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The recent advancement of generative foundational models has ushered in a new era of image generation in the realm of natural images, revolutionizing art design, entertainment, environment simulation, and beyond.
X. Wang, L. Xie, C. Dong, and Y. Shan, “Real-esrgan: Training real-world blind super-resolution with pure synthetic data,” in Proceedings of the IEEE/CVF international conference on computer vision , 2021, pp. 1905–1914
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2015
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J. Sohl-Dickstein, E. Weiss, N. Maheswaranathan, and S. Ganguli, “Deep unsupervised learning using nonequilibrium thermodynamics,” in International conference on machine learning . PMLR, 2015, pp. 2256–2265
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C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the inception architecture for computer vision,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 2818–2826
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K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 770–778
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A. Van Den Oord, O. Vinyals et al. , “Neural discrete representation learning,” Advances in neural information processing systems , vol. 30, 2017
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M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, and S. Hochreiter, “Gans trained by a two time-scale update rule converge to a local nash equilibrium,” Advances in neural information processing systems , vol. 30, 2017
2017
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X. Wang, K. Yu, S. Wu, J. Gu, Y. Liu, C. Dong, Y. Qiao, and C. Change Loy, “Esrgan: Enhanced super-resolution generative adversarial networks,” in Proceedings of the European conference on computer vision (ECCV) workshops , 2018, pp. 0–0
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T. Karras, S. Laine, and T. Aila, “A style-based generator architecture for generative adversarial networks,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019, pp. 4401–4410
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I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial networks,” Communications of the ACM , vol. 63, no. 11, pp. 139–144, 2020
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J. Ho, A. Jain, and P. Abbeel, “Denoising diffusion probabilistic models,” Advances in neural information processing systems , vol. 33, pp. 6840–6851, 2020
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2020
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P. Esser, R. Rombach, and B. Ommer, “Taming transformers for high-resolution image synthesis,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2021, pp. 12 873–12 883
2021
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A. Q. Nichol and P. Dhariwal, “Improved denoising diffusion probabilistic models,” in International conference on machine learning . PMLR, 2021, pp. 8162–8171
2021
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A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark et al. , “Learning transferable visual models from natural language supervision,” in International conference on machine learning . PMLR, 2021, pp. 8748–8763
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R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High-resolution image synthesis with latent diffusion models,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2022, pp. 10 684–10 695
2022
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L. Chai, Y. Liu, W. Liu, G. Han, and S. He, “Crowdgan: Identity-free interactive crowd video generation and beyond,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 44, no. 6, pp. 2856–2871, 2022
2022
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M. Zhao, F. Bao, C. Li, and J. Zhu, “Egsde: Unpaired image-to-image translation via energy-guided stochastic differential equations,” Advances in Neural Information Processing Systems , vol. 35, pp. 3609–3623, 2022
2022
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Z. Wan, B. Zhang, D. Chen, P. Zhang, F. Wen, and J. Liao, “Old photo restoration via deep latent space translation,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 45, no. 2, pp. 2071–2087, 2022
2022
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O. Avrahami, D. Lischinski, and O. Fried, “Blended diffusion for text-driven editing of natural images,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 18 208–18 218
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2022
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J. Ho, C. Saharia, W. Chan, D. J. Fleet, M. Norouzi, and T. Salimans, “Cascaded diffusion models for high fidelity image generation,” Journal of Machine Learning Research , vol. 23, no. 47, pp. 1–33, 2022
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C. Saharia, J. Ho, W. Chan, T. Salimans, D. J. Fleet, and M. Norouzi, “Image super-resolution via iterative refinement,” IEEE transactions on pattern analysis and machine intelligence , vol. 45, no. 4, pp. 4713–4726, 2022
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C. Saharia, W. Chan, S. Saxena, L. Li, J. Whang, E. L. Denton, K. Ghasemipour, R. Gontijo Lopes, B. Karagol Ayan, T. Salimans et al. , “Photorealistic text-to-image diffusion models with deep language understanding,” Advances in neural information processing systems , vol. 35, pp. 36 479–36 494, 2022
2022
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K. He, X. Chen, S. Xie, Y. Li, P. Dollár, and R. Girshick, “Masked autoencoders are scalable vision learners,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2022, pp. 16 000–16 009
2022
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X. Sun, P. Wang, W. Lu, Z. Zhu, X. Lu, Q. He, J. Li, X. Rong, Z. Yang, H. Chang et al. , “Ringmo: A remote sensing foundation model with masked image modeling,” IEEE Transactions on Geoscience and Remote Sensing , 2022
2022
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Y. Cong, S. Khanna, C. Meng, P. Liu, E. Rozi, Y. He, M. Burke, D. Lobell, and S. Ermon, “Satmae: Pre-training transformers for temporal and multi-spectral satellite imagery,” Advances in Neural Information Processing Systems , vol. 35, pp. 197–211, 2022
C. Zhao, Y. Ogawa, S. Chen, Z. Yang, and Y. Sekimoto, “Label freedom: Stable diffusion for remote sensing image semantic segmentation data generation,” in 2023 IEEE International Conference on Big Data (BigData) . IEEE, 2023, pp. 1022–1030
2023
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F. Bastani, P. Wolters, R. Gupta, J. Ferdinando, and A. Kembhavi, “Satlaspretrain: A large-scale dataset for remote sensing image understanding,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 16 772–16 782
2023
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D. Wang, Q. Zhang, Y. Xu, J. Zhang, B. Du, D. Tao, and L. Zhang, “Advancing plain vision transformer toward remote sensing foundation model,” IEEE Transactions on Geoscience and Remote Sensing , vol. 61, pp. 1–15, 2023
2023
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C. J. Reed, R. Gupta, S. Li, S. Brockman, C. Funk, B. Clipp, K. Keutzer, S. Candido, M. Uyttendaele, and T. Darrell, “Scale-mae: A scale-aware masked autoencoder for multiscale geospatial representation learning,” in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) , October 2023, pp. 4088–4099
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2022
Cited alongside, same era.
