  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.
