In this paper, we address a novel task, namely weakly-supervised spatio-temporally grounding natural sentence in video.
given a natural sentence and a video, we localize a spatio-temporal tube in the video that semantically corresponds to the given sentence, with no reliance on any spatio-temporal annotations during training.