Computer Science > Computer Vision and Pattern Recognition
[Submitted on 10 Sep 2024 (v1), last revised 8 Feb 2025 (this version, v3)]
Title:AMNS: Attention-Weighted Selective Mask and Noise Label Suppression for Text-to-Image Person Retrieval
View PDF HTML (experimental)Abstract: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.
Submission history
From: Runqing Zhang [view email][v1] Tue, 10 Sep 2024 10:08:01 UTC (4,376 KB)
[v2] Wed, 11 Sep 2024 02:45:39 UTC (10,530 KB)
[v3] Sat, 8 Feb 2025 03:45:33 UTC (11,090 KB)
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