# AMNS: Attention-Weighted Selective Mask and Noise Label Suppression for Text-to-Image Person Retrieval

arXiv: https://arxiv.org/abs/2409.06385 · HTML: https://mlsanity.com/paper/2409.06385/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 36 references that are in the map.
- First posted on arXiv 10 Sep 2024.

## Builds on (most cited first, 29 of 29)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://mlsanity.com/paper/2201.12086/) (2022)
- [A Closer Look at Memorization in Deep Networks](https://mlsanity.com/paper/1706.05394/) (2017)
- [Deep Learning for Person Re-identification: A Survey and Outlook](https://mlsanity.com/paper/2001.04193/) (2020)
- [Person Search with Natural Language Description](https://mlsanity.com/paper/1702.05729/) (2017)
- [Dual-Path Convolutional Image-Text Embeddings with Instance Loss](https://mlsanity.com/paper/1711.05535/) (2017)
- [Cross-Modal Implicit Relation Reasoning and Aligning for Text-to-Image Person Retrieval](https://mlsanity.com/paper/2303.12501/) (2023)
- [Semantically Self-Aligned Network for Text-to-Image Part-aware Person Re-identification](https://mlsanity.com/paper/2107.12666/) (2021)
- [DSSL: Deep Surroundings-person Separation Learning for Text-based Person Retrieval](https://mlsanity.com/paper/2109.05534/) (2021)
- [CLIP-Driven Fine-grained Text-Image Person Re-identification](https://mlsanity.com/paper/2210.10276/) (2022)
- [See Finer, See More: Implicit Modality Alignment for Text-based Person Retrieval](https://mlsanity.com/paper/2208.08608/) (2022)
- [Learning Granularity-Unified Representations for Text-to-Image Person Re-identification](https://mlsanity.com/paper/2207.07802/) (2022)
- [A Survey on Masked Autoencoder for Self-supervised Learning in Vision and Beyond](https://mlsanity.com/paper/2208.00173/) (2022)
- [Noisy-Correspondence Learning for Text-to-Image Person Re-identification](https://mlsanity.com/paper/2308.09911/) (2023)
- [PLIP: Language-Image Pre-training for Person Representation Learning](https://mlsanity.com/paper/2305.08386/) (2023)
- [An Empirical Study of CLIP for Text-based Person Search](https://mlsanity.com/paper/2308.10045/) (2023)
- [AXM-Net: Implicit Cross-Modal Feature Alignment for Person Re-identification](https://mlsanity.com/paper/2101.08238/) (2021)
- [UFineBench: Towards Text-based Person Retrieval with Ultra-fine Granularity](https://mlsanity.com/paper/2312.03441/) (2023)
- [Masked Image Modeling: A Survey](https://mlsanity.com/paper/2408.06687/) (2024)
- [Noisy Correspondence Learning with Meta Similarity Correction](https://mlsanity.com/paper/2304.06275/) (2023)
- [Attentive Mask CLIP](https://mlsanity.com/paper/2212.08653/) (2022)
- [Cross-modal Active Complementary Learning with Self-refining Correspondence](https://mlsanity.com/paper/2310.17468/) (2023)
- [Unified Pre-training with Pseudo Texts for Text-To-Image Person Re-identification](https://mlsanity.com/paper/2309.01420/) (2023)
- [CLIP-based Synergistic Knowledge Transfer for Text-based Person Retrieval](https://mlsanity.com/paper/2309.09496/) (2023)
- [BiLMa: Bidirectional Local-Matching for Text-based Person Re-identification](https://mlsanity.com/paper/2309.04675/) (2023)
- [Learning Comprehensive Representations with Richer Self for Text-to-Image Person Re-Identification](https://mlsanity.com/paper/2310.11210/) (2023)
- [Beat: Bi-directional One-to-Many Embedding Alignment for Text-based Person Retrieval](https://mlsanity.com/paper/2406.05620/) (2024)

## Built on by (most cited first, 2 of 2)

- [AuEmoChat: Authentic Emotion Understanding and Rendering for Conversational Speech Synthesis](https://mlsanity.com/paper/2607.15755/) (2026)
- [S2Dialog: Multimodal Dialogue Retrieval with Semantic and Acoustic-Style Modeling](https://mlsanity.com/paper/2608.14029/) (2026)

## Abstract (arXiv)

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.