  Part-level representations are important for robust person re-identification
(ReID), but in practice feature quality suffers due to the body part
misalignment problem. In this paper, we present a robust, compact, and
easy-to-use method called the Multi-task Part-aware Network (MPN), which is
designed to extract semantically aligned part-level features from pedestrian
images. MPN solves the body part misalignment problem via multi-task learning
(MTL) in the training stage. More specifically, it builds one main task (MT)
and one auxiliary task (AT) for each body part on the top of the same backbone
model. The ATs are equipped with a coarse prior of the body part locations for
training images. ATs then transfer the concept of the body parts to the MTs via
optimizing the MT parameters to identify part-relevant channels from the
backbone model. Concept transfer is accomplished by means of two novel
alignment strategies: namely, parameter space alignment via hard parameter
sharing and feature space alignment in a class-wise manner. With the aid of the
learned high-quality parameters, MTs can independently extract semantically
aligned part-level features from relevant channels in the testing stage. MPN
has three key advantages: 1) it does not need to conduct body part detection in
the inference stage; 2) its model is very compact and efficient for both
training and testing; 3) in the training stage, it requires only coarse priors
of body part locations, which are easy to obtain. Systematic experiments on
four large-scale ReID databases demonstrate that MPN consistently outperforms
state-of-the-art approaches by significant margins. Code is available at
https://github.com/WangKan0128/MPN.
