  The goal of multimodal alignment is to learn a single latent space that is
shared between multimodal inputs. The most powerful models in this space have
been trained using massive datasets of paired inputs and large-scale
computational resources, making them prohibitively expensive to train in many
practical scenarios. We surmise that existing unimodal encoders pre-trained on
large amounts of unimodal data should provide an effective bootstrap to create
multimodal models from unimodal ones at much lower costs. We therefore propose
FuseMix, a multimodal augmentation scheme that operates on the latent spaces of
arbitrary pre-trained unimodal encoders. Using FuseMix for multimodal
alignment, we achieve competitive performance -- and in certain cases
outperform state-of-the art methods -- in both image-text and audio-text
retrieval, with orders of magnitude less compute and data: for example, we
outperform CLIP on the Flickr30K text-to-image retrieval task with $\sim \!
600\times$ fewer GPU days and $\sim \! 80\times$ fewer image-text pairs.
Additionally, we show how our method can be applied to convert pre-trained
text-to-image generative models into audio-to-image ones. Code is available at:
https://github.com/layer6ai-labs/fusemix.
