  This paper mainly studies one-example and few-example video person
re-identification. A multi-branch network PAM that jointly learns local and
global features is proposed. PAM has high accuracy, few parameters and
converges fast, which is suitable for few-example person re-identification. We
iteratively estimates labels for unlabeled samples, incorporates them into
training sets, and trains a more robust network. We propose the static relative
distance sampling(SRD) strategy based on the relative distance between classes.
For the problem that SRD can not use all unlabeled samples, we propose adaptive
relative distance sampling (ARD) strategy. For one-example setting, We get
89.78\%, 56.13\% rank-1 accuracy on PRID2011 and iLIDS-VID respectively, and
85.16\%, 45.36\% mAP on DukeMTMC and MARS respectively, which exceeds the
previous methods by large margin.

Computer Vision and Pattern Recognition