  Previous Person Re-Identification (Re-ID) models aim to focus on the most
discriminative region of an image, while its performance may be compromised
when that region is missing caused by camera viewpoint changes or occlusion. To
solve this issue, we propose a novel model named Hierarchical Bi-directional
Feature Perception Network (HBFP-Net) to correlate multi-level information and
reinforce each other. First, the correlation maps of cross-level feature-pairs
are modeled via low-rank bilinear pooling. Then, based on the correlation maps,
Bi-directional Feature Perception (BFP) module is employed to enrich the
attention regions of high-level feature, and to learn abstract and specific
information in low-level feature. And then, we propose a novel end-to-end
hierarchical network which integrates multi-level augmented features and inputs
the augmented low- and middle-level features to following layers to retrain a
new powerful network. What's more, we propose a novel trainable generalized
pooling, which can dynamically select any value of all locations in feature
maps to be activated. Extensive experiments implemented on the mainstream
evaluation datasets including Market-1501, CUHK03 and DukeMTMC-ReID show that
our method outperforms the recent SOTA Re-ID models.
