  Automatically describing audio-visual content with texts, namely video
captioning, has received significant attention due to its potential
applications across diverse fields. Deep neural networks are the dominant
methods, offering state-of-the-art performance. However, these methods are
often undeployable in low-power devices like smartphones due to the large size
of the model parameters. In this paper, we propose to exploit simple pooling
front-end and down-sampling algorithms with knowledge distillation for audio
and visual attributes using a reduced number of audio-visual frames. With the
help of knowledge distillation from the teacher model, our proposed method
greatly reduces the redundant information in audio-visual streams without
losing critical contexts for caption generation. Extensive experimental
evaluations on the MSR-VTT dataset demonstrate that our proposed approach
significantly reduces the inference time by about 80% with a small sacrifice
(less than 0.02%) in captioning accuracy.
