[29] Alex Nichol, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam, Pamela Mishkin, Bob McGrew, Ilya Sutskever, and Mark Chen. Glide: Towards photorealistic image generation and editing with text-guided diffusion models. arXiv Preprint, 2021.
[30] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. In Conference on Computer Vision and Pattern Recognition, 2022.
[31] Christoph Schuhmann, Romain Beaumont, Richard Vencu, Cade Gordon, Ross Wightman, Mehdi Cherti, Theo Coombes, Aarush Katta, Clayton Mullis, Mitchell Wortsman, et al. Laion-5b: An open large-scale dataset for training next generation image-text models. Advances in Neural Information Processing Systems, 2022.
[32] Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. A simple framework for contrastive learning of visual representations. In International Conference on Machine Learning, 2020.
[33] Ron Mokady, Amir Hertz, Kfir Aberman, Yael Pritch, and Daniel Cohen-Or. Null-text inversion for editing real images using guided diffusion models. Proceedings of the IEEE Conf. on Computer Vision and Pattern Recognition, 2022.
[34] Rinon Gal, Yuval Alaluf, Yuval Atzmon, Or Patashnik, Amit H Bermano, Gal Chechik, and Daniel Cohen-Or. An image is worth one word: Personalizing text-to-image generation using textual inversion. arXiv Preprint, 2022.
[35] Zhengcong Fei, Mingyuan Fan, and Junshi Huang. Gradient-free textual inversion. arXiv Preprint, 2023.
[36] Anton Voronov, Mikhail Khoroshikh, Artem Babenko, and Max Ryabinin. Is This Loss Informative? Speeding Up Textual Inversion with Deterministic Objective Evaluation. arXiv Preprint, 2023.
[37] Catherine Wah, Steve Branson, Peter Welinder, Pietro Perona, and Serge Belongie. The Caltech-UCSD Birds-200-2011 Dataset. Technical report, California Institute of Technology, 2011.
[38] Bruce D Lucas and Takeo Kanade. An Iterative Image Registration Technique With an Application to Stereo Vision. In International Joint Conference on Artificial Intelligence, 1981.
[39] Sameer Agarwal, Yasutaka Furukawa, Noah Snavely, Ian Simon, Brian Curless, Steven M. Seitz, and Rick Szeliski. Building rome in a day. Communications of the ACM, 2011.
[40] Jan-Michael Frahm, Pierre Fite-Georgel, David Gallup, Tim Johnson, Rahul Raguram, Changchang Wu, Yi-Hung Jen, Enrique Dunn, Brian Clipp, Svetlana Lazebnik, and Marc Pollefeys. Building rome on a cloudless day. In Kostas Daniilidis, Petros Maragos, and Nikos Paragios, editors, European Conference on Computer Vision, 2010.
[41] Ondrej Chum, Tomas Werner, and Jiri Matas. Two-View Geometry Estimation Unaffected by a Dominant Plane. In Conference on Computer Vision and Pattern Recognition, 2005.
[42] Kwang Moo Yi, Eduard Trulls, Yuki Ono, Vincent Lepetit, Mathieu Salzmann, and Pascal Fua. Learning to Find Good Correspondences. In Conference on Computer Vision and Pattern Recognition, 2018.
[43] Weiwei Sun, Wei Jiang, Andrea Tagliasacchi, Eduard Trulls, and Kwang Moo Yi. Attentive context normalization for robust permutation-equivariant learning. In Proceedings of the IEEE Conf. on Computer Vision and Pattern Recognition, 2020.
[44] Prune Truong, Martin Danelljan, and Radu Timofte. GLU-Net: Global-local universal network for dense flow and correspondences. In Conference on Computer Vision and Pattern Recognition, 2020.
[45] Prune Truong, Martin Danelljan, Luc Van Gool, and Radu Timofte. GOCor: Bringing globally optimized correspondence volumes into your neural network. In Advances in Neural Information Processing Systems, 2020.