  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.
