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We present a machine learning system that can quantify fine art paintingswith a set of visual elements and principles of art. This formal analysis isfundamental for understanding art, but developing such a system is challenging.Paintings have high visual complexities, but it is also difficult to collectenough training data with direct labels. To resolve these practicallimitations, we introduce a novel mechanism, called proxy learning, whichlearns visual concepts in paintings though their general relation to styles.This framework does not require any visual annotation, but only uses stylelabels and a general relationship between visual concepts and style. In thispaper, we propose a novel proxy model and reformulate four pre-existing methodsin the context of proxy learning. Through quantitative and qualitativecomparison, we evaluate these methods and compare their effectiveness inquantifying the artistic visual concepts, where the general relationship isestimated by language models; GloVe or BERT. The language modeling is apractical and scalable solution requiring no labeling, but it is inevitablyimperfect. We demonstrate how the new proxy model is robust to theimperfection, while the other models are sensitively affected by it.

Formal Analysis of Art: Proxy Learning of Visual Concepts from Style Through Language Models