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 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 …

Long-form video understanding represents a significant challenge within computer vision,
demanding a model capable of reasoning over long multi-modal sequences. Motivated by …

We explore how reconciling several foundation models (large language models and vision-
language models) with a novel unified memory mechanism could tackle the challenging …

With the burgeoning growth of online video platforms and the escalating volume of video
content, the demand for proficient video understanding tools has intensified markedly. Given …

We propose SlowFast-LLaVA (or SF-LLaVA for short), a training-free video large language
model (LLM) that can jointly capture detailed spatial semantics and long-range temporal …

Current datasets for long-form video understanding often fall short of providing genuine long-
form comprehension challenges, as many tasks derived from these datasets can be …

We present HourVideo, a benchmark dataset for hour-long video-language understanding.
Our dataset consists of a novel task suite comprising summarization, perception (recall …

What makes good representations for video understanding, such as anticipating future
activities, or answering video-conditioned questions? While earlier approaches focus on …

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 …