Mitigating Object Hallucinations in Vision-Language Models through Region-Aware Attention Recalibration

ArXi:2605.24957v1 Announce Type: new The generation of factually incorrect objects, commonly known as object hallucination, remains a persistent challenge in Large Vision-Language Models (LVLMs). Current approaches to address this issue - ranging from expensive data-driven fine-tuning and high-latency contrastive decoding to rigid attention head truncation - frequently compromise either computational efficiency or the continuity of the model's feature space. To overcome these limitations, we.