TALL: Temporal Activity Localization via Language Query

Jiyang Gao, Chen Sun, Zhenheng Yang, Ram Nevatia; Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017, pp. 5267-5275

This paper focuses on temporal localization of actions from untrimmed videos. Existing methods typically involve training classifiers for a pre-defined list of actions and applying the classifiers in a sliding window fashion. However, activities in the wild consist of a wide combination of actors, actions and objects; it is difficult to design a proper activity list that meets users' needs. We propose to localize activities by natural language queries. Temporal Activity Localization via Language (TALL) is challenging as it requires: (1) suitable design of text and video representations to allow cross-modal matching of actions and language queries; (2) ability to locate actions accurately given features from sliding windows of limited granularity. We propose a novel Cross-modal Temporal Regression Localizer (CTRL) to jointly model text query and video clips, output alignment scores and location regression results for candidate clips. For evaluation, we adopt TaCoS dataset, and build a new dataset for this task on top of Charades by adding sentence temporal annotations, called Charades-STA. Experimental results show that CTRL outperforms previous methods significantly on both datasets.