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