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Graphic layout designs play an essential role in visual communication.
Lok, S., Feiner, S.: A survey of automated layout techniques for information presentations. Proceedings of SmartGraphics (2001)
2001
Earlier work this paper cites.
Haykin, S., Network, N.: A comprehensive foundation. Neural networks (2004)
2004
Earlier work this paper cites.
Landa, R.: Graphic design solutions/robin landa. Wadsworth, Boston (2010)
2010
Earlier work this paper cites.
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. NeurIPS (2012)
2012
Earlier work this paper cites.
Kingma, D.P., Welling, M.: Auto-encoding variational bayes. ICLR (2013)
2013
Earlier work this paper cites.
Socher, R., Perelygin, A., Wu, J., Chuang, J., Manning, C.D., Ng, A.Y., Potts, C.: Recursive deep models for semantic compositionality over a sentiment treebank. EMNLP (2013)
2013
Earlier work this paper cites.
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial networks. NeurIPS (2014)
2014
Earlier work this paper cites.
Rezende, D.J., Mohamed, S., Wierstra, D.: Stochastic backpropagation and approximate inference in deep generative models. ICML (2014)
2014
Earlier work this paper cites.
Girshick, R.: Fast r-cnn. ICCV (2015)
2015
Earlier work this paper cites.
Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. ICLR (2015)
2015
Earlier work this paper cites.
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. ICLR (2015)
2015
Earlier work this paper cites.
Larsen, A.B.L., Sønderby, S.K., Larochelle, H., Winther, O.: Autoencoding beyond pixels using a learned similarity metric. ICML (2016)
2016
Earlier work this paper cites.
Van den Oord, A., Kalchbrenner, N., Espeholt, L., Vinyals, O., Graves, A., et al.: Conditional image generation with pixelcnn decoders. NeurIPS (2016)
2016
Earlier work this paper cites.
Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. CVPR (2016)
2016
Earlier work this paper cites.
Stribley, M.: Rules of composition all designers live by. Retrieved May (2016)
2016
Earlier work this paper cites.
He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. ICCV (2017)
2017
Earlier work this paper cites.
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. NeurIPS (2017)
2017
Earlier work this paper cites.
Hussain, Z., Zhang, M., Zhang, X., Ye, K., Thomas, C., Agha, Z., Ong, N., Kovashka, A.: Automatic understanding of image and video advertisements. CVPR (2017)
2017
Earlier work this paper cites.
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. ICLR (2017)
2017
Earlier work this paper cites.
Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. ICCV (2017)
2017
Earlier work this paper cites.
Salimans, T., Karpathy, A., Chen, X., Kingma, D.P.: Pixelcnn++: Improving the pixelcnn with discretized logistic mixture likelihood and other modifications. ICLR (2017)
2017
Earlier work this paper cites.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. NeurIPS (2017)
2017
Earlier work this paper cites.
Bińkowski, M., Sutherland, D.J., Arbel, M., Gretton, A.: Demystifying mmd gans. ICLR (2018)
2018
Earlier work this paper cites.
Chen, X., Mishra, N., Rohaninejad, M., Abbeel, P.: Pixelsnail: An improved autoregressive generative model. ICML (2018)
2018
Earlier work this paper cites.
Karras, T., Aila, T., Laine, S., Lehtinen, J.: Progressive growing of gans for improved quality, stability, and variation. ICLR (2018)
2018
Earlier work this paper cites.
Parmar, N., Vaswani, A., Uszkoreit, J., Kaiser, L., Shazeer, N., Ku, A., Tran, D.: Image transformer. ICML (2018)
2018
Earlier work this paper cites.
Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. CVPR (2018)
2018
Earlier work this paper cites.
Bello, I., Zoph, B., Vaswani, A., Shlens, J., Le, Q.V.: Attention augmented convolutional networks. ICCV (2019)
2019
Earlier work this paper cites.
Brock, A., Donahue, J., Simonyan, K.: Large scale gan training for high fidelity natural image synthesis. ICLR (2019)
2019
Earlier work this paper cites.
Chen, G., Xie, P., Dong, J., Wang, T.: Understanding programmatic creative: The role of ai. Journal of Advertising (2019)
2019
Cited alongside, same era.
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. NAACL (2019)
2019
Cited alongside, same era.
Jyothi, A.A., Durand, T., He, J., Sigal, L., Mori, G.: Layoutvae: Stochastic scene layout generation from a label set. ICCV (2019)
2019
Cited alongside, same era.
Karras, T., Laine, S., Aila, T.: A style-based generator architecture for generative adversarial networks. CVPR (2019)
2019
Cited alongside, same era.
Li, J., Yang, J., Hertzmann, A., Zhang, J., Xu, T.: Layoutgan: Generating graphic layouts with wireframe discriminators. ICLR (2019)
2019
Cited alongside, same era.
Yamaguchi, K.: Canvasvae: learning to generate vector graphic documents. ICCV (2021)
2021
Later among the works it cites.
Yu, N., Liu, G., Dundar, A., Tao, A., Catanzaro, B., Davis, L.S., Fritz, M.: Dual contrastive loss and attention for gans. ICCV (2021)
2021
Later among the works it cites.
Cao, Y., Ma, Y., Zhou, M., Liu, C., Xie, H., Ge, T., Jiang, Y.: Geometry aligned variational transformer for image-conditioned layout generation. Multimedia (2022)
2022
Closest in time.
He, K., Chen, X., Xie, S., Li, Y., Dollár, P., Girshick, R.: Masked autoencoders are scalable vision learners. CVPR (2022)
2022
Closest in time.
Jiang, Z., Deng, H., Wu, Z., Guo, J., Sun, S., Mijovic, V., Yang, Z., Lou, J.G., Zhang, D.: Unilayout: Taming unified sequence-to-sequence transformers for graphic layout generation. arXiv (2022)
2022
Closest in time.
