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SCAN: Self-and-Collaborative Attention Network for Video Person Re-identification
[article]
2018
arXiv
pre-print
Video person re-identification attracts much attention in recent years. It aims to match image sequences of pedestrians from different camera views. Previous approaches usually improve this task from three aspects, including a) selecting more discriminative frames, b) generating more informative temporal representations, and c) developing more effective distance metrics. To address the above issues, we present a novel and practical deep architecture for video person re-identification termed
arXiv:1807.05688v3
fatcat:ti7yyafavrgmlghcnhptfsk5yy