[1] J. Ahn and S. Kwak. Learning pixel-level semantic affinity with image-level supervision for weakly supervised semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4981-4990, 2018.
[2] P. Akiva and K. Dana. Towards single stage weakly supervised semantic segmentation. arXiv preprint arXiv:2106.10309, 2021.
[3] D. Baranchuk, I. Rubachev, A. Voynov, V. Khrulkov, and A. Babenko. Label-efficient semantic segmentation with diffusion models. arXiv preprint arXiv:2112.03126, 2021.
[4] S. Bubeck, V. Chandrasekaran, R. Eldan, J. Gehrke, E. Horvitz, E. Kamar, P. Lee, Y. T. Lee, Y. Li, S. Lundberg, et al. Sparks of artificial general intelligence: Early experiments with gpt-4. arXiv preprint arXiv:2303.12712, 2023.
[5] M. Bucher, T.-H. Vu, M. Cord, and P. Pérez. Zero-shot semantic segmentation. NeurIPS, 2019.
[6] D. J. Butler, J. Wulff, G. B. Stanley, and M. J. Black. A naturalistic open source movie for optical flow evaluation. In Computer Vision-ECCV 2012: 12th European Conference on Computer Vision, Florence, Italy, October 7-13, 2012, Proceedings, Part VI 12, pages 611-625. Springer, 2012.
[7] Y. Cabon, N. Murray, and M. Humenberger. Virtual katii 2. arXiv preprint arXiv:2001.10773, 2020.
[8] B. Cheng, I. Misra, A. G. Schwing, A. Kirillov, and R. Girdhar. Masked-attention mask transformer for universal image segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1290–1299, 2022.
[9] B. Cheng, A. Schwing, and A. Kirillov. Per-pixel classification is not all you need for semantic segmentation. Advances in Neural Information Processing Systems, 34:17864-17875, 2021.
[10] 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, pages 3213-3223, 2016.
[11] 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, pages 3213-3223, 2016.
[12] L. Dinh, D. Krueger, and Y. Bengio. Nice: Non-linear independent components estimation. arXiv preprint arXiv:1410.8516, 2014.
[13] D. Eigen, C. Puhrsch, and R. Fergus. Depth map prediction from a single image using a multi-scale deep network. Advances in neural information processing systems, 27, 2014.
[14] P. Esser, R. Rombach, and B. Ommer. Taming transformers for high-resolution image synthesis. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 12873-12883, 2021.
[15] M. Everingham, L. Van Gool, C. K. Williams, J. Winn, and A. Zisserman. The Pascal visual object classes (voc) challenge. International journal of computer vision, 88(2):303-338, 2010.
[16] L. Floridi and M. Chiriatti. Gpt-3: Its nature, scope, limits, and consequences. *Minds and Machines*, 30:681–694, 2020.
[17] A. Gaidon, Q. Wang, Y. Cabon, and E. Vig. Virtual worlds as proxy for multi-object tracking analysis. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4340-4349, 2016.
[18] Y. Ge, J. Zhao, and L. Itti. Pose augmentation: Class-agnostic object pose transformation for object recognition. In Computer Vision-ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXVIII 16, pages 138–155. Springer, 2020.
[19] I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio. Generative adversarial networks. Communications of the ACM, 63(11):139–144, 2020.
[20] 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. arXiv preprint arXiv:2305.18292, 2023.
[21] K. Han, Y. Xiong, C. You, P. Khosravi, S. Sun, X. Yan, J. Duncan, and X. Xie. Medgen3d: A deep generative framework for paired 3d image and mask generation. arXiv preprint arXiv:2304.04106, 2023.
[22] R. He, S. Sun, X. Yu, C. Xue, W. Zhang, P. Torr, S. Bai, and X. Qi. Is synthetic data from generative models ready for image recognition? arXiv preprint arXiv:2210.07574, 2022.
[23] A. Hendy, M. Abdelrehim, A. Sharaf, V. Raunak, M. Gabr, H. Matsushita, Y. J. Kim, M. Affy, and H. H. Awadalla. How good are gpt models at machine translation? a comprehensive evaluation. arXiv preprint arXiv:2302.09210, 2023.