  The application of video captioning models aims at translating the content of
videos by using accurate natural language. Due to the complex nature inbetween
object interaction in the video, the comprehensive understanding of
spatio-temporal relations of objects remains a challenging task. Existing
methods often fail in generating sufficient feature representations of video
content. In this paper, we propose a video captioning model based on dual
graphs and gated fusion: we adapt two types of graphs to generate feature
representations of video content and utilize gated fusion to further understand
these different levels of information. Using a dual-graphs model to generate
appearance features and motion features respectively can utilize the content
correlation in frames to generate various features from multiple perspectives.
Among them, dual-graphs reasoning can enhance the content correlation in frame
sequences to generate advanced semantic features; The gated fusion, on the
other hand, aggregates the information in multiple feature representations for
comprehensive video content understanding. The experiments conducted on worldly
used datasets MSVD and MSR-VTT demonstrate state-of-the-art performance of our
proposed approach.
