Video moment retrieval pursues an efficient and generalized solution to identify the specific
temporal segments within an untrimmed video that correspond to a given language …

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

The recent and increasing interest in video-language research has driven the development
of large-scale datasets that enable data-intensive machine learning techniques. In …

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 …

Video Grounding (VG) aims to locate the desired segment from a video given a sentence
query. Recent studies have found that current VG models are prone to over-rely the …

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

Temporal sentence grounding in video has witnessed significant advancements, but suffers
from substantial dataset bias, which undermines its generalization ability. Existing debias …

Video moment retrieval (VMR) aims to identify the specific moment in an untrimmed video for
a given natural language query. However, this task is prone to suffer the weak visual-textual …

Given a text description, Temporal Language Grounding (TLG) aims to localize temporal
boundaries of the segments that contain the specified semantics in an untrimmed video. TLG …

This paper addresses the temporal sentence grounding (TSG). Although existing methods
have made decent achievements in this task, they not only severely rely on abundant video …