Temporal sentence grounding in videos (TSGV), aka, natural language video localization
(NLVL) or video moment retrieval (VMR), aims to retrieve a temporal moment that …

Recent DETR-based video grounding models have made the model directly predict moment
timestamps without any hand-crafted components, such as a pre-defined proposal or non …

Given an untrimmed video, temporal sentence grounding (TSG) aims to locate a target
moment semantically according to a sentence query. Although previous respectable works …

The correlation between the vision and text is essential for video moment retrieval (VMR),
however, existing methods heavily rely on separate pre-training feature extractors for visual …

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

Given an untrimmed video and a sentence query, video moment retrieval using language
(VMR) aims to locate a target query-relevant moment. Since the untrimmed video is …

Video-text Large Language Models (video-text LLMs) have shown remarkable performance
in answering questions and holding conversations on simple videos. However, they perform …

We propose a novel and challenging benchmark, AutoEval-Video, to comprehensively
evaluate large vision-language models in open-ended video question answering. The …

Almost all previous text-to-video retrieval works assume that videos are pre-trimmed with
short durations. However, in practice, videos are generally untrimmed containing much …

Given a descriptive language query, Video Moment Retrieval (VMR) aims to seek the
corresponding semantic-consistent moment clip in the video, which is represented as a pair …