Unsupervised Domain Adaptation for Person Re-Identification Via Individual-Preserving and Environmental-Switching Cyclic Generation

Abstract

Unsupervised domain adaptation for person re-identification (Re-ID) suffers severe domain discrepancies between source and target domains. To reduce the domain shift caused by the changes of context, camera style, or viewpoint, existing methods in this field fine-tune and adapt the Re-ID model with augmented samples, either translating source samples to the target style or assigning pseudo labels to the target. The former methods may lose identity details but keep redundant source background during translation. In contrast, the latter techniques may give noisy labels when the model meets the unseen background and person pose. We mitigate the domain shift in the former translation direction by cyclically decoupling environment and identity-related features. We propose a novel individual-preserving and environmental-switching cyclic generation network (IPES-GAN). Our network has the following distinct features: 1) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Decoupled features instead of fused features:</i> we encode the images into an individual part and an environmental part, which are proved beneficial to generation and adaptation; 2) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Cyclic generation instead of one-step adaptive generation</i> . We swap source and target environment features to generate cross-domain images with preserved identity-related features conditioned with source (target) background features and then changed again to generate back the input image so that cyclic generation runs in a self-supervised way. Experiments carried out on two significant benchmarks: Market-1501 and DukeMTMC-Reid, reveal state-of-the-art performance.