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.

本文中提出了一种名为 “三叉神经变压器” 的框架，通过将原始视频数据转化为不同的特征域，同时使用自监督学习的三个自视图变压器和一个交叉视图变压器来增强信息并提取更全面的视频标识，实现了公共 Re-ID 基准上优于其他最先进方法的表现。