  We propose weakly supervised language localization networks (WSLLN) to detect
events in long, untrimmed videos given language queries. To learn the
correspondence between visual segments and texts, most previous methods require
temporal coordinates (start and end times) of events for training, which leads
to high costs of annotation. WSLLN relieves the annotation burden by training
with only video-sentence pairs without accessing to temporal locations of
events. With a simple end-to-end structure, WSLLN measures segment-text
consistency and conducts segment selection (conditioned on the text)
simultaneously. Results from both are merged and optimized as a video-sentence
matching problem. Experiments on ActivityNet Captions and DiDeMo demonstrate
that WSLLN achieves state-of-the-art performance.
