点表示学习进行分层特征集成，使模型能够同时关注活动语义特征和对象视觉特征。 我们在公开可用的数据集上进行了比较实验，STVH和M-STVH都能取得出色的结果。 显示英文摘要

摘要： With the explosive growth of video data in various complex scenarios, quickly retrieving group activities has become an urgent problem. However, many tasks can only retrieve videos focusing on an entire video, not the activity granularity. To solve this problem, we propose a new STVH (spatiotemporal interleaved video hashing) technique for the first time. Through a unified framework, the STVH simultaneously models individual object dynamics and group interactions, capturing the spatiotemporal evolution on both group visual features and positional features. Moreover, in real-life video retrieval scenarios, it may sometimes require activity features, while at other times, it may require visual features of objects. We then further propose a novel M-STVH (multi-focused spatiotemporal video hashing) as an enhanced version to handle this difficult task. The advanced method incorporates hierarchical feature integration through multi-focused representation learning, allowing the model to jointly focus on activity semantics features and object visual features. We conducted comparative experiments on publicly available datasets, and both STVH and M-STVH can achieve excellent results.