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The field of image synthesis has made tremendous strides forward in the last years.
Canny, J.: A computational approach to edge detection. IEEE Transactions on pattern analysis and machine intelligence (6), 679–698 (1986)
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McCloskey, M., Cohen, N.J.: Catastrophic Interference in Connectionist Networks: The Sequential Learning Problem. vol. 24, pp. 109–165. Academic Press (1989). https://doi.org/https://doi.org/10.1016/S0079-7421(08)60536-8, https://www.sciencedirect.com/science/article/pii/S0079742108605368
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Wah, C., Branson, S., Welinder, P., Perona, P., Belongie, S.: The caltech-ucsd birds-200-2011 dataset (2011)
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Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. In: Advances in neural information processing systems. pp. 2672–2680 (2014)
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Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: Microsoft coco: Common objects in context. In: Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13. pp. 740–755. Springer (2014)
2014
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Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M.: Imagenet large scale visual recognition challenge. International journal of computer vision 115
2015
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Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: International Conference on Machine Learning. pp. 2256–2265. PMLR (2015)
2015
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Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks (2016)
2016
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Reed, S., Akata, Z., Yan, X., Logeswaran, L., Schiele, B., Lee, H.: Generative adversarial text to image synthesis. In: International conference on machine learning. pp. 1060–1069. PMLR (2016)
2016
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Reed, S.E., Akata, Z., Mohan, S., Tenka, S., Schiele, B., Lee, H.: Learning what and where to draw. Advances in neural information processing systems 29
2016
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Cao, Z., Simon, T., Wei, S.E., Sheikh, Y.: Realtime multi-person 2d pose estimation using part affinity fields. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 7291–7299 (2017)
2017
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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. Advances in neural information processing systems 30
2017
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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)
2017
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2017
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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)
2017
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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)
2017
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Caesar, H., Uijlings, J., Ferrari, V.: Coco-stuff: Thing and stuff classes in context. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 1209–1218 (2018)
2018
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Murez, Z., Kolouri, S., Kriegman, D., Ramamoorthi, R., Kim, K.: Image to image translation for domain adaptation. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 4500–4509 (2018)
2018
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Rebuffi, S.A., Bilen, H., Vedaldi, A.: Efficient Parametrization of Multi-Domain Deep Neural Networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)
2018
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Rosenfeld, A., Tsotsos, J.K.: Incremental Learning Through Deep Adaptation. IEEE Transactions on Pattern Analysis and Machine Intelligence 42
2018
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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)
2018
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Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 586–595 (2018)
2018
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Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423, https://aclanthology.org/N19-1423
2019
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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)
2019
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Stickland, A.C., Murray, I.: BERT and PALs: Projected Attention Layers for Efficient Adaptation in Multi-Task Learning. In: Chaudhuri, K., Salakhutdinov, R. (eds.) Proceedings of the 36th International Conference on Machine Learning. vol. 97, pp. 5986–5995. PMLR (2019), https://proceedings.mlr.press/v97/stickland19a.html
2019
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Wang, W., Guo, R., Tian, Y., Yang, W.: Cfsnet: Toward a controllable feature space for image restoration. In: Proceedings of the IEEE/CVF international conference on computer vision. pp. 4140–4149 (2019)
2019
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Zhu, M., Pan, P., Chen, W., Yang, Y.: Dm-gan: Dynamic memory generative adversarial networks for text-to-image synthesis. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 5802–5810 (2019)
2019
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Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. Advances in neural information processing systems 33
2020
Earlier work this paper cites.
Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J., Aila, T.: Analyzing and improving the image quality of stylegan. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 8110–8119 (2020)
2020
Earlier work this paper cites.
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., Liu, P.J.: Exploring the limits of transfer learning with a unified text-to-text transformer. The Journal of Machine Learning Research 21
2020
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Ranftl, R., Lasinger, K., Hafner, D., Schindler, K., Koltun, V.: Towards robust monocular depth estimation: Mixing datasets for zero-shot cross-dataset transfer. IEEE transactions on pattern analysis and machine intelligence 44
2020
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2020
Earlier work this paper cites.
Tang, H., Bai, S., Zhang, L., Torr, P.H., Sebe, N.: Xinggan for person image generation. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXV 16. pp. 717–734. Springer (2020)
2020
Cited alongside, same era.
Dhariwal, P., Nichol, A.: Diffusion models beat gans on image synthesis. Advances in neural information processing systems 34
2021
Cited alongside, same era.
