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Given a text and an image of a specific subject, text-to-image customization aims to generate new images that align with both the text and the subject's appearance.
N. Kumari, B. Zhang, R. Zhang, E. Shechtman, and J.-Y. Zhu, “Multi-concept customization of text-to-image diffusion,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 1931–1941
1941
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1975
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Y. Bengio, J. Louradour, R. Collobert, and J. Weston, “Curriculum learning,” in Proceedings of the 26th annual international conference on machine learning , 2009, pp. 41–48
2009
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2014
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O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18 . Springer, 2015, pp. 234–241
2015
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Y. Wei, X. Liang, Y. Chen, X. Shen, M.-M. Cheng, J. Feng, Y. Zhao, and S. Yan, “Stc: A simple to complex framework for weakly-supervised semantic segmentation,” IEEE transactions on pattern analysis and machine intelligence , vol. 39, no. 11, pp. 2314–2320, 2016
2016
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R. Tudor Ionescu, B. Alexe, M. Leordeanu, M. Popescu, D. P. Papadopoulos, and V. Ferrari, “How hard can it be? estimating the difficulty of visual search in an image,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2016, pp. 2157–2166
2016
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2016
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H. Zhang, T. Xu, H. Li, S. Zhang, X. Wang, X. Huang, and D. N. Metaxas, “Stackgan++: Realistic image synthesis with stacked generative adversarial networks,” IEEE transactions on pattern analysis and machine intelligence , vol. 41, no. 8, pp. 1947–1962, 2018
2018
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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
2019
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T. Matiisen, A. Oliver, T. Cohen, and J. Schulman, “Teacher–student curriculum learning,” IEEE transactions on neural networks and learning systems , vol. 31, no. 9, pp. 3732–3740, 2019
2019
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T. Hinz, S. Heinrich, and S. Wermter, “Semantic object accuracy for generative text-to-image synthesis,” IEEE transactions on pattern analysis and machine intelligence , vol. 44, no. 3, pp. 1552–1565, 2020
2020
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A. Kuznetsova, H. Rom, N. Alldrin, J. Uijlings, I. Krasin, J. Pont-Tuset, S. Kamali, S. Popov, M. Malloci, A. Kolesnikov et al. , “The open images dataset v4: Unified image classification, object detection, and visual relationship detection at scale,” International Journal of Computer Vision , vol. 128, no. 7, pp. 1956–1981, 2020
2020
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C. Raffel, N. Shazeer, A. Roberts, K. Lee, S. Narang, M. Matena, Y. Zhou, W. Li, and P. J. Liu, “Exploring the limits of transfer learning with a unified text-to-text transformer,” Journal of machine learning research , vol. 21, no. 140, pp. 1–67, 2020
2020
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2020
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A. Ramesh, M. Pavlov, G. Goh, S. Gray, C. Voss, A. Radford, M. Chen, and I. Sutskever, “Zero-shot text-to-image generation,” in International conference on machine learning . Pmlr, 2021, pp. 8821–8831
2021
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2021
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X. Wang, Y. Chen, and W. Zhu, “A survey on curriculum learning,” IEEE transactions on pattern analysis and machine intelligence , vol. 44, no. 9, pp. 4555–4576, 2021
2021
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B. Xu, Q. Wang, Y. Lyu, Y. Zhu, and Z. Mao, “Entity structure within and throughout: Modeling mention dependencies for document-level relation extraction,” in Proceedings of the AAAI conference on artificial intelligence , vol. 35, no. 16, 2021, pp. 14 149–14 157
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
2021
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M. Caron, H. Touvron, I. Misra, H. Jégou, J. Mairal, P. Bojanowski, and A. Joulin, “Emerging properties in self-supervised vision transformers,” in Proceedings of the IEEE/CVF international conference on computer vision , 2021, pp. 9650–9660
2021
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2022
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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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2022
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2022
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C. Schuhmann, R. Beaumont, R. Vencu, C. Gordon, R. Wightman, M. Cherti, T. Coombes, A. Katta, C. Mullis, M. Wortsman et al. , “Laion-5b: An open large-scale dataset for training next generation image-text models,” Advances in Neural Information Processing Systems , vol. 35, pp. 25 278–25 294, 2022
2022
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J. Ho and T. Salimans, “Classifier-free diffusion guidance,” arXiv preprint arXiv:2207.12598 , 2022
2022
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2023
Later among the works it cites.
X. Zhai, B. Mustafa, A. Kolesnikov, and L. Beyer, “Sigmoid loss for language image pre-training,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 11 975–11 986
2023
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X. Yu, M. Xu, Y. Zhang, H. Liu, C. Ye, Y. Wu, Z. Yan, C. Zhu, Z. Xiong, T. Liang et al. , “Mvimgnet: A large-scale dataset of multi-view images,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2023, pp. 9150–9161
2023
Later among the works it cites.
2023
Later among the works it cites.
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F.-A. Croitoru, V. Hondru, R. T. Ionescu, and M. Shah, “Diffusion models in vision: A survey,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 45, no. 9, pp. 10 850–10 869, 2023
2023
Cited alongside, same era.
N. Ruiz, Y. Li, V. Jampani, Y. Pritch, M. Rubinstein, and K. Aberman, “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 , 2023, pp. 22 500–22 510
2023
Cited alongside, same era.
L. Han, Y. Li, H. Zhang, P. Milanfar, D. Metaxas, and F. Yang, “Svdiff: Compact parameter space for diffusion fine-tuning,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 7323–7334
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
Y. Wei, Y. Zhang, Z. Ji, J. Bai, L. Zhang, and W. Zuo, “Elite: Encoding visual concepts into textual embeddings for customized text-to-image generation,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 15 943–15 953
2023
Cited alongside, same era.
2023
Cited alongside, same era.
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 , 2024
2024
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J. Nam, H. Kim, D. Lee, S. Jin, S. Kim, and S. Chang, “Dreammatcher: Appearance matching self-attention for semantically-consistent text-to-image personalization,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 8100–8110
2024
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D. Li, J. Li, and S. Hoi, “Blip-diffusion: Pre-trained subject representation for controllable text-to-image generation and editing,” Advances in Neural Information Processing Systems , vol. 36, 2024
2024
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Z. Li, M. Cao, X. Wang, Z. Qi, M.-M. Cheng, and Y. Shan, “Photomaker: Customizing realistic human photos via stacked id embedding,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 8640–8650
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M. Huang, Z. Mao, M. Liu, Q. He, and Y. Zhang, “Realcustom: Narrowing real text word for real-time open-domain text-to-image customization,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 7476–7485
2024
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Y. Gu, X. Wang, J. Z. Wu, Y. Shi, Y. Chen, Z. Fan, W. Xiao, R. Zhao, S. Chang, W. Wu et al. , “Mix-of-show: Decentralized low-rank adaptation for multi-concept customization of diffusion models,” Advances in Neural Information Processing Systems , vol. 36, 2024
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Y. Wang, Y. Yue, R. Lu, Y. Han, S. Song, and G. Huang, “Efficienttrain++: Generalized curriculum learning for efficient visual backbone training,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2024
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S. Wu, T. Zhou, Y. Du, J. Yu, B. Han, and T. Liu, “A time-consistency curriculum for learning from instance-dependent noisy labels,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2024
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Y. Zhang, Y. Song, J. Liu, R. Wang, J. Yu, H. Tang, H. Li, X. Tang, Y. Hu, H. Pan et al. , “Ssr-encoder: Encoding selective subject representation for subject-driven generation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 8069–8078
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