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

Large language models (LLMs) have shown remarkable text understanding capabilities
which have been extended as Video LLMs to handle video data for comprehending visual …

Video captioning (VC) is a fast-moving, cross-disciplinary area of research that comprises
contributions from domains such as computer vision, natural language processing …

We present AIST++, a new multi-modal dataset of 3D dance motion and music, along with
FACT, a Full-Attention Cross-modal Transformer network for generating 3D dance motion …

Most state-of-the-art action localization systems process each action proposal individually,
without explicitly exploiting their relations during learning. However, the relations between …

Temporal sentence grounding aims to detect the most salient moment corresponding to the
natural language query from untrimmed videos. As labeling the temporal boundaries is labor …

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

Video captioning is a challenging task as it needs to accurately transform visual
understanding into natural language description. To date, state-of-the-art methods …

We have witnessed the tremendous growth of videos over the Internet, where most of these
videos are typically paired with abundant sentence descriptions, such as video titles …

Dense video captioning is a task of localizing interesting events from an untrimmed video
and producing textual description (captions) for each localized event. Most of the previous …