Long-context capability is critical for multi-modal foundation models, especially for long
video understanding. We introduce LongVILA, a full-stack solution for long-context visual …

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 …

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

Despite the rapid integration of video perception capabilities into Large Multimodal Models
(LMMs), the underlying mechanisms driving their video understanding remain poorly …

Rapid development of large language models (LLMs) has significantly advanced multimodal
large language models (LMMs), particularly in vision-language tasks. However, existing …

We present xGen-MM-Vid (BLIP-3-Video): a multimodal language model for videos,
particularly designed to efficiently capture temporal information over multiple frames. BLIP-3 …

Creating AI systems that can interact with environments over long periods, similar to human
cognition, has been a longstanding research goal. Recent advancements in multimodal …

Recently, multimodal large language models (MLLMs), such as GPT-4o, Gemini 1.5 Pro,
and Reka Core, have expanded their capabilities to include vision and audio modalities …

Despite the recent breakthroughs achieved by Large Vision Language Models (LVLMs) in
understanding and responding to complex visual-textual contexts, their inherent …

Large language models (LLMs) have revolutionized video-based computer vision
applications, including action recognition, anomaly detection, and video summarization …