Search:
Match:
2 results
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.
Reference

“NIRNL achieves state-of-the-art performance, exhibiting remarkable robustness, especially under high noise rates.”

PermalinkArXiv
Research Paper#Vision Transformers, Token Reduction, Computer Vision🔬 ResearchAnalyzed: Jan 3, 2026 16:21

Neighbor-Aware Token Reduction for Efficient Vision Transformers

Published:Dec 28, 2025 03:25
1 min read
ArXiv

Analysis

This paper addresses the computational inefficiency of Vision Transformers (ViTs) due to redundant token representations. It proposes a novel approach using Hilbert curve reordering to preserve spatial continuity and neighbor relationships, which are often overlooked by existing token reduction methods. The introduction of Neighbor-Aware Pruning (NAP) and Merging by Adjacent Token similarity (MAT) are key contributions, leading to improved accuracy-efficiency trade-offs. The work emphasizes the importance of spatial context in ViT optimization.
Reference

“The paper proposes novel neighbor-aware token reduction methods based on Hilbert curve reordering, which explicitly preserves the neighbor structure in a 2D space using 1D sequential representations.”

PermalinkArXiv