  Current state-of-the-art image captioning models adopt autoregressive
decoders, \ie they generate each word by conditioning on previously generated
words, which leads to heavy latency during inference. To tackle this issue,
non-autoregressive image captioning models have recently been proposed to
significantly accelerate the speed of inference by generating all words in
parallel. However, these non-autoregressive models inevitably suffer from large
generation quality degradation since they remove words dependence excessively.
To make a better trade-off between speed and quality, we introduce a
semi-autoregressive model for image captioning~(dubbed as SATIC), which keeps
the autoregressive property in global but generates words parallelly in local .
Based on Transformer, there are only a few modifications needed to implement
SATIC. Experimental results on the MSCOCO image captioning benchmark show that
SATIC can achieve a good trade-off without bells and whistles. Code is
available at {\color{magenta}\url{https://github.com/YuanEZhou/satic}}.
