Current universal segmentation methods demonstrate strong capabilities in pixel-level
image and video understanding. However, they lack reasoning abilities and cannot be …

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

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

Recent advances in Video Large Language Models (Video-LLMs) have demonstrated their
great potential in general-purpose video understanding. To verify the significance of these …

Multi-modal generative AI has received increasing attention in both academia and industry.
Particularly, two dominant families of techniques are: i) The multi-modal large language …

Multimodal Large Language Models (MLLMs) have gained significant attention due to their
impressive capabilities in multimodal understanding. However, existing methods rely heavily …

We present VisionLLM v2, an end-to-end generalist multimodal large model (MLLM) that
unifies visual perception, understanding, and generation within a single framework. Unlike …

Advances in CLIP and large multimodal models (LMMs) have enabled open-vocabulary and
free-text segmentation, yet existing models still require predefined category prompts, limiting …

Visual instruction tuning (VIT) enables multimodal large language models (MLLMs) to
effectively handle a wide range of vision tasks by framing them as language-based …

Multimodal Large Language Models (MLLMs) have gained significant attention due to their
impressive capabilities in multimodal understanding. However, existing methods rely heavily …