Conversational AI has seen a growing interest among government, researchers, and
industrialists. This comprehensive survey paper provides an in-depth analysis of large …

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

Cross-modal retrieval has recently attracted growing attention, which aims to match
instances captured from different modalities. The performance of cross-modal retrieval …

Almost all previous text-to-video retrieval works assume that videos are pre-trimmed with
short durations. However, in practice, videos are generally untrimmed containing much …

This paper aims for the task of text-to-video retrieval, where given a query in the form of a
natural-language sentence, it is asked to retrieve videos which are semantically relevant to …

Given a collection of untrimmed and unsegmented videos, video corpus moment retrieval
(VCMR) is to retrieve a temporal moment (ie, a fraction of a video) that semantically …

Current methods for text-to-video retrieval (T2VR) are trained and tested on video-captioning
oriented datasets such as MSVD, MSR-VTT and VATEX. A key property of these datasets is …

Video-text retrieval is an important yet challenging task in vision-language understanding,
which aims to learn a joint embedding space where related video and text instances are …

In this paper we revisit feature fusion, an old-fashioned topic, in the new context of text-to-
video retrieval. Different from previous research that considers feature fusion only at one …

We present a novel network to transfer the image-language pre-trained model to video-text
retrieval in an end-to-end manner. Leading approaches in the domain of video-and …