ArtNet

Image recognition systems perform well when detecting objects in photographic images, but poorly when confronted with an image style that is different from the data used in training - in particular when dealing with non-photographic images. This is remarkably different from human vision: Even a very small child can recognize the same objects in photographies and simple drawings. We hypothesize that developing visual recognition algorithms that can recognize depictions in art images may give us a deeper understanding of computer vision compared to human vision, and may help develop ​more general and robust vision algorithms.​ We address two important obstacles: lack of sufficiently large datasets of non-photographic images for training, as well as the ethical and cultural tensions between computer vision and human interpretation of images.