In recent years, the Transformer architecture has shown its superiority in
the video-based person re-identification task. Inspired by video representation
learning, these methods mainly focus on designing modules to extract
informative spatial and temporal features. However, they are still limited in
extracting local attributes and global identity information, which are critical
for the person re-identification task. In this paper, we propose a novel
Multi-Stage Spatial-Temporal Aggregation Transformer (MSTAT) with two novel
designed proxy embedding modules to address the above issue. Specifically,
MSTAT consists of three stages to encode the attribute-associated, the
identity-associated, and the attribute-identity-associated information from the
video clips, respectively, achieving the holistic perception of the input
person. We combine the outputs of all the stages for the final identification.
In practice, to save the computational cost, the Spatial-Temporal Aggregation
(STA) modules are first adopted in each stage to conduct the self-attention
operations along the spatial and temporal dimensions separately. We further
introduce the Attribute-Aware and Identity-Aware Proxy embedding modules (AAP
and IAP) to extract the informative and discriminative feature representations
at different stages. All of them are realized by employing newly designed
self-attention operations with specific meanings. Moreover, temporal patch
shuffling is also introduced to further improve the robustness of the model.
Extensive experimental results demonstrate the effectiveness of the proposed
modules in extracting the informative and discriminative information from the
videos, and illustrate the MSTAT can achieve state-of-the-art accuracies on
various standard benchmarks.

本文提出了一种新的 Multi-Stage Spatial-Temporal Aggregation Transformer，采用两个代理嵌入模块来全面感知输入个人的属性和身份信息；通过 SPA 模块进行自注意操作，并采用新设计的自注意力操作提取信息，同时引入了时间分块混洗以进一步提高模型的鲁棒性，在各个标准基准测试中也实现了最先进的准确性。