Knowledge Graphs (KGs) play a pivotal role in advancing various AI applications, with the
semantic web community's exploration into multi-modal dimensions unlocking new avenues …

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 …

With the rapid development of Multi-modal Large Language Models (MLLMs) a number of
diagnostic benchmarks have recently emerged to evaluate the comprehension capabilities …

We present InternLM-XComposer-2.5 (IXC-2.5), a versatile large-vision language model that
supports long-contextual input and output. IXC-2.5 excels in various text-image …

The multimodal task of Visual Question Answering (VQA) encompassing elements of
Computer Vision (CV) and Natural Language Processing (NLP), aims to generate answers …

Instruction tuning has significantly advanced large language models (LLMs) such as
ChatGPT, enabling them to align with human instructions across diverse tasks. However …

Pre-trained vision and language models have demonstrated state-of-the-art capabilities over
existing tasks involving images and texts, including visual question answering. However, it …

The Large Vision-Language Model (LVLM) field has seen significant advancements, yet its
progression has been hindered by challenges in comprehending fine-grained visual content …

While visual language model architectures and training infrastructures advance rapidly, data
curation remains under-explored where quantity and quality become a bottleneck. Existing …

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 …