3 papers
cs.CV2025
Two Causes, Not One: Rethinking Omission and Fabrication Hallucinations in MLLMs
Guangzong Si, Hao Yin, Xianfei Li +4
Multimodal Large Language Models (MLLMs) have achieved impressive advances, yet object hallucination remains a persistent challenge. Existing methods, based on the flawed assumptio…
cs.CV2025
Lifting the Veil on Visual Information Flow in MLLMs: Unlocking Pathways to Faster Inference
Hao Yin, Guangzong Si, Zilei Wang
Multimodal large language models (MLLMs) improve performance on vision-language tasks by integrating visual features from pre-trained vision encoders into large language models (LL…
cs.CV2025
ClearSight: Visual Signal Enhancement for Object Hallucination Mitigation in Multimodal Large language Models
Hao Yin, Guangzong Si, Zilei Wang
Contrastive decoding strategies are widely used to mitigate object hallucinations in multimodal large language models (MLLMs). By reducing over-reliance on language priors, these s…