  Deep part-based methods in recent literature have revealed the great
potential of learning local part-level representation for pedestrian image in
the task of person re-identification. However, global features that capture
discriminative holistic information of human body are usually ignored or not
well exploited. This motivates us to investigate joint learning global and
local features from pedestrian images. Specifically, in this work, we propose a
novel framework termed tree branch network (TBN) for person re-identification.
Given a pedestrain image, the feature maps generated by the backbone CNN, are
partitioned recursively into several pieces, each of which is followed by a
bottleneck structure that learns finer-grained features for each level in the
hierarchical tree-like framework. In this way, representations are learned in a
coarse-to-fine manner and finally assembled to produce more discriminative
image descriptions. Experimental results demonstrate the effectiveness of the
global and local feature learning method in the proposed TBN framework. We also
show significant improvement in performance over state-of-the-art methods on
three public benchmarks: Market-1501, CUHK-03 and DukeMTMC.
