  This notebook paper presents our system in the ActivityNet Dense Captioning
in Video task (task 3). Temporal proposal generation and caption generation are
both important to the dense captioning task. Therefore, we propose a proposal
ranking model to employ a set of effective feature representations for proposal
generation, and ensemble a series of caption models enhanced with context
information to generate captions robustly on predicted proposals. Our approach
achieves the state-of-the-art performance on the dense video captioning task
with 8.529 METEOR score on the challenge testing set.
