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Detecting meaningful events in an untrimmed video is essential for densevideo captioning. In this work, we propose a novel and simple model for eventsequence generation and explore temporal relationships of the event sequence inthe video. The proposed model omits inefficient two-stage proposal generationand directly generates event boundaries conditioned on bi-directional temporaldependency in one pass. Experimental results show that the proposed eventsequence generation model can generate more accurate and diverse events withina small number of proposals. For the event captioning, we follow our previouswork to employ the intra-event captioning models into our pipeline system. Theoverall system achieves state-of-the-art performance on the dense-captioningevents in video task with 9.894 METEOR score on the challenge testing set.

Team RUC_AIM3 Technical Report at Activitynet 2020 Task 2: Exploring Sequential Events Detection for Dense Video Captioning