Cross-modality disentanglement and shared feedback learning for infrared-visible person re-identification

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Infrared-visible person re-identification (IV-ReID) has become a research hotspot in the field of computer vision. Compared with traditional person re-identification, the IV-ReID task is still very challenging due to huge difference between modalities. Most existing approaches are designed to bridge the cross-modality gap through single feature-level constraints, but the results are not very satisfactory. To this end, a novel cross-modality disentanglement and shared feedback (CMDSF) learning framework is proposed. The framework consists of a cross-modality images disentanglement network (CMIDN) and a dual-path shared feedback learning network (DSFLN). Specifically, the former uses a pairing strategy to more efficiently disentangle the cross-modality features and constrain the feature distribution distances between modalities. It achieves modality-level alignment while maintaining specific identity-consistency. The latter adopts a dual-path shared module (DSM) to obtain discriminative mid-level feature information, and achieves feature-level alignment. Furthermore, a feedback scoring module (FSM) with a negative feedback mechanism is proposed to compensate for the weak supervision defect of objective loss during backpropagation. It optimizes model parameters by providing a strong feedback signal. In summary, we propose an efficient learning framework with two parts jointly trained and optimized in an end-to-end manner. Extensive experimental results on two cross-modality datasets demonstrate that our method achieves a competitive performance compared with the state-of-the-art methods.