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.
W. Ji and K. He—These authors contributed equally as co-first authors.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Abaci, B., Akgul, T.: Matching caricatures to photographs. Signal Image Video Process. 9(1), 295–303 (2015). https://doi.org/10.1007/s11760-015-0819-8
Abdulnabi, A.H., Wang, G., Lu, J., Jia, K.: Multi-task CNN model for attribute prediction. IEEE Trans. Multimed. 17(11), 1949–1959 (2015)
Brennan, S.E.: Caricature generator: the dynamic exaggeration of faces by computer. Leonardo 40(4), 392–400 (2007)
Cao, K., Liao, J., Yuan, L.: Carigans: unpaired photo-to-caricature translation. ACM Trans. Graph. 37(6), 244 (2018)
Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 834–848 (2017)
Csurka, G.: Domain adaptation for visual applications: a comprehensive survey. arXiv preprint arXiv:1702.05374 (2017)
Ding, H., Zhou, H., Zhou, S.K., Chellappa, R.: A deep cascade network for unaligned face attribute classification. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018)
Ehrlich, M., Shields, T.J., Almaev, T., Amer, M.R.: Facial attributes classification using multi-task representation learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 47–55 (2016)
Ganin, Y., Lempitsky, V.: Unsupervised domain adaptation by backpropagation. In: International Conference on Machine Learning, pp. 1180–1189 (2015)
Geng, X., Yin, C., Zhou, Z.H.: Facial age estimation by learning from label distributions. IEEE Trans. Pattern Anal. Mach. Intell. 35(10), 2401–2412 (2013)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
He, K., Wang, Z., Fu, Y., Feng, R., Jiang, Y.G., Xue, X.: Adaptively weighted multi-task deep network for person attribute classification. In: Proceedings of the 25th ACM International Conference on Multimedia, pp. 1636–1644. ACM (2017)
Hoffman, J., et al.: Cycada: cycle-consistent adversarial domain adaptation. In: Proceedings of the 35th International Conference on Machine Learning (2018)
Huo, J., Li, W., Shi, Y., Gao, Y., Yin, H.: Webcaricature: a benchmark for caricature recognition. arXiv preprint arXiv:1703.03230 (2017)
Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1125–1134 (2017)
Jacob, L., Philippe Vert, J., Bach, F.R.: Clustered multi-task learning: a convex formulation. In: Koller, D., Schuurmans, D., Bengio, Y., Bottou, L. (eds.) Advances in Neural Information Processing Systems, vol. 21, pp. 745–752. Curran Associates, Inc. (2009). http://papers.nips.cc/paper/3499-clustered-multi-task-learning-a-convex-formulation.pdf
Kim, J., Kim, M., Kang, H., Lee, K.H.: U-gat-it: unsupervised generative attentional networks with adaptive layer-instance normalization for image-to-image translation. In: International Conference on Learning Representations (2019)
Klare, B.F., Bucak, S.S., Jain, A.K., Akgul, T.: Towards automated caricature recognition. In: 2012 5th IAPR International Conference on Biometrics (ICB), pp. 139–146. IEEE (2012)
Kumar, A., Daume III, H.: Learning task grouping and overlap in multi-task learning. In: ICML (2012)
Lee, S., Kim, D., Kim, N., Jeong, S.G.: Drop to adapt: learning discriminative features for unsupervised domain adaptation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 91–100 (2019)
Liu, Z., Luo, P., Wang, X., Tang, X.: Deep learning face attributes in the wild. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3730–3738 (2015)
Long, M., Cao, Y., Wang, J., Jordan, M.: Learning transferable features with deep adaptation networks. In: International Conference on Machine Learning, pp. 97–105 (2015)
Lu, Y., Kumar, A., Zhai, S., Cheng, Y., Javidi, T., Feris, R.: Fully-adaptive feature sharing in multi-task networks with applications in person attribute classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5334–5343 (2017)
Luo, P., Wang, X., Tang, X.: Hierarchical face parsing via deep learning. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 2480–2487. IEEE (2012)
Luo, P., Wang, X., Tang, X.: A deep sum-product architecture for robust facial attributes analysis. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2864–2871 (2013)
Mauro, R., Kubovy, M.: Caricature and face recognition. Mem. Cogn. 20(4), 433–440 (1992)
Perkins, D.: A definition of caricature and caricature and recognition. Stud. Vis. Commun. 2(1), 1–24 (1975)
Rudd, E.M., Günther, M., Boult, T.E.: MOON: a mixed objective optimization network for the recognition of facial attributes. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9909, pp. 19–35. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46454-1_2
Russo, P., Carlucci, F.M., Tommasi, T., Caputo, B.: From source to target and back: symmetric bi-directional adaptive GAN. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2018
Saito, K., Watanabe, K., Ushiku, Y., Harada, T.: Maximum classifier discrepancy for unsupervised domain adaptation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3723–3732 (2018)
Smith, V., Chiang, C.K., Sanjabi, M., Talwalkar, A.S.: Federated multi-task learning. In: Advances in Neural Information Processing Systems, pp. 4424–4434 (2017)
Tsai, Y.H., Hung, W.C., Schulter, S., Sohn, K., Yang, M.H., Chandraker, M.: Learning to adapt structured output space for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7472–7481 (2018)
Valentine, T., Lewis, M.B., Hills, P.J.: Face-space: a unifying concept in face recognition research. Quart. J. Exp. Psychol. 69(10), 1996–2019 (2016)
Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, pp. 5998–6008 (2017)
Vázquez, D., López, A.M., Ponsa, D.: Unsupervised domain adaptation of virtual and real worlds for pedestrian detection. In: Proceedings of the 21st International Conference on Pattern Recognition (ICPR 2012), pp. 3492–3495. IEEE (2012)
Wang, X., Guo, R., Kambhamettu, C.: Deeply-learned feature for age estimation. In: 2015 IEEE Winter Conference on Applications of Computer Vision, pp. 534–541. IEEE (2015)
Wang, Z., He, K., Fu, Y., Feng, R., Jiang, Y.G., Xue, X.: Multi-task deep neural network for joint face recognition and facial attribute prediction. In: Proceedings of the 2017 ACM on International Conference on Multimedia Retrieval, pp. 365–374. ACM (2017)
Zhang, Y., Shen, W., Sun, L., Li, Q.: Position-squeeze and excitation module for facial attribute analysis. In: BMVC (2018)
Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.: Learning deep features for discriminative localization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2921–2929 (2016)
Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2223–2232 (2017)
Zhu, Z., Luo, P., Wang, X., Tang, X.: Multi-view perceptron: a deep model for learning face identity and view representations. In: Advances in Neural Information Processing Systems, pp. 217–225 (2014)
Acknowledgement
This work is supported in part by National Science Foundation of China under Grant No. 61806092, and in part by Jiangsu Natural Science Foundation under Grant No. BK20180326.
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
1 Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Ji, W., He, K., Huo, J., Gu, Z., Gao, Y. (2020). Unsupervised Domain Attention Adaptation Network for Caricature Attribute Recognition. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12353. Springer, Cham. https://doi.org/10.1007/978-3-030-58598-3_2
Download citation
DOI: https://doi.org/10.1007/978-3-030-58598-3_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-58597-6
Online ISBN: 978-3-030-58598-3
eBook Packages: Computer ScienceComputer Science (R0)