Ding, M., Yang, Z., Hong, W., Zheng, W., Zhou, C., Yin, D., Lin, J., Zou, X., Shao, Z., Yang, H., Tang, J.: CogView: Mastering Text-to-Image Generation via Transformers. In: Ranzato, M., Beygelzimer, A., Dauphin, Y., Liang, P.S., Vaughan, J.W. (eds.) Advances in Neural Information Processing Systems. vol. 34, pp. 19822–19835. Curran Associates, Inc. (2021), https://proceedings.neurips.cc/paper_files/paper/2021/file/a4d92e2cd541fca87e4620aba658316d-Paper.pdf
2021
Cited alongside, same era.
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)
Midjourney (2023), https://www.midjourney.com/
2023
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Betker, J., Goh, G., Jing, L., Brooks, T., Wang, J., Li, L., Ouyang, L., Zhuang, J., Lee, J., Guo, Y., Manassra, W., Dhariwal, P., Chu, C., Jiao, Y., Ramesh, A.: Improving Image Generation with Better Captions (2023)
2023
Closest in time.
Brooks, T., Holynski, A., Efros, A.A.: Instructpix2pix: Learning to follow image editing instructions. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 18392–18402 (2023)
2023
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2023
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2021
Cited alongside, same era.
2021
Cited alongside, same era.
Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: LoRA: Low-Rank Adaptation of Large Language Models (2021)
2021
Cited alongside, same era.
Karras, T., Aittala, M., Laine, S., Härkönen, E., Hellsten, J., Lehtinen, J., Aila, T.: Alias-free generative adversarial networks. Advances in Neural Information Processing Systems 34
2021
Cited alongside, same era.
Kingma, D., Salimans, T., Poole, B., Ho, J.: Variational diffusion models. Advances in neural information processing systems 34
2021
Cited alongside, same era.
Liu, L., Chen, J., Wu, H., Li, G., Li, C., Lin, L.: Cross-modal collaborative representation learning and a large-scale rgbt benchmark for crowd counting. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 4823–4833 (2021)
2021
Cited alongside, same era.
2021
Cited alongside, same era.
Nichol, A.Q., Dhariwal, P.: Improved denoising diffusion probabilistic models. In: International Conference on Machine Learning. pp. 8162–8171. PMLR (2021)
2021
Cited alongside, same era.
Pfeiffer, J., Kamath, A., Ruckl, A., Cho, K., Gurevych1, I.: AdapterFusion: Non-Destructive Task Composition for Transfer Learning. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics. pp. 487–503. ACL (2021)
2021
Cited alongside, same era.
2023
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2023
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Hu, M., Zheng, J., Liu, D., Zheng, C., Wang, C., Tao, D., Cham, T.J.: Cocktail: Mixing Multi-Modality Control for Text-Conditional Image Generation. In: Thirty-seventh Conference on Neural Information Processing Systems (2023)
2023
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2023
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Kang, M., Zhu, J.Y., Zhang, R., Park, J., Shechtman, E., Paris, S., Park, T.: Scaling up gans for text-to-image synthesis. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 10124–10134 (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)
2023
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2023
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Podell, D., English, Z., Lacey, K., Blattmann, A., Dockhorn, T., Müller, J., Penna, J., Rombach, R.: SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis (2023)
2023
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2023
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Ruiz, N., Li, Y., Jampani, V., Pritch, Y., Rubinstein, M., Aberman, K.: Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 22500–22510 (2023)
2023
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2023
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Xia, Y., Monica, J., Chao, W.L., Hariharan, B., Weinberger, K.Q., Campbell, M.: Image-to-Image Translation for Autonomous Driving from Coarsely-Aligned Image Pairs. In: 2023 IEEE International Conference on Robotics and Automation (ICRA). pp. 7756–7762. IEEE (2023)
2023
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Xu, X., Wang, Z., Zhang, G., Wang, K., Shi, H.: Versatile Diffusion: Text, Images and Variations All in One Diffusion Model. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). pp. 7754–7765 (2023)
2023
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Yang, B., Gu, S., Zhang, B., Zhang, T., Chen, X., Sun, X., Chen, D., Wen, F.: Paint by example: Exemplar-based image editing with diffusion models. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 18381–18391 (2023)
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 (ICCV). pp. 3836–3847 (2023)
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Chen, M., Laina, I., Vedaldi, A.: Training-free layout control with cross-attention guidance. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. pp. 5343–5353 (2024)
2024
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Zhao, S., Chen, D., Chen, Y.C., Bao, J., Hao, S., Yuan, L., Wong, K.Y.K.: Uni-controlnet: All-in-one control to text-to-image diffusion models. Advances in Neural Information Processing Systems 36
2024
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