[1] 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, pp. 770-778, 2016.
[2] O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, et al., "Imagenet large scale visual recognition challenge," International journal of computer vision, vol. 115, pp. 211-252, 2015.
[3] M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benenson, U. Franke, S. Roth, and B. Schiele, “The cityscapes dataset for semantic urban scene understanding,” in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 3213–3223, 2016.
[4] T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollar, and C. L. Zitnick, "Microsoft coco: Common objects in context," in Computer Vision-ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13, pp. 740-755, Springer, 2014.
[5] D. Hoiem, S. K. Divvala, and J. H. Hays, “Pascal voc 2008 challenge,” World Literature Today, vol. 24, 2009.
[6] Z. Liu, Z. Miao, X. Zhan, J. Wang, B. Gong, and S. X. Yu, "Large-scale long-tailed recognition in an open world," in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2537-2546, 2019.
[7] I. Misra, A. Gupta, and M. Hebert, “From red wine to red tomato: Composition with context,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1792–1801, 2017.
[8] C. Sakaridis, D. Dai, and L. Van Gool, "Semantic foggy scene understanding with synthetic data," International Journal of Computer Vision, vol. 126, pp. 973-992, 2018.
[9] X. Peng, Q. Bai, X. Xia, Z. Huang, K. Saenko, and B. Wang, "Moment matching for multi-source domain adaptation," in Proceedings of the IEEE/CVF international conference on computer vision, pp. 1406-1415, 2019.
[10] D. Hendrycks and T. Dietterich, "Benchmarking neural network robustness to common corruptions and perturbations," arXiv preprint arXiv:1903.12261, 2019.
[11] B. Recht, R. Roelofs, L. Schmidt, and V. Shankar, “DoImagenet classifiers generalize toImagenet?,” in International conference on machine learning, pp. 5389–5400, PMLR, 2019.
[12] P. Kattakinda, A. Levine, and S. Feizi, "Invariant learning via diffusion dreamed distribution shifts," arXiv preprint arXiv:2211.10370, 2022.
[13] P. W. Koh, S. Sagawa, H. Marklund, S. M. Xie, M. Zhang, A. Balsubramani, W. Hu, M. Yasunaga, R. L. Phillips, I. Gao, et al., "Wilds: A benchmark of in-the-wild distribution shifts," in International Conference on Machine Learning, pp. 5637-5664, PMLR, 2021.
[14] J. Pearl, Causality. Cambridge university press, 2009.
[15] 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, pp. 8821–8831, PMLR, 2021.
[16] 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, pp. 10684-10695, 2022.
[17] B. Kawar, S. Zada, O. Lang, O. Tov, H. Chang, T. Dekel, I. Mosseri, and M. Irani, "Imagic: Text-based real image editing with diffusion models," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2023.
[18] T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, et al., "Language models are few-shot learners," Advances in neural information processing systems, vol. 33, pp. 1877-1901, 2020.
[19] A. Hertz, R. Mokady, J. Tenenbaum, K. Aberman, Y. Pritch, and D. Cohen-Or, “Prompt-to-prompt image editing with cross attention control,” in International Conference on Learning Representations, 2022.
[20] R. Mokady, A. Hertz, K. Aberman, Y. Pritch, and D. Cohen-Or, "Null-text inversion for editing real images using guided diffusion models," arXiv preprint arXiv:2211.09794, 2022.