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Inversion methods, such as Textual Inversion, generate personalized images by incorporating concepts of interest provided by user images.
Multi-concept customization of text-to-image diffusion
Kumari, N.; Zhang, B.; Zhang, R.; Shechtman, E.; and Zhu, J.-Y. 2023 · 1941
Earlier work this paper cites.
Emergence of simple-cell receptive field properties by learning a sparse code for natural images
Olshausen, B. A.; and Field, D. J. 1996 · 1996
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Deng, J.; Dong, W.; Socher, R.; Li, L.-J.; Li, K.; and Fei-Fei, L. 2009 · 2009
Earlier work this paper cites.
Coached active learning for interactive video search
Wei, X.-Y.; and Yang, Z.-Q. 2011 · 2011
Earlier work this paper cites.
Coaching the exploration and exploitation in active learning for interactive video retrieval
Wei, X.-Y.; and Yang, Z.-Q. 2012 · 2012
Earlier work this paper cites.
Generative adversarial nets
Goodfellow, I.; Pouget-Abadie, J.; Mirza, M.; Xu, B.; Warde-Farley, D.; Ozair, S.; Courville, A.; and Bengio, Y. 2014 · 2014
Earlier work this paper cites.
Auto-Encoding Variational Bayes
Kingma, D. P.; and Welling, M. 2014 · 2014
Earlier work this paper cites.
Microsoft coco: Common objects in context
Lin, T.-Y.; Maire, M.; Belongie, S.; Hays, J.; Perona, P.; Ramanan, D.; Dollár, P.; and Zitnick, C. L. 2014 · 2014
Earlier work this paper cites.
Deep residual learning for image recognition
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016 · 2016
Earlier work this paper cites.
A style-based generator architecture for generative adversarial networks
Karras, T.; Laine, S.; and Aila, T. 2019 · 2019
Earlier work this paper cites.
Dm-gan: Dynamic memory generative adversarial networks for text-to-image synthesis
Zhu, M.; Pan, P.; Chen, W.; and Yang, Y. 2019 · 2019
Earlier work this paper cites.
End-to-end object detection with transformers
Carion, N.; Massa, F.; Synnaeve, G.; Usunier, N.; Kirillov, A.; and Zagoruyko, S. 2020 · 2020
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Denoising diffusion probabilistic models
Ho, J.; Jain, A.; and Abbeel, P. 2020 · 2020
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Denoising Diffusion Implicit Models
Song, J.; Meng, C.; and Ermon, S. 2020 · 2020
Cited alongside, same era.
Learning transferable visual models from natural language supervision
Radford, A.; Kim, J. W.; Hallacy, C.; Ramesh, A.; Goh, G.; Agarwal, S.; Sastry, G.; Askell, A.; Mishkin, P.; Clark, J.; et al. 2021 · 2021
Cited alongside, same era.
Zero-shot text-to-image generation
Ramesh, A.; Pavlov, M.; Goh, G.; Gray, S.; Voss, C.; Radford, A.; Chen, M.; and Sutskever, I. 2021 · 2021
Cited alongside, same era.
An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion
Gal, R.; Alaluf, Y.; Atzmon, Y.; Patashnik, O.; Bermano, A. H.; Chechik, G.; and Cohen-or, D. 2022 · 2022
DF-GAN: A Simple and Effective Baseline for Text-to-Image Synthesis
Tao, M.; Tang, H.; Wu, F.; Jing, X.-Y.; Bao, B.-K.; and Xu, C. 2022 · 2022
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Training-free layout control with cross-attention guidance
Chen, M.; Laina, I.; and Vedaldi, A. 2023 · 2023
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Svdiff: Compact parameter space for diffusion fine-tuning
Han, L.; Li, Y.; Zhang, H.; Milanfar, P.; Metaxas, D.; and Yang, F. 2023 · 2023
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Gligen: Open-set grounded text-to-image generation
Li, Y.; Liu, H.; Wu, Q.; Mu, F.; Yang, J.; Gao, J.; Li, C.; and Lee, Y. J. 2023 · 2023
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Cones: Concept neurons in diffusion models for customized generation
Liu, Z.; Feng, R.; Zhu, K.; Zhang, Y.; Zheng, K.; Liu, Y.; Zhao, D.; Zhou, J.; and Cao, Y. 2023 · 2023
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Prompt-to-Prompt Image Editing with Cross-Attention Control
Hertz, A.; Mokady, R.; Tenenbaum, J.; Aberman, K.; Pritch, Y.; and Cohen-or, D. 2022 · 2022
Cited alongside, same era.
Hierarchical text-conditional image generation with clip latents
Ramesh, A.; Dhariwal, P.; Nichol, A.; Chu, C.; and Chen, M. 2022 · 2022
Cited alongside, same era.
High-resolution image synthesis with latent diffusion models
Rombach, R.; Blattmann, A.; Lorenz, D.; Esser, P.; and Ommer, B. 2022 · 2022
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Photorealistic text-to-image diffusion models with deep language understanding
Saharia, C.; Chan, W.; Saxena, S.; Li, L.; Whang, J.; Denton, E. L.; Ghasemipour, K.; Gontijo Lopes, R.; Karagol Ayan, B.; Salimans, T.; et al. 2022 · 2022
Cited alongside, same era.
Laion-5b: An open large-scale dataset for training next generation image-text models
Schuhmann, C.; Beaumont, R.; Vencu, R.; Gordon, C.; Wightman, R.; Cherti, M.; Coombes, T.; Katta, A.; Mullis, C.; Wortsman, M.; et al. 2022 · 2022
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Zero-shot image-to-image translation
Parmar, G.; Kumar Singh, K.; Zhang, R.; Li, Y.; Lu, J.; and Zhu, J.-Y. 2023 · 2023
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Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation
Ruiz, N.; Li, Y.; Jampani, V.; Pritch, Y.; Rubinstein, M.; and Aberman, K. 2023 · 2023
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Key-locked rank one editing for text-to-image personalization
Tewel, Y.; Gal, R.; Chechik, G.; and Atzmon, Y. 2023 · 2023
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Reco: Region-controlled text-to-image generation
Yang, Z.; Wang, J.; Gan, Z.; Li, L.; Lin, K.; Wu, C.; Duan, N.; Liu, Z.; Liu, C.; Zeng, M.; et al. 2023 · 2023
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Adding conditional control to text-to-image diffusion models
Zhang, L.; Rao, A.; and Agrawala, M. 2023 · 2023
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