World models are progressively being employed across diverse fields, extending from basic
environment simulation to complex scenario construction. However, existing models are …

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 …

With the rise of multimodal applications, instruction data has become critical for training
multimodal language models capable of understanding complex image-based queries …

Medical Vision-Language Pre-training (MedVLP) has made significant progress in enabling
zero-shot tasks for medical image understanding. However, training MedVLP models …

The increasing demand for high-quality, diverse training data poses a significant bottleneck
in advancing vision-language models (VLMs). This paper presents VLM Dialog Games, a …

In the past five years, research has shifted from traditional Machine Learning (ML) and Deep
Learning (DL) approaches to leveraging Large Language Models (LLMs), including …

Cross-modal visual information has been demonstrated to enhance the performance of
unimodal text tasks. However, efficiently acquiring and utilizing this cross-modal visual …

With the success of large language models (LLMs) driving progress towards general-
purpose AI, there has been a growing focus on extending these models to multi-modal …