Unsupervised Domain Attention Adaptation Network for Caricature Attribute Recognition

• URL: http://arxiv.org/abs/2007.09344v1
• Date: Sat, 18 Jul 2020 06:38:45 GMT
• Title: Unsupervised Domain Attention Adaptation Network for Caricature Attribute Recognition
• Authors: Wen Ji, Kelei He, Jing Huo, Zheng Gu, Yang Gao
• Abstract summary: Caricature attributes provide distinctive facial features to help research in Psychology and Neuroscience. Unlike the facial photo attribute datasets that have a quantity of annotated images, the annotations of caricature attributes are rare. We propose a caricature attribute dataset, namely WebCariA, to facility the research in attribute learning of caricatures.
• Score: 23.95731281719786
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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