An Efficient Multi-Branch Attention Network for Person Re-Identification

Abstract

Due to the absence of tailored designs that address challenges such as variations in scale, disparities in illumination, and instances of occlusion, the implementation of current person re-identification techniques remains challenging in practical applications. An Efficient Multi-Branch Attention Network over OSNet (EMANet) is proposed. The structure is composed of three parts, the global branch, relational branch, and global contrastive pooling branch, and corresponding features are obtained from different branches. With the attention mechanism, which focuses on important features, DAS attention evaluates the significance of learned features, awarding higher ratings to those that are deemed crucial and lower ratings to those that are considered distracting. This approach leads to an enhancement in identification accuracy by emphasizing important features while discounting the influence of distracting ones. Identity loss and adaptive sparse pairwise loss are used to efficiently facilitate the information interaction. In experiments on the Market-1501 mainstream dataset, EMANet exhibited high identification accuracies of 96.1% and 89.8% for Rank-1 and mAP, respectively. The results indicate the superiority and effectiveness of the proposed model.