摘要
Abstract
Cloth-Changing person Re-Identification(CC-ReID)aims to identify target pedestrians wearing different outfits.Existing methods incorporate additional information(such as contours,gait,and 3D information)to assist the model in learning the clothing-agnostic features of pedestrians.However,owing to factors such as lighting and pose variations,the extracted biometric features may contain errors.To enhance accuracy,this paper explores the application of Contrastive Language-Image Pre-training(CLIP)and proposes CLIP-driven Fine-grained Feature Enhancement(CFFE)for CC-ReID.This method first models the potential intrinsic relationship between the class text features and image features extracted by CLIP.Subsequently,it uses a salient feature retention module and a saliency feature guiding module.The saliency feature retention module utilizes attention masks to locate foreground regions relevant to clothing and erases these features to ensure that the network focus on effective non-clothing features.Next,the saliency feature guidance module focuses on the important local and global features of pedestrians through attention mechanisms.The CFFE method achieves detection accuracies of 42.1％,71.1％,and 89.9％on the LTCC,PRCC,and VC-Clothes datasets,respectively.Compared with algorithms such as AIM and CAL,CFFE extracts more robust features,showing significant improvements across multiple metrics.关键词
换装行人重识别/对比语言-图像预训练/特征保留策略/注意力机制/语义解析Key words
Cloth-Changing person Re-Identification(CC-ReID)/Contrastive Language-Image Pre-training(CLIP)/feature retention strategy/attention mechanisms/semantic parsing分类
信息技术与安全科学
