Recent development of Large Vision-Language Models (LVLMs) has attracted growing
attention within the AI landscape for its practical implementation potential. However,`` …

With the deepening of research on Large Language Models (LLMs), significant progress has
been made in recent years on the development of Large Multimodal Models (LMMs), which …

We introduce MMMU: a new benchmark designed to evaluate multimodal models on
massive multi-discipline tasks demanding college-level subject knowledge and deliberate …

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 …

Abstract We present Visual AutoRegressive modeling (VAR), a new generation paradigm
that redefines the autoregressive learning on images as coarse-to-fine" next-scale …

Abstract We present the ShareGPT4Video series, aiming to facilitate the video
understanding of large video-language models (LVLMs) and the video generation of text-to …

We introduce InternVideo2, a new family of video foundation models (ViFM) that achieve the
state-of-the-art results in video recognition, video-text tasks, and video-centric dialogue. Our …

Large vision-language models (LVLMs) have recently achieved rapid progress, sparking
numerous studies to evaluate their multi-modal capabilities. However, we dig into current …

Recent advances demonstrate that scaling Large Vision-Language Models (LVLMs)
effectively improves downstream task performances. However, existing scaling methods …

PaliGemma is an open Vision-Language Model (VLM) that is based on the SigLIP-So400m
vision encoder and the Gemma-2B language model. It is trained to be a versatile and …