A Lightweight Multi‐Scale Feature Enhancement Network for Person Re‐ ID

Recent advances in deep learning have significantly propelled progress in person re‐identification. However, many current solutions often prioritize architectural optimization. Although this approach has led to considerable performance improvements, it may inadvertently overlook the critical challenge of suppressing interference from complex background noise. To explore a path toward addressing this aspect, we propose a Multi‐scale Feature Enhancement Network (MSFENet). Our approach includes a spatial‐frequency fusion module designed to guide the network's attention toward pedestrian‐specific regions. Furthermore, the incorporation of frequency‐domain cues is intended to facilitate the capture of fine‐grained details, thereby potentially enhancing robustness. We also design a Multi‐Granularity Fusion (MGFusion) module to help alleviate overfitting and information loss during feature interaction. Experimental results indicate that MSFENet achieves competitive performance across evaluated tasks on the Market1501 and MSMT17 datasets, as well as under cross‐domain settings (MSMT17 → Market1501).