  Given the recent advances in multimodal image pretraining where visual models
trained with semantically dense textual supervision tend to have better
generalization capabilities than those trained using categorical attributes or
through unsupervised techniques, in this work we investigate how recent CLIP
model can be applied in several tasks in artwork domain. We perform exhaustive
experiments on the NoisyArt dataset which is a dataset of artwork images
crawled from public resources on the web. On such dataset CLIP achieves
impressive results on (zero-shot) classification and promising results in both
artwork-to-artwork and description-to-artwork domain.
