For a long time, researchers have sought artificial intelligence (AI) that matches or exceeds
human intelligence. AI agents, which are artificial entities capable of sensing the …

Large Language Models (LLMs) have recently demonstrated remarkable capabilities in
natural language processing tasks and beyond. This success of LLMs has led to a large …

Large vision-language models (VLMs) have recently achieved remarkable progress,
exhibiting impressive multimodal perception and reasoning abilities. However, effectively …

We present LLaVA-OneVision, a family of open large multimodal models (LMMs) developed
by consolidating our insights into data, models, and visual representations in the LLaVA …

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 …

In this work, we present a novel method to tackle the token generation challenge in Vision
Language Models (VLMs) for video and image understanding, called LLaMA-VID. Current …

We introduce Cambrian-1, a family of multimodal LLMs (MLLMs) designed with a vision-
centric approach. While stronger language models can enhance multimodal capabilities, the …

Large language models (LLMs) have become increasingly capable, but their development
often requires substantial computational resources. Although model merging has emerged …

Multimodal machine learning is a vibrant multi-disciplinary research field that aims to design
computer agents with intelligent capabilities such as understanding, reasoning, and learning …

Visual encoding constitutes the basis of large multimodal models (LMMs) in understanding
the visual world. Conventional LMMs process images in fixed sizes and limited resolutions …