C. Liu, R. Zhao, and Z. Shi, “Remote sensing image captioning based on multi-layer aggregated transformer,” IEEE Geoscience and Remote Sensing Letters , pp. 1–1, 2022
2022
Cited alongside, same era.
C. Liu, R. Zhao, H. Chen, Z. Zou, and Z. Shi, “Remote sensing image change captioning with dual-branch transformers: A new method and a large scale dataset,” IEEE Transactions on Geoscience and Remote Sensing , vol. 60, pp. 1–20, 2022
2022
Cited alongside, same era.
H. Li, Y. Yang, M. Chang, S. Chen, H. Feng, Z. Xu, Q. Li, and Y. Chen, “Srdiff: Single image super-resolution with diffusion probabilistic models,” Neurocomputing , vol. 479, pp. 47–59, 2022
2022
Cited alongside, same era.
J. Choi, J. Lee, C. Shin, S. Kim, H. Kim, and S. Yoon, “Perception prioritized training of diffusion models,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 11 472–11 481
2022
Cited alongside, same era.
F. Zhan, Y. Yu, R. Wu, J. Zhang, S. Lu, L. Liu, A. Kortylewski, C. Theobalt, and E. Xing, “Multimodal image synthesis and editing: The generative ai era,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 45, no. 12, pp. 15 098–15 119, 2023
2023
Cited alongside, same era.
H. Tang, P. H. Torr, and N. Sebe, “Multi-channel attention selection gans for guided image-to-image translation,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 45, no. 5, pp. 6055–6071, 2023
2023
Cited alongside, same era.
Y. Dalva, H. Pehlivan, O. I. Hatipoglu, C. Moran, and A. Dundar, “Image-to-image translation with disentangled latent vectors for face editing,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 45, no. 12, pp. 14 777–14 788, 2023
2023
Cited alongside, same era.
R. Yi, Y.-J. Liu, Y.-K. Lai, and P. L. Rosin, “Quality metric guided portrait line drawing generation from unpaired training data,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 45, no. 1, pp. 905–918, 2023
2023
Cited alongside, same era.
2023
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M. Mendieta, B. Han, X. Shi, Y. Zhu, and C. Chen, “Towards geospatial foundation models via continual pretraining,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 16 806–16 816
2023
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2023
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2023
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Y. Yuan, Y. Zhan, and Z. Xiong, “Parameter-efficient transfer learning for remote sensing image-text retrieval,” IEEE Transactions on Geoscience and Remote Sensing , 2023
2023
Later among the works it cites.
C. Liu, R. Zhao, J. Chen, Z. Qi, Z. Zou, and Z. Shi, “A decoupling paradigm with prompt learning for remote sensing image change captioning,” IEEE Transactions on Geoscience and Remote Sensing , 2023
2023
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2023
Later among the works it cites.
G. Sun, W. Liang, J. Dong, J. Li, Z. Ding, and Y. Cong, “Create your world: Lifelong text-to-image diffusion,” IEEE Transactions on Pattern Analysis and Machine Intelligence , pp. 1–17, 2024
2024
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J. Sun, Q. Deng, Q. Li, M. Sun, Y. Liu, and Z. Sun, “Anyface++: A unified framework for free-style text-to-face synthesis and manipulation,” IEEE Transactions on Pattern Analysis and Machine Intelligence , pp. 1–15, 2024
2024
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F.-T. Hong, L. Shen, and D. Xu, “Dagan++: Depth-aware generative adversarial network for talking head video generation,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 46, no. 5, pp. 2997–3012, 2024
2024
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B. Chen, L. Liu, C. Liu, Z. Zou, and Z. Shi, “Spectral-cascaded diffusion model for remote sensing image spectral super-resolution,” IEEE Transactions on Geoscience and Remote Sensing , 2024
2024
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D. Hong, B. Zhang, X. Li, Y. Li, C. Li, J. Yao, N. Yokoya, H. Li, P. Ghamisi, X. Jia et al. , “Spectralgpt: Spectral remote sensing foundation model,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2024
2024
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F. Liu, D. Chen, Z. Guan, X. Zhou, J. Zhu, Q. Ye, L. Fu, and J. Zhou, “Remoteclip: A vision language foundation model for remote sensing,” IEEE Transactions on Geoscience and Remote Sensing , 2024
2024
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C. Liu, K. Chen, B. Chen, H. Zhang, Z. Zou, and Z. Shi, “Rscama: Remote sensing image change captioning with state space model,” IEEE Geoscience and Remote Sensing Letters , 2024
2024
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Z. Wang, R. Prabha, T. Huang, J. Wu, and R. Rajagopal, “Skyscript: A large and semantically diverse vision-language dataset for remote sensing,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 38, no. 6, 2024, pp. 5805–5813
2024
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