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 …

This work proposes TimeChat a time-sensitive multimodal large language model specifically
designed for long video understanding. Our model incorporates two key architectural …

With the success of large language models (LLMs) integrating the vision model into LLMs to
build vision-language foundation models has gained much more interest recently. However …

In the quest for artificial general intelligence, Multi-modal Large Language Models (MLLMs)
have emerged as a focal point in recent advancements. However, the predominant focus …

In this paper, we present the VideoLLaMA 2, a set of Video Large Language Models (Video-
LLMs) designed to enhance spatial-temporal modeling and audio understanding in video …

Temporal data, notably time series and spatio-temporal data, are prevalent in real-world
applications. They capture dynamic system measurements and are produced in vast …

An ideal model for dense video captioning--predicting captions localized temporally in a
video--should be able to handle long input videos predict rich detailed textual descriptions …

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 …

Vision-language pre-training has significantly elevated performance across a wide range of
image-language applications. Yet, the pre-training process for video-related tasks demands …

This paper presents VideoStreaming, an advanced vision-language large model (VLLM) for
video understanding, that capably understands arbitrary-length video with a constant …