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We present a machine learning system that can quantify fine art paintings with a set of visual elements and principles of art.
The concept of style
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The big book of concepts
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Emine Gul Danaci and Nazli Ikizler-Cinbis · 2016
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Chen Huang, Chen Change Loy, and Xiaoou Tang · 2016
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Google’s neural machine translation system: Bridging the gap between human and machine translation, 2016
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Deepart: Learning joint representations of visual arts
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Glove: Global vectors for word representation
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The sun attribute database: Beyond categories for deeper scene understanding
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Modeling attributes from category-attribute proportions
Felix X Yu, Liangliang Cao, Michele Merler, Noel Codella, Tao Chen, John R Smith, and Shih-Fu Chang · 2014
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An embarrassingly simple approach to zero-shot learning
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Principles of Art: Pt. 1. Art in History; Pt. 2. Art in Theory
John Charles Van Dyke
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Hui Mao, Ming Cheung, and James She · 2017
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Bert: Pre-training of deep bidirectional transformers for language understanding, 2018
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The shape of art history in the eyes of the machine
Ahmed Elgammal, Bingchen Liu, Diana Kim, Mohamed Elhoseiny, and Marian Mazzone · 2018
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Finding principal semantics of style in art
Diana Kim, Bingchen Liu, Ahmed Elgammal, and Marian Mazzone · 2018
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Zero-shot learning—a comprehensive evaluation of the good, the bad and the ugly
Yongqin Xian, Christoph H Lampert, Bernt Schiele, and Zeynep Akata · 2018
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Wikiart: Visual art encyclopedia
WikiArt · 2019
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