  The last decade has witnessed remarkable progress in the image captioning
task; however, most existing methods cannot control their captions,
\emph{e.g.}, choosing to describe the image either roughly or in detail. In
this paper, we propose to use a simple length level embedding to endow them
with this ability. Moreover, due to their autoregressive nature, the
computational complexity of existing models increases linearly as the length of
the generated captions grows. Thus, we further devise a non-autoregressive
image captioning approach that can generate captions in a length-irrelevant
complexity. We verify the merit of the proposed length level embedding on three
models: two state-of-the-art (SOTA) autoregressive models with different types
of decoder, as well as our proposed non-autoregressive model, to show its
generalization ability. In the experiments, our length-controllable image
captioning models not only achieve SOTA performance on the challenging MS COCO
dataset but also generate length-controllable and diverse image captions.
Specifically, our non-autoregressive model outperforms the autoregressive
baselines in terms of controllability and diversity, and also significantly
improves the decoding efficiency for long captions. Our code and models are
released at \textcolor{magenta}{\texttt{https://github.com/bearcatt/LaBERT}}.
