论文标题
漫画属性识别的无监督域注意适应网络
Unsupervised Domain Attention Adaptation Network for Caricature Attribute Recognition
论文作者
论文摘要
漫画属性提供了独特的面部特征，以帮助心理学和神经科学研究。但是，与具有大量注释图像的面部照片属性数据集不同，漫画属性的注释很少见。为了进行讽刺漫画的属性学习研究，我们提出了一个漫画属性数据集，即WebCaria。此外，要利用通过面部属性训练的模型，我们提出了一个新颖的无监督域适应性框架（即，即漫画的照片）属性识别，并采用集成的内部和内部内部和内域一致性学习方案。具体而言，通过生成中间图像样本和标签一致性学习模块来对齐其语义信息，将组成图像到图像转换器组成的域间一致性学习方案首先填补照片和漫画之间的域间隙。域内的一致性学习方案将共同特征一致性学习模块与新型属性 - 感知注意力一致性学习模块相结合，以更有效地对齐。我们进行了一项广泛的消融研究，以显示该方法的有效性。所提出的方法还以边距优于最先进的方法。该方法的实现可在https://github.com/keleihe/daan上获得。
Caricature attributes provide distinctive facial features to help research in Psychology and Neuroscience. However, unlike the facial photo attribute datasets that have a quantity of annotated images, the annotations of caricature attributes are rare. To facility the research in attribute learning of caricatures, we propose a caricature attribute dataset, namely WebCariA. Moreover, to utilize models that trained by face attributes, we propose a novel unsupervised domain adaptation framework for cross-modality (i.e., photos to caricatures) attribute recognition, with an integrated inter- and intra-domain consistency learning scheme. Specifically, the inter-domain consistency learning scheme consisting an image-to-image translator to first fill the domain gap between photos and caricatures by generating intermediate image samples, and a label consistency learning module to align their semantic information. The intra-domain consistency learning scheme integrates the common feature consistency learning module with a novel attribute-aware attention-consistency learning module for a more efficient alignment. We did an extensive ablation study to show the effectiveness of the proposed method. And the proposed method also outperforms the state-of-the-art methods by a margin. The implementation of the proposed method is available at https://github.com/KeleiHe/DAAN.
