  Automatically generating a natural language sentence to describe the content
of an input video is a very challenging problem. It is an essential multimodal
task in which auditory and visual contents are equally important. Although
audio information has been exploited to improve video captioning in previous
works, it is usually regarded as an additional feature fed into a black box
fusion machine. How are the words in the generated sentences associated with
the auditory and visual modalities? The problem is still not investigated. In
this paper, we make the first attempt to design an interpretable audio-visual
video captioning network to discover the association between words in sentences
and audio-visual sequences. To achieve this, we propose a multimodal
convolutional neural network-based audio-visual video captioning framework and
introduce a modality-aware module for exploring modality selection during
sentence generation. Besides, we collect new audio captioning and visual
captioning datasets for further exploring the interactions between auditory and
visual modalities for high-level video understanding. Extensive experiments
demonstrate that the modality-aware module makes our model interpretable on
modality selection during sentence generation. Even with the added
interpretability, our video captioning network can still achieve comparable
performance with recent state-of-the-art methods.
