Vision systems that see and reason about the compositional nature of visual scenes are
fundamental to understanding our world. The complex relations between objects and their …

Despite significant strides in multimodal tasks, Multimodal Large Language Models (MLLMs)
are plagued by the critical issue of hallucination. The reliable detection of such …

The rapid advancement of foundation models (FMs) across language, image, audio, and
video domains has shown remarkable capabilities in diverse tasks. However, the …

This survey presents a comprehensive analysis of the phenomenon of hallucination in
multimodal large language models (MLLMs), also known as Large Vision-Language Models …

Learning from feedback reduces the hallucination of multimodal large language models
(MLLMs) by aligning them with human preferences. While traditional methods rely on labor …

Multimodal Entity Linking (MEL) is a crucial task that aims at linking ambiguous mentions
within multimodal contexts to the referent entities in a multimodal knowledge base, such as …

Recent research suggests that the failures of Vision-Language Models (VLMs) at visual
reasoning often stem from erroneous agreements--when semantically distinct images are …

Abstract Large Vision-Language Models (LVLMs) may produce outputs that are unfaithful to
reality, also known as visual hallucinations (VH), which hinders their application in …

Despite their remarkable progress, Multimodal Large Language Models (MLLMs) tend to
hallucinate factually inaccurate information. In this work, we address object hallucinations in …

The recent advancements in large language models (LLMs) and pre-trained vision models
have accelerated the development of vision-language large models (VLLMs), enhancing the …