  We consider the problem of video-based person re-identification. The goal is
to identify a person from videos captured under different cameras. In this
paper, we propose an efficient spatial-temporal attention based model for
person re-identification from videos. Our method generates an attention score
for each frame based on frame-level features. The attention scores of all
frames in a video are used to produce a weighted feature vector for the input
video. Unlike most existing deep learning methods that use global
representation, our approach focuses on attention scores. Extensive experiments
on two benchmark datasets demonstrate that our method achieves the
state-of-the-art performance. This is a technical report.
