  Given an untrimmed video and a language query depicting a specific temporal
moment in the video, video grounding aims to localize the time interval by
understanding the text and video simultaneously. One of the most challenging
issues is an extremely time- and cost-consuming annotation collection,
including video captions in a natural language form and their corresponding
temporal regions. In this paper, we present a simple yet novel training
framework for video grounding in the zero-shot setting, which learns a network
with only video data without any annotation. Inspired by the recent
language-free paradigm, i.e. training without language data, we train the
network without compelling the generation of fake (pseudo) text queries into a
natural language form. Specifically, we propose a method for learning a video
grounding model by selecting a temporal interval as a hypothetical correct
answer and considering the visual feature selected by our method in the
interval as a language feature, with the help of the well-aligned
visual-language space of CLIP. Extensive experiments demonstrate the prominence
of our language-free training framework, outperforming the existing zero-shot
video grounding method and even several weakly-supervised approaches with large
margins on two standard datasets.
