# Noisy Correspondence Learning with Meta Similarity Correction

arXiv: https://arxiv.org/abs/2304.06275 · HTML: https://mlsanity.com/paper/2304.06275/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 47 references that are in the map.
- First posted on arXiv 13 Apr 2023.

## Builds on (most cited first, 24 of 24)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels](https://mlsanity.com/paper/1804.06872/) (2018)
- [A Closer Look at Memorization in Deep Networks](https://mlsanity.com/paper/1706.05394/) (2017)
- [Learning to Generalize: Meta-Learning for Domain Generalization](https://mlsanity.com/paper/1710.03463/) (2017)
- [DivideMix: Learning with Noisy Labels as Semi-supervised Learning](https://mlsanity.com/paper/2002.07394/) (2020)
- [Learning to Reweight Examples for Robust Deep Learning](https://mlsanity.com/paper/1803.09050/) (2018)
- [Stacked Cross Attention for Image-Text Matching](https://mlsanity.com/paper/1803.08024/) (2018)
- [Bilevel Programming for Hyperparameter Optimization and Meta-Learning](https://mlsanity.com/paper/1806.04910/) (2018)
- [Unsupervised Label Noise Modeling and Loss Correction](https://mlsanity.com/paper/1904.11238/) (2019)
- [Objects that Sound](https://mlsanity.com/paper/1712.06651/) (2017)
- [Visual Semantic Reasoning for Image-Text Matching](https://mlsanity.com/paper/1909.02701/) (2019)
- [Learning to Learn from Noisy Labeled Data](https://mlsanity.com/paper/1812.05214/) (2018)
- [Learning Deep Structure-Preserving Image-Text Embeddings](https://mlsanity.com/paper/1511.06078/) (2015)
- [Similarity Reasoning and Filtration for Image-Text Matching](https://mlsanity.com/paper/2101.01368/) (2021)
- [IMRAM: Iterative Matching with Recurrent Attention Memory for Cross-Modal Image-Text Retrieval](https://mlsanity.com/paper/2003.03772/) (2020)
- [Meta Label Correction for Noisy Label Learning](https://mlsanity.com/paper/1911.03809/) (2019)
- [Graph Structured Network for Image-Text Matching](https://mlsanity.com/paper/2004.00277/) (2020)
- [Triplet-Based Deep Hashing Network for Cross-Modal Retrieval](https://mlsanity.com/paper/1904.02449/) (2019)
- [Learning to Answer Questions From Image Using Convolutional Neural Network](https://mlsanity.com/paper/1506.00333/) (2015)
- [Image-Question-Answer Synergistic Network for Visual Dialog](https://mlsanity.com/paper/1902.09774/) (2019)
- [Cross-modal Embeddings for Video and Audio Retrieval](https://mlsanity.com/paper/1801.02200/) (2018)
- [Noise-Tolerant Learning for Audio-Visual Action Recognition](https://mlsanity.com/paper/2205.07611/) (2022)

