  Our understanding of the visual world is centered around various concept
axes, characterizing different aspects of visual entities. While different
concept axes can be easily specified by language, e.g. color, the exact visual
nuances along each axis often exceed the limitations of linguistic
articulations, e.g. a particular style of painting. In this work, our goal is
to learn a language-informed visual concept representation, by simply
distilling large pre-trained vision-language models. Specifically, we train a
set of concept encoders to encode the information pertinent to a set of
language-informed concept axes, with an objective of reproducing the input
image through a pre-trained Text-to-Image (T2I) model. To encourage better
disentanglement of different concept encoders, we anchor the concept embeddings
to a set of text embeddings obtained from a pre-trained Visual Question
Answering (VQA) model. At inference time, the model extracts concept embeddings
along various axes from new test images, which can be remixed to generate
images with novel compositions of visual concepts. With a lightweight test-time
finetuning procedure, it can also generalize to novel concepts unseen at
training.
