Cross-domain person re-identification with normalized and enhanced feature

Existing person re-identification methods are difficult to generalize to unseen datasets because of the domain gaps. The key to solving this problem is to extract domain-invariant and discriminative features. In this paper, we propose a normalization and enhancement (NE) module that can effectively suppress domain gaps and enhance pedestrian features without any target domain data, thereby enhancing the generalization ability of the model. NE module consists of a residual connection and a channel attention (CA) block. The residual connection is designed to suppress the domain gaps while retaining discriminative information. The proposed CA block embeds spatial information into the channel dimension and models the dependencies between channels to enhance pedestrian features. In addition, the NE loss is designed to constrain the training of NE module to extract domain-invariant features with excellent distribution. Ablation experiments were conducted on Market-1501, DukeMTMC-reID, CUHK03-NP and MSMT17. Experimental results confirmed the superiority of the proposed method, the mAP reached a maximum of 47.3% in cross-domain scenarios. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023.