While next-token prediction is considered a promising path towards artificial general
intelligence, it has struggled to excel in multimodal tasks, which are still dominated by …

The development of video large multimodal models (LMMs) has been hindered by the
difficulty of curating large amounts of high-quality raw data from the web. To address this, we …

Large language models (LLMs) with vision capabilities (eg, GPT-4o, Gemini 1.5, and Claude
3) are powering countless image-text processing applications, enabling unprecedented …

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 …

In this paper, we introduce Janus, an autoregressive framework that unifies multimodal
understanding and generation. Prior research often relies on a single visual encoder for …

Recent progress in multimodal large language models has markedly enhanced the
understanding of short videos (typically under one minute), and several evaluation datasets …

Visual data comes in various forms, ranging from small icons of just a few pixels to long
videos spanning hours. Existing multi-modal LLMs usually standardize these diverse visual …

Multimodal Large Language Models (MLLMs) have shown promising progress in
understanding and analyzing video content. However, processing long videos remains a …

We introduce LLaVA-Critic, the first open-source large multimodal model (LMM) designed as
a generalist evaluator to assess performance across a wide range of multimodal tasks …

Expanding the long-context capabilities of Multi-modal Large Language Models~(MLLMs) is
crucial for video understanding, high-resolution image understanding, and multi-modal …