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 …

This paper proposes LLaFS the first attempt to leverage large language models (LLMs) in
few-shot segmentation. In contrast to the conventional few-shot segmentation methods that …

In this work, we propose a training-free method to inject visual prompts into Multimodal
Large Language Models (MLLMs) through learnable latent variable optimization. We …

Catastrophic forgetting emerges as a critical challenge when fine-tuning multi-modal large
language models (MLLMs), where improving performance on unseen tasks often leads to a …

Despite their great success across various multimodal tasks, Large Vision-Language
Models (LVLMs) are facing a prevalent problem with object hallucinations, where the …

The advent of large models, also known as foundation models, has significantly transformed
the AI research landscape, with models like Segment Anything (SAM) achieving notable …

Despite the remarkable ability of large vision-language models (LVLMs) in image
comprehension, these models frequently generate plausible yet factually incorrect …

In this paper, we address the challenge of Perspective-Invariant Learning in machine
learning and computer vision, which involves enabling a network to understand images from …

Recently, Multimodal Large Language Models (MLLMs) have made significant progress in
the video comprehension field. Despite remarkable content reasoning and instruction …