  Recently, the Transformer module has been transplanted from natural language
processing to computer vision. This paper applies the Transformer to
video-based person re-identification, where the key issue is to extract the
discriminative information from a tracklet. We show that, despite the strong
learning ability, the vanilla Transformer suffers from an increased risk of
over-fitting, arguably due to a large number of attention parameters and
insufficient training data. To solve this problem, we propose a novel pipeline
where the model is pre-trained on a set of synthesized video data and then
transferred to the downstream domains with the perception-constrained
Spatiotemporal Transformer (STT) module and Global Transformer (GT) module. The
derived algorithm achieves significant accuracy gain on three popular
video-based person re-identification benchmarks, MARS, DukeMTMC-VideoReID, and
LS-VID, especially when the training and testing data are from different
domains. More importantly, our research sheds light on the application of the
Transformer on highly-structured visual data.
