  It is encouraged to see that progress has been made to bridge videos and
natural language. However, mainstream video captioning methods suffer from slow
inference speed due to the sequential manner of autoregressive decoding, and
prefer generating generic descriptions due to the insufficient training of
visual words (e.g., nouns and verbs) and inadequate decoding paradigm. In this
paper, we propose a non-autoregressive decoding based model with a
coarse-to-fine captioning procedure to alleviate these defects. In
implementations, we employ a bi-directional self-attention based network as our
language model for achieving inference speedup, based on which we decompose the
captioning procedure into two stages, where the model has different focuses.
Specifically, given that visual words determine the semantic correctness of
captions, we design a mechanism of generating visual words to not only promote
the training of scene-related words but also capture relevant details from
videos to construct a coarse-grained sentence "template". Thereafter, we devise
dedicated decoding algorithms that fill in the "template" with suitable words
and modify inappropriate phrasing via iterative refinement to obtain a
fine-grained description. Extensive experiments on two mainstream video
captioning benchmarks, i.e., MSVD and MSR-VTT, demonstrate that our approach
achieves state-of-the-art performance, generates diverse descriptions, and
obtains high inference efficiency. Our code is available at
https://github.com/yangbang18/Non-Autoregressive-Video-Captioning.
