Taking full advantage of the information from both vision and language is
critical for the video captioning task. Existing models lack adequate visual
representation due to the neglect of interaction between object, and sufficient
training for content-related words due to long-tailed problems. In this paper,
we propose a complete video captioning system including both a novel model and
an effective training strategy. Specifically, we propose an object relational
graph (ORG) based encoder, which captures more detailed interaction features to
enrich visual representation. Meanwhile, we design a teacher-recommended
learning (TRL) method to make full use of the successful external language
model (ELM) to integrate the abundant linguistic knowledge into the caption
model. The ELM generates more semantically similar word proposals which extend
the ground-truth words used for training to deal with the long-tailed problem.
Experimental evaluations on three benchmarks: MSVD, MSR-VTT and VATEX show the
proposed ORG-TRL system achieves state-of-the-art performance. Extensive
ablation studies and visualizations illustrate the effectiveness of our system.

本文提出了一个视频字幕生成系统，包括一种新颖的基于对象关系图的编码器和一种有效的训练策略，其中使用了教师推荐学习方法和外部语言模型来解决长尾问题，并在多个基准测试中取得最好的性能，实验证明我们的系统是有效的。