Multi-modal large language models (LLMs) have shown remarkable performance in various
natural language processing tasks, including data extraction from documents. However, the …

Detecting out-of-distribution (OOD) samples is crucial for ensuring the safety of machine
learning systems and has shaped the field of OOD detection. Meanwhile, several other …

We present a unified transformer, ie, Show-o, that unifies multimodal understanding and
generation. Unlike fully autoregressive models, Show-o unifies autoregressive and …

Medical imaging datasets for research are frequently collected from multiple imaging centers
using different scanners, protocols, and settings. These variations affect data consistency …

Despite the recent advancements in Multi-modal Large Language Models (MLLMs),
understanding inter-object relations, ie, interactions or associations between distinct objects …

Hallucination issues persistently plagued current multimodal large language models
(MLLMs). While existing research primarily focuses on object-level or attribute-level …

Large Vision Language Models (LVLMs) are increasingly integral to healthcare applications,
including medical visual question answering and imaging report generation. While these …

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

Instruction tuning in multimodal large language models (MLLMs) aims to smoothly integrate
a backbone LLM with a pre-trained feature encoder for downstream tasks. The major …

Despite the rapid progress and outstanding performance of Large Vision-Language Models
(LVLMs) in recent years, LVLMs have been plagued by the issue of hallucination, ie, LVLMs …