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.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.24957 |
| Publication time | Tue, 26 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-26T04:07:43.013Z |
| Last seen | 2026-05-26T04:07:43.013Z |
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Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
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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).
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