  Video-based person re-identification (Re-ID) is an important computer vision
task. The batch-hard triplet loss frequently used in video-based person Re-ID
suffers from the Distance Variance among Different Positives (DVDP) problem. In
this paper, we address this issue by introducing a new metric learning method
called Attribute-aware Identity-hard Triplet Loss (AITL), which reduces the
intra-class variation among positive samples via calculating attribute
distance. To achieve a complete model of video-based person Re-ID, a multi-task
framework with Attribute-driven Spatio-Temporal Attention (ASTA) mechanism is
also proposed. Extensive experiments on MARS and DukeMTMC-VID datasets shows
that both the AITL and ASTA are very effective. Enhanced by them, even a simple
light-weighted video-based person Re-ID baseline can outperform existing
state-of-the-art approaches. The codes has been published on
https://github.com/yuange250/Video-based-person-ReID-with-Attribute-information.
