Hierarchical Feature Fusion for Cross-Modality Person Re-identification

To address the limitations of visible light cameras that cannot function effectively at night, infrared cameras have become the optimal supplement. However, current methods for visible–infrared cross-modality person re-identification focus solely on feature combination and fusion, neglecting the importance of feature alignment. To address this issue, we introduce a novel Hierarchical Feature Fusion (HFF) network, which comprehensively integrates features across various levels through sequential feature extraction. Specifically, we design a pixel-level contrastive loss function that makes pixels in the same region of cross-modality images more similar and distinguishes pixel features at different locations, thereby extracting similar low-frequency information in the shallow network. Furthermore, in the deep network, we extract high-frequency information of different modalities through the Bi-Transformer Layer and propose Node-level Coupling Attention and Modality-level Decoupling Attention. Coupling attention is used for high-frequency information coupling within the same modality while decoupling attention is used for high-frequency information decoupling between different modalities to obtain more texture and detail information. Through a series of experimental results, we validate the superiority of the proposed HFF network in cross-modality person re-identification. Our proposed method achieved 87.16% and 95.23% Rank-1 on the SYSU-MM01 and RegDB datasets, respectively, and extensive experiments have validated its effectiveness in feature alignment.