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Supervised Attribute Information Removal and Reconstruction for Image Manipulation

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Computer Vision – ECCV 2022 (ECCV 2022)

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Abstract

The goal of attribute manipulation is to control specified attribute(s) in given images. Prior work approaches this problem by learning disentangled representations for each attribute that enables it to manipulate the encoded source attributes to the target attributes. However, encoded attributes are often correlated with relevant image content. Thus, the source attribute information can often be hidden in the disentangled features, leading to unwanted image editing effects. In this paper, we propose an Attribute Information Removal and Reconstruction (AIRR) network that prevents such information hiding by learning how to remove the attribute information entirely, creating attribute excluded features, and then learns to directly inject the desired attributes in a reconstructed image. We evaluate our approach on four diverse datasets with a variety of attributes including DeepFashion Synthesis, DeepFashion Fine-grained Attribute, CelebA and CelebA-HQ, where our model improves attribute manipulation accuracy and top-k retrieval rate by 10% on average over prior work. A user study also reports that AIRR manipulated images are preferred over prior work in up to 76% of cases (Code and models are available at https://github.com/NannanLi999/AIRR).

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References

  1. Abdal, R., Qin, Y., Wonka, P.: Image2stylegan++: how to edit the embedded images? In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2020)

    Google Scholar 

  2. Ak, K.E., Lim, J.H., Sun, Y., Tham, J.Y., Kassim, A.A.: Fashionsearchnet-v2: learning attribute representations with localization for image retrieval with attribute manipulation. arXiv preprint. arXiv:2111.14145 (2021)

  3. Ak, K.E., Lim, J.H., Tham, J.Y., Kassim, A.A.: Attribute manipulation generative adversarial networks for fashion images. In: Proceedings of the IEEE International Conference on Computer Vision (2019)

    Google Scholar 

  4. Bashkirova, D., Usman, B., Saenko, K.: Adversarial self-defense for cycle-consistent GANs. arXiv preprint. arXiv:1908.01517 (2019)

  5. Burns, A., Sarna, A., Krishnan, D., Maschinot, A.: Unsupervised disentanglement without autoencoding: Pitfalls and future directions. arXiv preprint. arXiv:2108.06613 (2021)

  6. Chen, X., Duan, Y., Houthooft, R., Schulman, J., Sutskever, I., Abbeel, P.: Infogan: interpretable representation learning by information maximizing generative adversarial nets. In: Proceedings of the International Conference on Neural Information Processing Systems (2016)

    Google Scholar 

  7. Choi, Y., Choi, M., Kim, M., Ha, J., Kim, S., Choo, J.: StarGAN: unified generative adversarial networks for multi-domain image-to-image translation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2018)

    Google Scholar 

  8. Choi, Y., Uh, Y., Yoo, J., Ha, J.W.: StarGAN v2: diverse image synthesis for multiple domains. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2020)

    Google Scholar 

  9. Härkönen, E., Hertzman, A., Lehtinen, J., Paris, S.: GANSpace: discovering interpretable GAN controls. In: Proceedings of the IEEE Conference on Neural Information Processing Systems (2020)

    Google Scholar 

  10. 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 (2016)

    Google Scholar 

  11. He, Z., Zuo, W., Kan, M., Shan, S., Chen, X.: AttGAN: facial attribute editing by only changing what you want. IEEE Trans. Image Process. 28(11), 5464–5478 (2019)

    Article  MathSciNet  Google Scholar 

  12. Hou, Y., Vig, E., Donoser, M., Bazzani, L.: Learning attribute-driven disentangled representations for interactive fashion retrieval. In: Proceedings of the IEEE International Conference on Computer Vision (2021)

    Google Scholar 

  13. Hu, Q., Szabó, A., Portenier, T., Favaro, P., Zwicker, M.: Disentangling factors of variation by mixing them. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2018)

    Google Scholar 

  14. Karras, T., Laine, S., Aila, T.: A style-based generator architecture for generative adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2019)

    Google Scholar 

  15. Kwak, J., Han, D.K., Ko, H.: CAFE-GAN: arbitrary face attribute editing with complementary attention feature. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12359, pp. 524–540. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58568-6_31

    Chapter  Google Scholar 

  16. Kwon, Y., Petrangeli, S., Kim, D., Wang, H., Swaminathan, V., Fuchs, H.: Tailor me: ln editing network for fashion attribute shape manipulation. In: Proceedings of the IEEE Winter Conference on Applications of Computer Vision (2022)

