一段视频值得三次观看：用于基于视频的人员重新识别的三叉Transformers
A Video Is Worth Three Views: Trigeminal Transformers for Video-based Person Re-identification
摘要：
基于视频的人物再识别（Re-ID）旨在检索非重叠摄像机下的同一个人的视频序列。以往的方法通常集中在有限的视图，如空间，时间或时空视图，缺乏在不同的特征域的观察。为了获取更丰富的感知信息并提取更全面的视频表征，本文提出了一种新的基于视频的人物身份识别框架--三叉Transformers（TMT）。更具体地说，我们设计了一个三叉特征提取器，将原始视频数据联合变换到空间、时间和时空域。此外，受视觉变换器Transformer的巨大成功的启发，本文提出了一种用于视频人Re-ID的Transformer结构。在我们的工作中，我们提出了三种自视图变换器，利用局部特征之间的关系在空间域、时间域和时空域进行信息增强。此外，一个跨视图Transformer提出了聚合的多视图功能，全面的视频表示。实验结果表明，我们的方法可以实现更好的性能比其他国家的最先进的方法在公共Re-ID基准。我们将发布模型复制的代码。
英文摘要：
Video-based person re-identification (Re-ID) aims to retrieve video sequences of the same person under non-overlapping cameras. Previous methods usually focus on limited views, such as spatial, temporal or spatial-temporal view, which lack of the observations in different feature domains. To capture richer perceptions and extract more comprehensive video representations, in this paper we propose a novel framework named Trigeminal Transformers (TMT) for video-based person Re-ID. More specifically, we design a trigeminal feature extractor to jointly transform raw video data into spatial, temporal and spatial-temporal domain. Besides, inspired by the great success of vision transformer, we introduce the transformer structure for video-based person Re-ID. In our work, three self-view transformers are proposed to exploit the relationships between local features for information enhancement in spatial, temporal and spatial-temporal domains. Moreover, a cross-view transformer is proposed to aggregate the multi-view features for comprehensive video representations. The experimental results indicate that our approach can achieve better performance than other state-of-the-art approaches on public Re-ID benchmarks. We will release the code for model reproduction.
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。
