Research Paper#Cross-Modal Retrieval, Noisy Labels, Robust Learning🔬 ResearchAnalyzed: Jan 3, 2026 17:04
Neighbor-aware Instance Refining for Cross-Modal Retrieval with Noisy Labels
Published:Dec 30, 2025 08:19
•1 min read
•ArXiv
Analysis
This paper addresses the problem of noisy labels in cross-modal retrieval, a common issue in multi-modal data analysis. It proposes a novel framework, NIRNL, to improve retrieval performance by refining instances based on neighborhood consensus and tailored optimization strategies. The key contribution is the ability to handle noisy data effectively and achieve state-of-the-art results.
Key Takeaways
Reference
“NIRNL achieves state-of-the-art performance, exhibiting remarkable robustness, especially under high noise rates.”