  Person re-identification (re-ID) under various occlusions has been a
long-standing challenge as person images with different types of occlusions
often suffer from misalignment in image matching and ranking. Most existing
methods tackle this challenge by aligning spatial features of body parts
according to external semantic cues or feature similarities but this alignment
approach is complicated and sensitive to noises. We design DRL-Net, a
disentangled representation learning network that handles occluded re-ID
without requiring strict person image alignment or any additional supervision.
Leveraging transformer architectures, DRL-Net achieves alignment-free re-ID via
global reasoning of local features of occluded person images. It measures image
similarity by automatically disentangling the representation of undefined
semantic components, e.g., human body parts or obstacles, under the guidance of
semantic preference object queries in the transformer. In addition, we design a
decorrelation constraint in the transformer decoder and impose it over object
queries for better focus on different semantic components. To better eliminate
interference from occlusions, we design a contrast feature learning technique
(CFL) for better separation of occlusion features and discriminative ID
features. Extensive experiments over occluded and holistic re-ID benchmarks
(Occluded-DukeMTMC, Market1501 and DukeMTMC) show that the DRL-Net achieves
superior re-ID performance consistently and outperforms the state-of-the-art by
large margins for Occluded-DukeMTMC.
