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Image inpainting, the process of restoring corrupted images, has seen significant advancements with the advent of diffusion models (DMs).
Bertalmio, M., Sapiro, G., Caselles, V., Ballester, C.: Image inpainting. In: International Conference and Exhibition on Computer Graphics and Interactive Techniques (SIGGRAPH). pp. 417–424 (2000)
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Criminisi, A., Pérez, P., Toyama, K.: Region filling and object removal by exemplar-based image inpainting. IEEE Transactions on Image Processing 13
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Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 248–255. Ieee (2009)
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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: European Conference on Computer Vision (ECCV). pp. 740–755. Springer (2014)
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Liu, Z., Luo, P., Wang, X., Tang, X.: Deep learning face attributes in the wild. In: IEEE/CVF International Conference on Computer Vision (ICCV) (December 2015)
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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 (NIPS) 30
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Huang, H., He, R., Sun, Z., Tan, T., et al.: Introvae: Introspective variational autoencoders for photographic image synthesis. Advances in Neural Information Processing Systems (NIPS) 31
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/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 586–595 (2018)
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Zheng, C., Cham, T.J., Cai, J.: Pluralistic image completion. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 1438–1447 (2019)
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Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. Advances in Neural Information Processing Systems (NIPS) 33
2020
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Kuznetsova, A., Rom, H., Alldrin, N., Uijlings, J., Krasin, I., Pont-Tuset, J., Kamali, S., Popov, S., Malloci, M., Kolesnikov, A., et al.: The open images dataset v4: Unified image classification, object detection, and visual relationship detection at scale. International Journal of Computer Vision (IJCV) 128
2020
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2020
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Liu, H., Wan, Z., Huang, W., Song, Y., Han, X., Liao, J.: Pd-GAN: Probabilistic diverse GAN for image inpainting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 9371–9381 (2021)
2021
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Peng, J., Liu, D., Xu, S., Li, H.: Generating diverse structure for image inpainting with hierarchical vq-vae. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 10775–10784 (2021)
2021
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Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., Krueger, G., Sutskever, I.: Learning transferable visual models from natural language supervision. In: International Conference on Machine Learning (ICML). pp. 8748–8763. PMLR (2021)
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2021
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2021
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Avrahami, O., Lischinski, D., Fried, O.: Blended diffusion for text-driven editing of natural images. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 18208–18218 (2022)
2022
Cited alongside, same era.
Ho, J., Salimans, T.: Classifier-free diffusion guidance. arXiv preprint arXiv:2207.12598 (2022)
2022
Cited alongside, same era.
Lugmayr, A., Danelljan, M., Romero, A., Yu, F., Timofte, R., Van Gool, L.: RePaint: Inpainting using denoising diffusion probabilistic models. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 11461–11471 (2022)
2022
Cited alongside, same era.
Lykon: Dreamshaper (2022), https://civitai.com/models/4384?modelVersionId=128713
2022
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2023
Later among the works it cites.
Xie, S., Zhang, Z., Lin, Z., Hinz, T., Zhang, K.: Smartbrush: Text and shape guided object inpainting with diffusion model. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 22428–22437 (2023)
2023
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2023
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Xu, J., Liu, X., Wu, Y., Tong, Y., Li, Q., Ding, M., Tang, J., Dong, Y.: Imagereward: Learning and evaluating human preferences for text-to-image generation (2023)
2023
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von Platen, P., Patil, S., Lozhkov, A., Cuenca, P., Lambert, N., Rasul, K., Davaadorj, M., Wolf, T.: Diffusers: State-of-the-art diffusion models. https://github.com/huggingface/diffusers (2022)
2022
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Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 10684–10695 (June 2022)
2022
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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 (NIPS) 35
2022
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Zheng, H., Lin, Z., Lu, J., Cohen, S., Shechtman, E., Barnes, C., Zhang, J., Xu, N., Amirghodsi, S., Luo, J.: Image inpainting with cascaded modulation GAN and object-aware training. In: European Conference on Computer Vision (ECCV). pp. 277–296. Springer (2022)
2022
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Avrahami, O., Fried, O., Lischinski, D.: Blended latent diffusion. ACM transactions on graphics (TOG) 42
2023
Cited alongside, same era.
Binghui, C., Chao, L., Chongyang, Z., Wangmeng, X., Yifeng, G., Xuansong, X.: Replaceanything as you want: Ultra-high quality content replacement (2023), https://aigcdesigngroup.github.io/replace-anything/
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epinikion: epicrealism (2023), https://civitai.com/models/25694?modelVersionId=143906
2023
Cited alongside, same era.
2023
Cited alongside, same era.
Xu, Z., Zhang, X., Chen, W., Yao, M., Liu, J., Xu, T., Wang, Z.: A review of image inpainting methods based on deep learning. Applied Sciences 13
2023
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Yang, S., Chen, X., Liao, J.: Uni-paint: A unified framework for multimodal image inpainting with pretrained diffusion model. In: ACM International Conference on Multimedia (MM). pp. 3190–3199 (2023)
2023
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2023
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2023
Later among the works it cites.
Zhang, G., Ji, J., Zhang, Y., Yu, M., Jaakkola, T., Chang, S.: Towards coherent image inpainting using denoising diffusion implicit models (2023)
2023
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Zhang, L., Rao, A., Agrawala, M.: Adding conditional control to text-to-image diffusion models. In: IEEE/CVF International Conference on Computer Vision (ICCV) (2023)
2023
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2023
Later among the works it cites.
Corneanu, C., Gadde, R., Martinez, A.M.: Latentpaint: Image inpainting in latent space with diffusion models. In: IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). pp. 4334–4343 (2024)
2024
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heni29833: Henmixreal (2024), https://civitai.com/models/20282?modelVersionId=305687
2024
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2024
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Quan, W., Chen, J., Liu, Y., Yan, D.M., Wonka, P.: Deep learning-based image and video inpainting: A survey. International Journal of Computer Vision (IJCV) pp. 1–34 (2024)
2024
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2024
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Wikipedia contributors: Mean squared error — Wikipedia, the free encyclopedia (2024), https://en.wikipedia.org/w/index.php?title=Mean_squared_error&oldid=1207422018 , [Online; accessed 4-March-2024]
2024
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Wikipedia contributors: Peak signal-to-noise ratio — Wikipedia, the free encyclopedia (2024), https://en.wikipedia.org/w/index.php?title=Peak_signal-to-noise_ratio&oldid=1210897995 , [Online; accessed 4-March-2024]
2024
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