In this paper, we introduce InternVL 1.5, an open-source multimodal large language model
(MLLM) to bridge the capability gap between open-source and proprietary commercial …

As a prominent direction of Artificial General Intelligence (AGI), Multimodal Large Language
Models (MLLMs) have garnered increased attention from both industry and academia …

We introduce InternVL 2.5, an advanced multimodal large language model (MLLM) series
that builds upon InternVL 2.0, maintaining its core model architecture while introducing …

Multi-modal large language models (MLLMs) have demonstrated impressive performance in
vision-language tasks across a wide range of domains. However, the large model scale and …

Visual preference alignment involves training Large Vision-Language Models (LVLMs) to
predict human preferences between visual inputs. This is typically achieved by using …

In recent years, vision-language models have made significant strides, excelling in tasks like
optical character recognition and geometric problem-solving. However, several critical …

Large models have recently played a dominant role in natural language processing and
multimodal vision-language learning. However, their effectiveness in text-related visual …

Recent advancements in multi-modal large language models have propelled the
development of joint probabilistic models capable of both image understanding and …

Video Large Language Models (Vid-LLMs) have made remarkable advancements in
comprehending video content for QA dialogue. However, they struggle to extend this visual …

In rapidly evolving field of vision-language models (VLMs), contrastive language-image pre-
training (CLIP) has made significant strides, becoming foundation for various downstream …