  We consider the task of image-captioning using only the CLIP model and
additional text data at training time, and no additional captioned images. Our
approach relies on the fact that CLIP is trained to make visual and textual
embeddings similar. Therefore, we only need to learn how to translate CLIP
textual embeddings back into text, and we can learn how to do this by learning
a decoder for the frozen CLIP text encoder using only text. We argue that this
intuition is "almost correct" because of a gap between the embedding spaces,
and propose to rectify this via noise injection during training. We demonstrate
the effectiveness of our approach by showing SOTA zero-shot image captioning
across four benchmarks, including style transfer. Code, data, and models are
available on GitHub.
