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 …

Abstract Video-Language Pretraining (VLP), which aims to learn transferable representation
to advance a wide range of video-text downstream tasks, has recently received increasing …

What makes a video task uniquely suited for videos, beyond what can be understood from a
single image? Building on recent progress in self-supervised image-language models, we …

Video-language pre-training (VLP) has become increasingly important due to its ability to
generalize to various vision and language tasks. However, existing egocentric VLP …

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 dominant paradigm for learning video-text representations--noise contrastive learning--
increases the similarity of the representations of pairs of samples that are known to be …

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 …

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 …

We consider the problem of localizing a spatio-temporal tube in a video corresponding to a
given text query. This is a challenging task that requires the joint and efficient modeling of …

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 …