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 …

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

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 …

Existing visual question answering methods often suffer from cross-modal spurious
correlations and oversimplified event-level reasoning processes that fail to capture event …

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 …

Segmenting untrimmed videos into chapters enables users to quickly navigate to the
information of their interest. This important topic has been understudied due to the lack of …

We tackle the task of video moment retrieval (VMR), which aims to localize a specific
moment in a video according to a textual query. Existing methods primarily model the …

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 …

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 …