Artistic Object Recognition by Unsupervised Style Adaptation
This addresses the challenge for computer vision systems in reliably recognizing artistically rendered objects, especially when data is limited, though it is incremental as it builds on existing domain adaptation methods.
The paper tackles the problem of recognizing objects in artistic modalities like paintings or sketches without labeled data, by proposing an unsupervised style adaptation method that uses automatically generated complementary training modalities and works with as few as ten unlabeled images, achieving significant accuracy improvements over existing domain adaptation techniques.
Computer vision systems currently lack the ability to reliably recognize artistically rendered objects, especially when such data is limited. In this paper, we propose a method for recognizing objects in artistic modalities (such as paintings, cartoons, or sketches), without requiring any labeled data from those modalities. Our method explicitly accounts for stylistic domain shifts between and within domains. To do so, we introduce a complementary training modality constructed to be similar in artistic style to the target domain, and enforce that the network learns features that are invariant between the two training modalities. We show how such artificial labeled source domains can be generated automatically through the use of style transfer techniques, using diverse target images to represent the style in the target domain. Unlike existing methods which require a large amount of unlabeled target data, our method can work with as few as ten unlabeled images. We evaluate it on a number of cross-domain object and scene classification tasks and on a new dataset we release. Our experiments show that our approach, though conceptually simple, significantly improves the accuracy that existing domain adaptation techniques obtain for artistic object recognition.