arXiv:2609.37263v1 Announce Type: new
Abstract: While Large Vision-Language Models (LVLMs) achieve remarkable success, hallucinations remain a significant barrier to their reliable deployment. Recent...
By Siqi Lu, Suo Wei, Yongbin Zheng, Jianhang Yao, Wanying Xu, Peng Wang
arXiv:2603. 24058v2 Announce Type: replace-cross Abstract: Object hallucination in Large Vision-Language Models (LVLMs) severely compromises their reliability in real-world applications, posing a critical barrier to their deployment in high-stakes scenarios such as autonomous driving and medical image analysis.
By Han Sun, Qin Li, Peixin Wang, Min Zhang
While Large Vision-Language Models (LVLMs) achieve remarkable success, hallucinations remain a significant barrier to their reliable deployment. Recent studies primarily attribute these issues to cros...
arXiv:2608. 07302v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) often suffer from object hallucination, generating objects that are absent from the image.
By Zichuan Wang, Songlin Yang, Bo Peng, Zhenchen Tang, Yang Li, Beibei Dong, Jing Dong
The paper introduces Segmentation-Based Attention Entropy (SAE), a method that uses semantic segmentation to measure visual attention uncertainty in large vision‑language models (LVLMs). SAE provides a reliability score for detecting hallucinated objects and an attention‑adjustment technique that reduces hallucinations during inference. Experiments on public benchmarks and real quadruped robot scenarios demonstrate that SAE improves LVLM reliability without requiring additional training.
By Jiale Song, Jiaxin Luo, Xue-song Tang, Kuangrong Hao, Mingbo Zhao
arXiv:2606. 29431v1 Announce Type: new Abstract: Despite the impressive capabilities of Large Vision-Language Models (LVLMs), they remain susceptible to hallucination, generating content inconsistent with the input image.
By Yichen Guo, Kai Tang, Fenglai Lin, Yiding Sun, Dongshuo Zhang, Wenya Wang, Lin William Cong, Shanghang Zhang