  In computer vision, visual arts are often studied from a purely aesthetics
perspective, mostly by analysing the visual appearance of an artistic
reproduction to infer its style, its author, or its representative features. In
this work, however, we explore art from both a visual and a language
perspective. Our aim is to bridge the gap between the visual appearance of an
artwork and its underlying meaning, by jointly analysing its aesthetics and its
semantics. We introduce the use of multi-modal techniques in the field of
automatic art analysis by 1) collecting a multi-modal dataset with fine-art
paintings and comments, and 2) exploring robust visual and textual
representations in artistic images.
