Abstract
The goal of this paper is to manipulate image attributes using text description. Although many methods can synthesize images with new properties from text, they cannot fully preserve the text-independent content of the original image. There are two major limitations: (1) Some important details in image sub-region will be lost in the process of image modification; (2) Compared with the original image, the shape and edge of the object in the modified image will be more blurred. Therefore, we propose a novel framework edge aware generative adversarial network (EA-GAN) that uses edge information to guide image modification, which ensures the network’s ability to identify local regions and realizes accurate modification of image sub-regions. At the same time, an edge reconstruction loss (ERLoss) is added to the generator to constrain the generation of edges, generate sharper edges, and improve the clarity of the image. The experimental data on the CUB and Oxford-102 datasets show that the algorithm used in this paper can well distinguish the corresponding image features in the conditional text, and modify the image attribute of specific regions in the image.
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References
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S.: Generative Adversarial Nets. In: NIPS, pp. 2672–2680 (2014)
Dong, H., Yu, S., Wu, C.,: Semantic Image Synthesis via Adversarial Learning. In: ICCV, pp. 5707–5715 (2017) 10.1109/iccv.2017.608
Nam, S., Kim, Y., Kim, S.J.: Text-Adaptive Generative Adversarial Networks: Manipulating Images with Natural Language. In: NeurIPS, pp. 42–51 (2018)
Geirhos, R., Rubisch, P., Michaelis, C.: ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness. In: ICLR (2019)
Reed, S., Akata, Z., Yan, X., Logeswaran, L., Schiele, B., Lee, H.: Generative adversarial text to image synthesis. In: ICML, pp. 1060–1069 (2016)
Zhang, H., Xu, T., Li, H., Zhang, S., Wang, X., Huang, X., Metaxas, D.N.: Stackgan: Text to photo-realistic image synthesis with stacked generative adversarial networks. In: Proceedings of the IEEE international conference on computer vision, pp. 5907–5915 (2017) 10.1109/iccv.2017.629
Xu, T., Zhang, P., Huang, Q., Zhang, H., Gan, Z., Huang, X., He, X.: Attngan: Fine-grained text to image generation with attentional generative adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1316–1324 (2018)
Yu, Y., Gong, Z., Zhong, P., Shan, J.: Unsupervised representation learning with deep convolutional neural network for remote sensing images. In: Zhao, Y., Kong, X., Taubman, D. (eds.) ICIG 2017. LNCS, vol. 10667, pp. 97–108. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-71589-6_9
Chen, Q., Koltun, V.: Photographic image synthesis with cascaded refinement networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1511–1520 (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)
Wang, T. C., Liu, M. Y., Zhu, J. Y., Tao, A., Kautz, J., Catanzaro, B.: High-resolution image synthesis and semantic manipulation with conditional GANs. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8798–8807 (2018) https://doi.org/10.1109/cvpr.2018.00917
Liu, M. Y., Tuzel, O.: Coupled generative adversarial networks. In: Advances in Neural Information Processing Systems, vol. 29 (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)
Catherine, W., Steve, B., Peter, W., Pietro, P., Serge B.: The caltech-ucsd birds-200-2011dataset (2011)
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., Chen, X.: Improved techniques for training GANs. In: Advances in Neural Information Processing Systems, vol. 29 (2016)
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: Gans trained by a two time-scale update rule converge to a local nash equilibrium. In: Advances in Neural Information Processing Systems, vol. 30 (2017)
Nilsback, M.E., Zisserman, A.: Automated flower classification over a large number of classes. In: 2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing, pp. 722–729 (2008)
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818–2826 (2016)
Karras, T., Laine, S., Aila, T.: A style-based generator architecture for generative adversarial networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4401–4410 (2019) https://doi.org/10.1109/cvpr.2019.00453
Yu, Y., Gong, Z., Zhong, P., Shan, J.: Unsupervised representation learning with deep convolutional neural network for remote sensing images. In: Zhao, Y., Kong, X., Taubman, D. (eds.) ICIG 2017. LNCS, vol. 10667, pp. 97–108. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-71589-6_9
Liu, X., Yin, G., Shao, J., Wang, X.: Learning to predict layout-to-image conditional convolutions for semantic image synthesis. In: Advances in Neural Information Processing Systems, vol. 32 (2019)
Yin, G., Liu, B., Sheng, L., Yu, N., Wang, X., Shao, J.: Semantics disentangling for text-to-image generation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2327–2336 (2019)
Tao, M., et al.: Df-gan: Deep fusion generative adversarial networks for text-to-image synthesis. arXiv preprint arXiv:2008.05865(2020)
He, Z., Zuo, W., Kan, M., Shan, S., Chen, X.: Arbitrary facial attribute editing: Only change what you want. arXiv preprint arXiv:1711.10678, vol. 1(3) (2017)
Iizuka, S., Simo-Serra, E., Ishikawa, H.: Let there be color! Joint end-to-end learning of global and local image priors for automatic image colorization with simultaneous classification. ACM Trans. Graph. (ToG) 35(4), 1–11 (2016)
Pathak, D., Krahenbuhl, P., Donahue, J., Darrell, T., Efros, A.A.: Context encoders: Feature learning by inpainting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2536–2544 (2016)
Zhu, J.-Y., Krähenbühl, P., Shechtman, E., Efros, A.A.: Generative visual manipulation on the natural image manifold. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9909, pp. 597–613. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46454-1_36
Gatys, L.A., Ecker, A.S., Bethge, M.: Image style transfer using convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2414–2423 (2016)
Johnson, Justin, Alahi, Alexandre, Fei-Fei, Li.: Perceptual losses for real-time style transfer and super-resolution. In: Leibe, Bastian, Matas, Jiri, Sebe, Nicu, Welling, Max (eds.) ECCV 2016. LNCS, vol. 9906, pp. 694–711. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46475-6_43
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)
Lample, G., Zeghidour, N., Usunier, N., Bordes, A., Denoyer, L., Ranzato, M.A.: Fader networks: Manipulating images by sliding attributes. In: Advances in Neural Information Processing Systems, vol. 30 (2017)
Li, B., Qi, X., Lukasiewicz, T., Torr, P.H.: ManiGAN: Text-guided image manipulation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7880–7889 (2020)
Liao, W., Hu, K., Yang, M.Y., Rosenhahn, B.: Text to image generation with semantic-spatial aware GAN. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 18187–18196 (2022)
Esser, P., Rombach, R., Ommer, B.: Taming transformers for high-resolution image synthesis. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 12873–12883 (2021). https://doi.org/10.1109/cvpr46437.2021.01268
Van Den Oord, A., Vinyals, O.: Neural discrete representation learning. In: Advances in Neural Information Processing Systems, vol. 30 (2017)
Ramesh, A., Pavlov, M., Goh, G., Gray, S., Voss, C., Radford, A., Sutskever, I.: Zero-shot text-to-image generation. In: International Conference on Machine Learning, pp. 8821–8831. PMLR (2021)
Ding, M., et al.: Cogview: Mastering text-to-image generation via transformers. Advances in Neural Information Processing Systems 34, 19822–19835 (2021)
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Zhao, L., Li, X., Fu, C., Chen, Z. (2022). Image Attribute Modification Based on Text Guidance. In: Ma, H., Wang, X., Cheng, L., Cui, L., Liu, L., Zeng, A. (eds) Wireless Sensor Networks. CWSN 2022. Communications in Computer and Information Science, vol 1715. Springer, Singapore. https://doi.org/10.1007/978-981-19-8350-4_16
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