## Built on by (most cited first, 26 of 26)

- [Noisy-Correspondence Learning for Text-to-Image Person Re-identification](https://mlsanity.com/paper/2308.09911/) (2023)
- [Multi-granularity Correspondence Learning from Long-term Noisy Videos](https://mlsanity.com/paper/2401.16702/) (2024)
- [INTENT: Invariance and Discrimination-aware Noise Mitigation for Robust Composed Image Retrieval](https://mlsanity.com/paper/2604.18051/) (2026)
- [Cross-modal Active Complementary Learning with Self-refining Correspondence](https://mlsanity.com/paper/2310.17468/) (2023)
- [The Synergy between Data and Multi-Modal Large Language Models: A Survey from Co-Development Perspective](https://mlsanity.com/paper/2407.08583/) (2024)
- [Learning to Rematch Mismatched Pairs for Robust Cross-Modal Retrieval](https://mlsanity.com/paper/2403.05105/) (2024)
- [Mitigating Noisy Correspondence by Geometrical Structure Consistency Learning](https://mlsanity.com/paper/2405.16996/) (2024)
- [Negative Pre-aware for Noisy Cross-modal Matching](https://mlsanity.com/paper/2312.05777/) (2023)
- [Noisy Pair Corrector for Dense Retrieval](https://mlsanity.com/paper/2311.03798/) (2023)
- [Noisy Correspondence Learning with Self-Reinforcing Errors Mitigation](https://mlsanity.com/paper/2312.16478/) (2023)
- [Semi-supervised Text-based Person Search](https://mlsanity.com/paper/2404.18106/) (2024)
- [Disentangled Noisy Correspondence Learning](https://mlsanity.com/paper/2408.05503/) (2024)
- [CAPro: Webly Supervised Learning with Cross-Modality Aligned Prototypes](https://mlsanity.com/paper/2310.09761/) (2023)
- [PC2: Pseudo-Classification Based Pseudo-Captioning for Noisy Correspondence Learning in Cross-Modal Retrieval](https://mlsanity.com/paper/2408.01349/) (2024)
- [ReCon: Enhancing True Correspondence Discrimination through Relation Consistency for Robust Noisy Correspondence Learning](https://mlsanity.com/paper/2502.19962/) (2025)
- [AMNS: Attention-Weighted Selective Mask and Noise Label Suppression for Text-to-Image Person Retrieval](https://mlsanity.com/paper/2409.06385/) (2024)
- [TSVC:Tripartite Learning with Semantic Variation Consistency for Robust Image-Text Retrieval](https://mlsanity.com/paper/2501.10935/) (2025)
- [Multi-Modal Dataset Distillation in the Wild](https://mlsanity.com/paper/2506.01586/) (2025)
- [PCSR: Pseudo-label Consistency-Guided Sample Refinement for Noisy Correspondence Learning](https://mlsanity.com/paper/2509.15623/) (2025)
- [REPAIR: Rank Correlation and Noisy Pair Half-replacing with Memory for Noisy Correspondence](https://mlsanity.com/paper/2403.08224/) (2024)
- [Robust Noisy Correspondence Learning via Self-Drop and Dual-Weight](https://mlsanity.com/paper/2412.06172/) (2024)
- [CRoF: CLIP-based Robust Few-shot Learning on Noisy Labels](https://mlsanity.com/paper/2412.12793/) (2024)
- [Set a Thief to Catch a Thief: Combating Label Noise through Noisy Meta Learning](https://mlsanity.com/paper/2502.16104/) (2025)
- [PAUL: Uncertainty-Guided Partition and Augmentation for Robust Cross-View Geo-Localization under Noisy Correspondence](https://mlsanity.com/paper/2508.20066/) (2025)
- [Intra-Modal Neighbors Never Lie: Rectifying Inter-Modal Noisy Correspondence via Graph-Based Intra-Modal Reasoning](https://mlsanity.com/paper/2606.04061/) (2026)
- [Generative Retrieval for Unsupervised Text-Based Person Search](https://mlsanity.com/paper/2609.12965/) (2026)

## Abstract (arXiv)

Despite the success of multimodal learning in cross-modal retrieval task, the remarkable progress relies on the correct correspondence among multimedia data. However, collecting such ideal data is expensive and time-consuming. In practice, most widely used datasets are harvested from the Internet and inevitably contain mismatched pairs. Training on such noisy correspondence datasets causes performance degradation because the cross-modal retrieval methods can wrongly enforce the mismatched data to be similar. To tackle this problem, we propose a Meta Similarity Correction Network (MSCN) to provide reliable similarity scores. We view a binary classification task as the meta-process that encourages the MSCN to learn discrimination from positive and negative meta-data. To further alleviate the influence of noise, we design an effective data purification strategy using meta-data as prior knowledge to remove the noisy samples. Extensive experiments are conducted to demonstrate the strengths of our method in both synthetic and real-world noises, including Flickr30K, MS-COCO, and Conceptual Captions.