  Person re-identification aims at identifying a certain pedestrian across
non-overlapping camera networks. Video-based re-identification approaches have
gained significant attention recently, expanding image-based approaches by
learning features from multiple frames. In this work, we propose a novel
Co-Saliency Spatio-Temporal Interaction Network (CSTNet) for person
re-identification in videos. It captures the common salient foreground regions
among video frames and explores the spatial-temporal long-range context
interdependency from such regions, towards learning discriminative pedestrian
representation. Specifically, multiple co-saliency learning modules within
CSTNet are designed to utilize the correlated information across video frames
to extract the salient features from the task-relevant regions and suppress
background interference. Moreover, multiple spatialtemporal interaction modules
within CSTNet are proposed, which exploit the spatial and temporal long-range
context interdependencies on such features and spatial-temporal information
correlation, to enhance feature representation. Extensive experiments on two
benchmarks have demonstrated the effectiveness of the proposed method.
