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 …

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

Abstract Video Moment Retrieval (MR) and Highlight Detection (HD) have attracted
significant attention due to the growing demand for video analysis. Recent approaches treat …

This paper addresses the challenging task of weakly-supervised video temporal grounding.
Existing approaches are generally based on the moment proposal selection framework that …

The ability of large language models (LLMs) to process visual inputs has given rise to
general-purpose vision systems unifying various vision-language (VL) tasks by instruction …

The core of video understanding tasks such as recognition captioning and tracking is to
automatically detect objects or actions in a video and analyze their temporal evolution …

Understanding videos is one of the fundamental directions in computer vision research, with
extensive efforts dedicated to exploring various architectures such as RNN, 3D CNN, and …

Video temporal grounding (VTG) is a fine-grained video understanding problem that aims to
ground relevant clips in untrimmed videos given natural language queries. Most existing …

Temporal Grounding is to identify specific moments or highlights from a video corresponding
to textual descriptions. Typical approaches in temporal grounding treat all video clips …

Temporal video grounding (TVG) is a critical task in video content understanding, requiring
precise alignment between video content and natural language instructions. Despite …