Recent neural network models for image captioning usually employ an
encoder-decoder architecture, where the decoder adopts a recursive sequence
decoding way. However, such autoregressive decoding may result in sequential
error accumulation and slow generation which limit the applications in
practice. Non-autoregressive (NA) decoding has been proposed to cover these
issues but suffers from language quality problem due to the indirect modeling
of the target distribution. Towards that end, we propose an improved NA
prediction framework to accelerate image captioning. Our decoding part consists
of a position alignment to order the words that describe the content detected
in the given image, and a fine non-autoregressive decoder to generate elegant
descriptions. Furthermore, we introduce an inference strategy that regards
position information as a latent variable to guide the further sentence
generation. The Experimental results on public datasets show that our proposed
model achieves better performance compared to general NA captioning models,
while achieves comparable performance as autoregressive image captioning models
with a significant speedup.

提出了一种非自回归解码框架用于图片描述，该框架使用位置对齐和推理策略来指导更进一步的句子生成，实验结果表明此模型相对于自回归模型有显著的加速效果，并且相较于 NA 模型有更好的表现。