    Google Scholar 

  17. Lee, C.H., Liu, Z., Wu, L., Luo, P.: MaskGAN: towards diverse and interactive facial image manipulation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2020)

    Google Scholar 

  18. Lezama, J.: Overcoming the disentanglement vs reconstruction trade-off via Jacobian supervision. In: International Conference on Learning Representations (2018)

    Google Scholar 

  19. Li, P., Xu, Y., Wei, Y., Yang, Y.: Self-correction for human parsing. IEEE Trans. Pattern Anal. Mach. Intell. (2020). https://doi.org/10.1109/TPAMI.2020.3048039

  20. Liu, X., Thermos, S., Valvano, G., Chartsias, A., O’Neil, A., Tsaftaris, S.A.: Measuring the biases and effectiveness of content-style disentanglement. In: Proceedings of the British Machine Vison Conference (2021)

    Google Scholar 

  21. Liu, Z., Luo, P., Qiu, S., Wang, X., Tang, X.: DeepFashion: powering robust clothes recognition and retrieval with rich annotations. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2016)

    Google Scholar 

  22. Liu, Z., Luo, P., Wang, X., Tang, X.: Deep learning face attributes in the wild. In: Proceedings of International Conference on Computer Vision (2015)

    Google Scholar 

  23. Locatello, F., et al.: Challenging common assumptions in the unsupervised learning of disentangled representations. In: International Conference on Machine Learning (2019)

    Google Scholar 

  24. Mao, X., Li, Q., Xie, H., Lau, R.Y., Wang, Z., Paul Smolley, S.: Least squares generative adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision (2017)

    Google Scholar 

  25. Ramesh, A., Choi, Y., LeCun, Y.: A spectral regularizer for unsupervised disentanglement. In: International Conference on Machine Learning (2018)

    Google Scholar 

  26. Shen, Y., Gu, J., Tang, X., Zhou, B.: Interpreting the latent space of GANs for semantic face editing. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2020)

    Google Scholar 

  27. Shin, M., Park, S., Kim, T.: Semi-supervised feature-level attribute manipulation for fashion image retrieval. In: Proceedings of the British Machine Vison Conference (2019)

    Google Scholar 

  28. Shoshan, A., Bhonker, N., Kviatkovsky, I., Medioni, G.: GAN-control: explicitly controllable GANs. In: Proceedings of the IEEE International Conference on Computer Vision (2021)

    Google Scholar 

  29. Szabo, A., Hu, Q., Portenier, T., Zwicker, M., Favaro, P.: Understanding degeneracies and ambiguities in attribute transfer. In: Proceedings of the European Conference on Computer Vision (2018)

    Google Scholar 

  30. Usman, B., Bashkirova, D., Saenko, K.: Disentangled unsupervised image translation via restricted information flow (2021)

    Google Scholar 

  31. Wang, R., et al.: Attribute-specific control units in StyleGAN for fine-grained image manipulation. In: Proceedings of the ACM International Conference on Multimedia (2021)

    Google Scholar 

  32. Wu, Z., Lischinski, D., Shechtman, E.: StyleSpace analysis: disentangled controls for StyleGAN image generation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2021)

    Google Scholar 

  33. Yang, G., Fei, N., Ding, M., Liu, G., Lu, Z., Xiang, T.: L2M-GAN: learning to manipulate latent space semantics for facial attribute editing. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2021)

    Google Scholar 

  34. Yang, X., Song, X., Han, X., Wen, H., Nie, J., Nie, L.: Generative attribute manipulation scheme for flexible fashion search. In: Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval (2020)

    Google Scholar 

  35. Yao, X., Newson, A., Gousseau, Y., Hellier, P.: A latent transformer for disentangled face editing in images and videos. In: Proceedings of the IEEE International Conference on Computer Vision (2021)

    Google Scholar 

  36. Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: Bisenet: bilateral segmentation network for real-time semantic segmentation. In: Proceedings of the European Conference on Computer Vision (2018)

    Google Scholar 

  37. 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 (2017)

    Google Scholar 

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Acknowledgements

This material is based upon work supported, in part, by DARPA under agreement number HR00112020054 and the National Science Foundation under Grant No. DBI-2134696. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the supporting agencies.

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Correspondence to Nannan Li .

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Li, N., Plummer, B.A. (2022). Supervised Attribute Information Removal and Reconstruction for Image Manipulation. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13677. Springer, Cham. https://doi.org/10.1007/978-3-031-19790-1_28

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  • DOI: https://doi.org/10.1007/978-3-031-19790-1_28

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