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Rezatofighi, H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I., Savarese, S.: Generalized intersection over union: A metric and a loss for bounding box regression. CVPR (2019)
2019
Cited alongside, same era.
Yu, F., Liu, K., Zhang, Y., Zhu, C., Xu, K.: Partnet: A recursive part decomposition network for fine-grained and hierarchical shape segmentation. CVPR (2019)
2019
Cited alongside, same era.
Zheng, X., Qiao, X., Cao, Y., Lau, R.W.: Content-aware generative modeling of graphic design layouts. TOG (2019)
2019
Cited alongside, same era.
Zhong, X., Tang, J., Yepes, A.J.: Publaynet: largest dataset ever for document layout analysis. ICDAR (2019)
2019
Cited alongside, same era.
Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. ECCV (2020)
2020
Cited alongside, same era.
Carlier, A., Danelljan, M., Alahi, A., Timofte, R.: Deepsvg: A hierarchical generative network for vector graphics animation. NeurIPS (2020)
2020
Cited alongside, same era.
Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments (2020)
2020
Cited alongside, same era.
Jiang, Z., Sun, S., Zhu, J., Lou, J.G., Zhang, D.: Coarse-to-fine generative modeling for graphic layouts. AAAI (2022)
2022
Closest in time.
Kong, X., Jiang, L., Chang, H., Zhang, H., Hao, Y., Gong, H., Essa, I.: Blt: Bidirectional layout transformer for controllable layout generation. ECCV (2022)
2022
Closest in time.
Lee, K., Chang, H., Jiang, L., Zhang, H., Tu, Z., Liu, C.: Vitgan: Training gans with vision transformers. ICLR (2022)
2022
Closest in time.
Li, G., Baechler, G., Tragut, M., Li, Y.: Learning to denoise raw mobile ui layouts for improving datasets at scale. CHI (2022)
2022
Closest in time.
Li, J., Li, D., Xiong, C., Hoi, S.: Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation. ICML (2022)
2022
Closest in time.
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. CVPR (2022)
2022
Closest in time.
Sauer, A., Schwarz, K., Geiger, A.: Stylegan-xl: Scaling stylegan to large diverse datasets. SIGGRAPH (2022)
2022
Closest in time.
Suvorov, R., Logacheva, E., Mashikhin, A., Remizova, A., Ashukha, A., Silvestrov, A., Kong, N., Goka, H., Park, K., Lempitsky, V.: Resolution-robust large mask inpainting with fourier convolutions. WACV (2022)
2022
Closest in time.
Wang, Y., Pu, G., Luo, W., Wang, Y., Xiong, P., Kang, H., Lian, Z.: Aesthetic text logo synthesis via content-aware layout inferring. CVPR (2022)
2022
Closest in time.
Yu, J., Li, X., Koh, J.Y., Zhang, H., Pang, R., Qin, J., Ku, A., Xu, Y., Baldridge, J., Wu, Y.: Vector-quantized image modeling with improved vqgan. ICLR (2022)
2022
Closest in time.
Zhou, M., Xu, C., Ma, Y., Ge, T., Jiang, Y., Xu, W.: Composition-aware graphic layout gan for visual-textual presentation designs. IJCAI (2022)
2022
Closest in time.
Cheng, C.Y., Huang, F., Li, G., Li, Y.: Play: Parametrically conditioned layout generation using latent diffusion. ICML (2023)
2023
Closest in time.
Horita, D., Inoue, N., Kikuchi, K., Yamaguchi, K., Aizawa, K.: Retrieval-augmented layout transformer for content-aware layout generation. arXiv (2023)
2023
Closest in time.
Hsu, H.Y., He, X., Peng, Y., Kong, H., Zhang, Q.: Posterlayout: A new benchmark and approach for content-aware visual-textual presentation layout. CVPR (2023)
2023
Closest in time.
Hui, M., Zhang, Z., Zhang, X., Xie, W., Wang, Y., Lu, Y.: Unifying layout generation with a decoupled diffusion model. CVPR (2023)
2023
Closest in time.
Inoue, N., Kikuchi, K., Simo-Serra, E., Otani, M., Yamaguchi, K.: Layoutdm: Discrete diffusion model for controllable layout generation. CVPR (2023)
2023
Closest in time.
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. CVPR (2023)
2023
Closest in time.
Levi, E., Brosh, E., Mykhailych, M., Perez, M.: Dlt: Conditioned layout generation with joint discrete-continuous diffusion layout transformer. ICCV (2023)
2023
Closest in time.
Li, Z., Li, F., Feng, W., Zhu, H., Liu, A., Li, Y., Zhang, Z., Lv, J., Zhu, X., Shen, J., et al.: Planning and rendering: Towards end-to-end product poster generation. arXiv (2023)
2023
Closest in time.
Lin, J., Guo, J., Sun, S., Xu, W., Liu, T., Lou, J.G., Zhang, D.: A parse-then-place approach for generating graphic layouts from textual descriptions. ICCV (2023)
2023
Closest in time.
Zhang, J., Guo, J., Sun, S., Lou, J.G., Zhang, D.: Layoutdiffusion: Improving graphic layout generation by discrete diffusion probabilistic models. ICCV (2023)
2023
Closest in time.
Feng, W., Zhu, W., Fu, T.j., Jampani, V., Akula, A., He, X., Basu, S., Wang, X.E., Wang, W.Y.: Layoutgpt: Compositional visual planning and generation with large language models. NeurIPS (2024)
2024
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Tang, Z., Wu, C., Li, J., Duan, N.: Layoutnuwa: Revealing the hidden layout expertise of large language models. ICLR (2024)
2024
Closest in time.