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arXiv · 1806.08854

RUC+CMU: System Report for Dense Captioning Events in Videos

Shizhe Chen·Yuqing Song·Yida Zhao·Jiarong Qiu·Qin Jin·Alexander Hauptmann
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Abstract

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.

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Shizhe Chen, Yuqing Song, Yida Zhao, Jiarong Qiu, Qin Jin, Alexander Hauptmann. 2018-06-22. RUC+CMU: System Report for Dense Captioning Events in Videos. https://arxiv.org/abs/1806.08854

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