  Dense event captioning aims to detect and describe all events of interest
contained in a video. Despite the advanced development in this area, existing
methods tackle this task by making use of dense temporal annotations, which is
dramatically source-consuming. This paper formulates a new problem: weakly
supervised dense event captioning, which does not require temporal segment
annotations for model training. Our solution is based on the one-to-one
correspondence assumption, each caption describes one temporal segment, and
each temporal segment has one caption, which holds in current benchmark
datasets and most real-world cases. We decompose the problem into a pair of
dual problems: event captioning and sentence localization and present a cycle
system to train our model. Extensive experimental results are provided to
demonstrate the ability of our model on both dense event captioning and
sentence localization in videos.
