Most existing text-to-image person retrieval methods usually assume that the training image-text pairs are perfectly aligned; however, the noisy correspondence(NC) issue (i.e., incorrect or unreliable alignment) exists due to poor image quality and labeling errors. Additionally, random masking augmentation may inadvertently discard critical semantic content, introducing noisy matches between images and text descriptions. To address the above two challenges, we propose a noise label suppression method to mitigate NC and an Attention-Weighted Selective Mask (AWM) strategy to resolve the issues caused by random masking. Specifically, the Bidirectional Similarity Distribution Matching (BSDM) loss enables the model to effectively learn from positive pairs while preventing it from over-relying on them, thereby mitigating the risk of overfitting to noisy labels. In conjunction with this, Weight Adjustment Focal (WAF) loss improves the model's ability to handle hard samples. Furthermore, AWM processes raw images through an EMA version of the image encoder, selectively retaining tokens with strong semantic connections to the text, enabling better feature extraction. Extensive experiments demonstrate the effectiveness of our approach in addressing noise-related issues and improving retrieval performance.
SCING:Towards More Efficient and Robust Person Re-Identification through Selective Cross-modal Prompt Tuning
Huazhong University of Science and Technology（华中科技大学）；Huazhong Agricultural University（华中农业大学）；University of California, Santa Cruz（加州大学圣克ruz分校）；City University of Hong Kong (Dongguan)（香港城市大学（东莞））
End-to-End Comparative Attention Networks for Person Re-identification
Hefei University of Technology（合肥工业大学）；National University of Singapore（新加坡国立大学）；Artificial Intelligence Institute, Qihoo 360 Technology Company, Ltd.（奇虎360科技有限公司人工智能研究院）
Heterogeneous Vision-Language Ensemble with Disagreement-Aware Reranking for Text-Based Person Anomaly Retrieval
Hanoi University of Science and Technology（河内科技大学）；University of Information Technology, VNU-HCM（胡志明市国家大学信息技术大学）；Vietnam National University, Ho Chi Minh City（胡志明市国家大学）；VinUniversity；Vietnamese-German University（越南德国大学）
