Video moment localization, also known as video moment retrieval, aims to search a target
segment within a video described by a given natural language query. Beyond the task of …

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 …

Recently, video moment retrieval and highlight detection (MR/HD) are being spotlighted as
the demand for video understanding is drastically increased. The key objective of MR/HD is …

While large-scale image-text pretrained models such as CLIP have been used for multiple
video-level tasks on trimmed videos, their use for temporal localization in untrimmed videos …

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

We target at the task of weakly-supervised action localization (WSAL), where only video-
level action labels are available during model training. Despite the recent progress, existing …

Temporal grounding aims to localize a video moment which is semantically aligned with a
given natural language query. Existing methods typically apply a detection or regression …

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 recent video grounding works attempt to introduce vanilla contrastive learning into video
grounding. However, we claim that this naive solution is suboptimal. Contrastive learning …

Finding relevant moments and highlights in videos according to natural language queries is
a natural and highly valuable common need in the current video content explosion era …