1 citations · 1 across the 4 of their papers we have counts for
4 papers
Finding the Correct Visual Evidence Without Forgetting: Mitigating Hallucination in LVLMs via Inter-Layer Visual Attention Discrepancy
Yutong Xie, Zhenglin Hua, Ran Wang +3
Large Vision-Language Models (LVLMs) have shown remarkable performance on a wide range of vision-language tasks. Despite this progress, they are still prone to hallucination, gener…
Rethinking Representativeness and Diversity in Dynamic Data Selection
Yuzhe Zhou, Zhenglin Hua, Haiyun Guo +1
Dynamic data selection accelerates training by sampling a changing subset of the dataset while preserving accuracy. We rethink two core notions underlying sample evaluation: repres…
Steering LVLMs via Sparse Autoencoder for Hallucination Mitigation
Zhenglin Hua, Jinghan He, Zijun Yao +4
Large vision-language models (LVLMs) have achieved remarkable performance on multimodal tasks. However, they still suffer from hallucinations, generating text inconsistent with vis…
Cracking the Code of Hallucination in LVLMs with Vision-aware Head Divergence
Jinghan He, Kuan Zhu, Haiyun Guo +6
Large vision-language models (LVLMs) have made substantial progress in integrating large language models (LLMs) with visual inputs, enabling advanced multimodal reasoning. Despite…