Abstract Art

Research on abstract art is exploring how computational methods can analyze, generate, and understand this artistic style, bridging the gap between human perception and machine interpretation of visual and emotional content. Current investigations utilize various deep learning architectures, including GANs, CLIP, and transformer-based models, to address tasks such as style transfer, emotion recognition, and content-based retrieval of abstract artworks. These studies aim to improve both our understanding of human perception of abstract art and to develop new tools for artists and art historians, such as advanced search engines and generative art systems. The resulting insights and technologies have implications for both the art world and the broader field of computer vision and AI.