Misinformation such as fake news and rumors is a serious threat for information ecosystems
and public trust. The emergence of large language models (LLMs) has great potential to …

Humans can easily solve multimodal tasks in context with only a few demonstrations or
simple instructions which current multimodal systems largely struggle to imitate. In this work …

Abstract Large Multimodal Models (LMMs) have shown promise in vision-language tasks but
struggle with high-resolution input and detailed scene understanding. Addressing these …

The ability to accurately interpret complex visual information is a crucial topic of multimodal
large language models (MLLMs). Recent work indicates that enhanced visual perception …

Connecting text and visual modalities plays an essential role in generative intelligence. For
this reason, inspired by the success of large language models, significant research efforts …

Understanding long real-world videos requires modeling of long-range visual
dependencies. To this end we explore video-first architectures building on the common …

The recent advance in vision-language models is largely attributed to the abundance of
image-text data. We aim to replicate this success for video-language models but there …

The field of vision-language models (VLMs), which take images and texts as inputs and
output texts, is rapidly evolving and has yet to reach consensus on several key aspects of …

In semi-supervised semantic segmentation, a model is trained with a limited number of
labeled images along with a large corpus of unlabeled images to reduce the high annotation …

Leveraging vast training data multimodal large language models (MLLMs) have
demonstrated formidable general visual comprehension capabilities and achieved …