Despite the effectiveness of vision-language supervised fine-tuning in enhancing the
performance of vision large language models (VLLMs), existing visual instruction tuning …

Multimodal large language models (MLLMs) equip pre-trained large-language models
(LLMs) with visual capabilities. While textual prompting in LLMs has been widely studied …

Despite significant advancements in Multimodal Large Language Models (MLLMs) for
understanding complex human intentions through cross-modal interactions, capturing …

Current multimodal large language models (MLLMs) struggle with fine-grained or precise
understanding of visuals though they give comprehensive perception and reasoning in a …

Remote sensing (RS) visual grounding aims to use natural language expression to locate
specific objects (in the form of the bounding box or segmentation mask) in RS images …

Large visual-language models (LVLMs) have achieved great success in multiple
applications. However, they still encounter challenges in complex scenes, especially those …

The recent development in multimodal learning has greatly advanced the research in 3D
scene understanding in various real-world tasks such as embodied AI. However, most …

Multi-modal large language models (MLLMs) have achieved remarkable success in fine-
grained visual understanding across a range of tasks. However, they often encounter …

Driving World Models (DWMs) have become essential for autonomous driving by enabling
future scene prediction. However, existing DWMs are limited to scene generation and fail to …

Perception and understanding are two pillars of computer vision. While multimodal large
language models (MLLM) have demonstrated remarkable visual understanding capabilities …