  Image captioning task has been extensively researched by previous work.
However, limited experiments focus on generating captions based on
non-autoregressive text decoder. Inspired by the recent success of the
denoising diffusion model on image synthesis tasks, we apply denoising
diffusion probabilistic models to text generation in image captioning tasks. We
show that our CLIP-Diffusion-LM is capable of generating image captions using
significantly fewer inference steps than autoregressive models. On the Flickr8k
dataset, the model achieves 0.1876 BLEU-4 score. By training on the combined
Flickr8k and Flickr30k dataset, our model achieves 0.2470 BLEU-4 score. Our
code is available at https://github.com/xu-shitong/diffusion-image-captioning.
