  Person re-identification (ReID) is to identify pedestrians observed from
different camera views based on visual appearance. It is a challenging task due
to large pose variations, complex background clutters and severe occlusions.
Recently, human pose estimation by predicting joint locations was largely
improved in accuracy. It is reasonable to use pose estimation results for
handling pose variations and background clutters, and such attempts have
obtained great improvement in ReID performance. However, we argue that the pose
information was not well utilized and hasn't yet been fully exploited for
person ReID.
  In this work, we introduce a novel framework called Attention-Aware
Compositional Network (AACN) for person ReID. AACN consists of two main
components: Pose-guided Part Attention (PPA) and Attention-aware Feature
Composition (AFC). PPA is learned and applied to mask out undesirable
background features in pedestrian feature maps. Furthermore, pose-guided
visibility scores are estimated for body parts to deal with part occlusion in
the proposed AFC module. Extensive experiments with ablation analysis show the
effectiveness of our method, and state-of-the-art results are achieved on
several public datasets, including Market-1501, CUHK03, CUHK01, SenseReID,
CUHK03-NP and DukeMTMC-reID.
