Exploiting Temporal Relationships in Video Moment Localization with Natural Language
This work addresses video moment localization for applications like video retrieval, but it is incremental as it builds on prior methods by incorporating temporal reasoning.
The paper tackles video moment localization by proposing a Temporal Compositional Modular Network (TCMN) that decomposes sentences into events and temporal signals, achieving state-of-the-art performance on the TEMPO dataset.
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.