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

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 …

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 …

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

Detecting customized moments and highlights from videos given natural language (NL) user
queries is an important but under-studied topic. One of the challenges in pursuing this …

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 …

We address the problem of retrieving a specific moment from an untrimmed video by a query
sentence. This is a challenging problem because a target moment may take place in …

We present a new large-scale multilingual video description dataset, VATEX, which contains
over 41,250 videos and 825,000 captions in both English and Chinese. Among the captions …

Given an untrimmed video and a text query, natural language video localization (NLVL) is to
locate a matching span from the video that semantically corresponds to the query. Existing …