Mitigating Object Hallucinations in Vision-Language Models through Region-Aware Attention Recalibration
A new paper addresses the issue of object hallucination in Large Vision-Language Models (LVLMs). The authors propose a training-free inference strategy that recalibrates attention mechanisms to improve visual-semantic alignment. Their method shows significant improvements in reducing hallucinations while maintaining generative fluency.
- ▪Object hallucination is a persistent challenge in Large Vision-Language Models.
- ▪The proposed method uses a region-aware adaptive weighting mechanism to correct semantic drift.
- ▪Comprehensive evaluations demonstrate that this strategy significantly reduces both instance- and sentence-level hallucinations.
Opening excerpt (first ~120 words) tap to expand
Computer Science > Artificial Intelligence arXiv:2605.24957 (cs) [Submitted on 24 May 2026] Title:Mitigating Object Hallucinations in Vision-Language Models through Region-Aware Attention Recalibration Authors:Yuanzhi Xu, Qian Gao, Jun Fan, Guohui Ding, Zhenyu Yang, Sixue Lin, Yuteng Xiao View a PDF of the paper titled Mitigating Object Hallucinations in Vision-Language Models through Region-Aware Attention Recalibration, by Yuanzhi Xu and 5 other authors View PDF HTML (experimental) Abstract:The generation of factually incorrect objects, commonly known as object hallucination, remains a persistent challenge in Large Vision-Language Models (LVLMs).
…
Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
Discussion
0 commentsMore from arXiv cs.AI
