基于语义耦合和身份一致性的跨模态行人重识别方法

Cross-modality person re-identification based on semantic coupling and identity-consistence constraint

针对跨模态行人重识别面临的较大跨模态差异和类内变化的问题,提出了一种基于语义耦合和身份一致性的跨模态行人重识别方法。在语义层面,通过双向耦合不同模态的语义特征,实现不同模态间语义的交互融合,有效缓解了跨模态差异;在行人身份层面,通过优化跨模态三元组损失和身份损失,实现类内身份信息一致性,有效缓解了类内变化问题。实验结果表明,本文算法能够有效提升跨模态行人重识别精度,与基线方法相比,Top-1和mAP指标精度提升了10%以上。

To solve the problem of the large inter-modality discrepancy and the intra-class variations in cross modality person re-Identification(CM-Reid),a novel CM-Reid framework based on semantic coupling and identity-consistence constraint was proposed.In the semantic level,the semantic representations bi-directionally and fuse the semantic information between different modalities were coupled to alleviate the inter-modality discrepancy.In the identity level,the cross-modality triplet loss and identity loss to maintain the identity consistence were optimized to alleviate the intra-class variations.The experimental results show that the proposed method can effectively improve the performance of CM-Reid.Compared with the baseline method,the accuracy of Top-1 and mAP indicators is improved by more than 10%.