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.

本研究提出了一种计算机视觉系统的方法，用于无需有标签的数据来识别艺术图像中的物体，通过域适应和样式转移等技术来实现，无需大量的目标数据即可进行跨域对象识别及场景分类。