  In this paper, we propose methods to build a powerful and efficient
Image-to-Speech captioning (Im2Sp) model. To this end, we start with importing
the rich knowledge related to image comprehension and language modeling from a
large-scale pre-trained vision-language model into Im2Sp. We set the output of
the proposed Im2Sp as discretized speech units, i.e., the quantized speech
features of a self-supervised speech model. The speech units mainly contain
linguistic information while suppressing other characteristics of speech. This
allows us to incorporate the language modeling capability of the pre-trained
vision-language model into the spoken language modeling of Im2Sp. With the
vision-language pre-training strategy, we set new state-of-the-art Im2Sp
performances on two widely used benchmark databases, COCO and Flickr8k. Then,
we further improve the efficiency of the Im2Sp model. Similar to the speech
unit case, we convert the original image into image units, which are derived
through vector quantization of the raw image. With these image units, we can
drastically reduce the required data storage for saving image data to just 0.8%
when compared to the original image data in terms of bits. Demo page:
https://ms-dot-k.github.io/Image-to-Speech-Captioning.
