Intra-Modal Neighbors Never Lie: Rectifying Inter-Modal Noisy Correspondence via Graph-Based Intra-Modal Reasoning
This paper proposes IN2R, a novel framework that shifts from discrete selection to synthesizing continuous soft prototypes to rectify noisy correspondence in cross-modal retrieval, leveraging the geometric stability of intra-modal data for graph reasoning, achieving state-of-the-art performance on multiple datasets.
[2606.04061] Intra-Modal Neighbors Never Lie: Rectifying Inter-Modal Noisy Correspondence via Graph-Based Intra-Modal Reasoning
[Submitted on 2 Jun 2026]
Title:Intra-Modal Neighbors Never Lie: Rectifying Inter-Modal Noisy Correspondence via Graph-Based Intra-Modal Reasoning
View a PDF of the paper titled Intra-Modal Neighbors Never Lie: Rectifying Inter-Modal Noisy Correspondence via Graph-Based Intra-Modal Reasoning, by Yang Liu and 4 other authors
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Abstract:Large-scale web-harvested datasets have fueled the progress of cross-modal retrieval but inevitably suffer from noisy correspondence, which severely degrades model generalization. Existing methods primarily address this by filtering out noise or seeking a substitute label, yet they predominantly remain bound by a "Discrete Selection" paradigm. We argue that relying on a single discrete proxy induces Single-Point Fragility and Discretization Error. To overcome these limitations, we propose a novel framework, Intra-modal Neighbor-aware Noise Rectification (IN2R), which shifts the paradigm from searching for a substitute to synthesizing a reliable supervision target. Leveraging the intrinsic geometric stability of intra-modal data, IN2R employs a Graph Refiner to perform relational reasoning over neighbors retrieved from a dynamic Cross-Model Memory. Instead of propagating discrete labels, our method synthesizes a continuous, soft prototype that reflects the consensus of the local semantic neighborhood, effectively rectifying inter-modal misalignment. Extensive experiments on Flickr30K, MS-COCO, and CC152K demonstrate that IN2R significantly outperforms state-of-the-art methods. Our code and pre-trained models are publicly available at this https URL.
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2606.04061 [cs.CV]
(or arXiv:2606.04061v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2606.04061
arXiv-issued DOI via DataCite (pending registration)
Journal reference: International Conference of Machine Learning 2026
Submission history
From: Yang Liu [view email] [v1] Tue, 2 Jun 2026 12:26:28 UTC (1,014 KB)
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