EGCSA: An Efficient Gated Attention Mechanism for Robust Person Re-Identification

Person re-identification remains a critical AI task for intelligent surveillance and security systems, demanding robust visual recognition under real-world complexities such as pose variations, occlusions, and lighting changes. We propose the Efficient Gated Channel-Spatial Attention (EGCSA) module an attention mechanism that concurrently processes channel and spatial information through a lightweight multi-stage gating mechanism to extract discriminative features under these challenging conditions. Integrated into ResNet-۵۰, our approach achieves state-of-the-art results on Market-۱۵۰۱ (۹۱.۸% mAP), DukeMTMC-ReID (۸۲.۶% mAP), and MSMT۱۷ (۶۲.۳% mAP) while adding only ۰.۳M parameters. This work demonstrates how efficiently designed attention mechanisms advance applied artificial intelligence by delivering significant performance gains with minimal computational overhead enabling practical deployment of robust Re-ID systems in resource-constrained real-world environments.

کلیدواژه ها:

Attention mechanism ، Discriminative feature leaming ، Efficient gated channel-spatial attention (EGCSA) ، Person re-identification