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Visual layout plays a critical role in graphic design fields such as advertising, posters, and web UI design.
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Arroyo, D.M., Postels, J., Tombari, F.: Variational transformer networks for layout generation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 13642–13652 (2021)
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Kikuchi, K., Simo-Serra, E., Otani, M., Yamaguchi, K.: Constrained graphic layout generation via latent optimization. In: Proceedings of the 29th ACM International Conference on Multimedia. pp. 88–96 (2021)
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2021
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Yamaguchi, K.: Canvasvae: Learning to generate vector graphic documents. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 5481–5489 (2021)
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Hui, M., Zhang, Z., Zhang, X., Xie, W., Wang, Y., Lu, Y.: Unifying layout generation with a decoupled diffusion model. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 1942–1951 (2023)
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Jiang, Z., Guo, J., Sun, S., Deng, H., Wu, Z., Mijovic, V., Yang, Z.J., Lou, J.G., Zhang, D.: Layoutformer++: Conditional graphic layout generation via constraint serialization and decoding space restriction. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 18403–18412 (2023)
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Cao, Y., Ma, Y., Zhou, M., Liu, C., Xie, H., Ge, T., Jiang, Y.: Geometry aligned variational transformer for image-conditioned layout generation. In: Proceedings of the 30th ACM International Conference on Multimedia. pp. 1561–1571 (2022)
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Jiang, Z., Sun, S., Zhu, J., Lou, J.G., Zhang, D.: Coarse-to-fine generative modeling for graphic layouts. In: Proceedings of the AAAI conference on artificial intelligence. vol. 36, pp. 1096–1103 (2022)
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Kong, X., Jiang, L., Chang, H., Zhang, H., Hao, Y., Gong, H., Essa, I.: Blt: bidirectional layout transformer for controllable layout generation. In: European Conference on Computer Vision. pp. 474–490. Springer (2022)
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2022
Cited alongside, same era.
Schuhmann, C., Beaumont, R., Vencu, R., Gordon, C., Wightman, R., Cherti, M., Coombes, T., Katta, A., Mullis, C., Wortsman, M., et al.: Laion-5b: An open large-scale dataset for training next generation image-text models. Advances in Neural Information Processing Systems 35
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Zhao, X., Pang, Y., Zhang, L., Lu, H.: Joint learning of salient object detection, depth estimation and contour extraction. IEEE Transactions on Image Processing 31
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2022
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Chai, S., Zhuang, L., Yan, F.: Layoutdm: Transformer-based diffusion model for layout generation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 18349–18358 (2023)
2023
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2023
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Li, Y., Liu, H., Wu, Q., Mu, F., Yang, J., Gao, J., Li, C., Lee, Y.J.: Gligen: Open-set grounded text-to-image generation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 22511–22521 (2023)
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2023
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Lin, J., Guo, J., Sun, S., Yang, Z., Lou, J.G., Zhang, D.: Layoutprompter: Awaken the design ability of large language models. Advances in Neural Information Processing Systems 36
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2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
Yang, Z., Wang, J., Gan, Z., Li, L., Lin, K., Wu, C., Duan, N., Liu, Z., Liu, C., Zeng, M., et al.: Reco: Region-controlled text-to-image generation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 14246–14255 (2023)
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Zhang, J., Guo, J., Sun, S., Lou, J.G., Zhang, D.: LayoutDiffusion: Improving Graphic Layout Generation by Discrete Diffusion Probabilistic Models. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) (2023)
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Zhang, L., Rao, A., Agrawala, M.: Adding conditional control to text-to-image diffusion models. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 3836–3847 (2023)
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2023
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Horita, D., Inoue, N., Kikuchi, K., Yamaguchi, K., Aizawa, K.: Retrieval-Augmented Layout Transformer for Content-Aware Layout Generation. In: CVPR (2024)
2024
Closest in time.
Stein, G., Cresswell, J., Hosseinzadeh, R., Sui, Y., Ross, B., Villecroze, V., Liu, Z., Caterini, A.L., Taylor, E., Loaiza-Ganem, G.: Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models. Advances in Neural Information Processing Systems 36
2024
Closest in time.