AI Art Critic? New Dataset and Models Make Emotional Sense of Visual Artworks

Although contemporary AI models have shown a remarkable ability to generate impressive visual artworks across a variety of styles, isn't there something fundamental missing in these works? In the words of French post-Impressionist painter Paul Cézanne, "A work of art which does not begin in emotion is not art." Is it possible for machines to understand and integrate human emotions in this context? The team used ArtEmis to develop machine learning models that can predict the dominant emotion from images or texts and provide associated explanations. The researchers propose that, unlike most natural images in machine learning tasks that are typically labelled based on the objects or actions that appear in the images, the visual art domain also involves understanding viewers' affective responses. This requires a relatively complex analysis integrating image content and its effect on the viewer.