  We address the problem of video moment localization with natural language,
i.e. localizing a video segment described by a natural language sentence. While
most prior work focuses on grounding the query as a whole, temporal
dependencies and reasoning between events within the text are not fully
considered. In this paper, we propose a novel Temporal Compositional Modular
Network (TCMN) where a tree attention network first automatically decomposes a
sentence into three descriptions with respect to the main event, context event
and temporal signal. Two modules are then utilized to measure the visual
similarity and location similarity between each segment and the decomposed
descriptions. Moreover, since the main event and context event may rely on
different modalities (RGB or optical flow), we use late fusion to form an
ensemble of four models, where each model is independently trained by one
combination of the visual input. Experiments show that our model outperforms
the state-of-the-art methods on the TEMPO dataset.
