Fetching the paper…
Reading the bibliography…
Image translation has wide applications, such as style transfer and modality conversion, usually aiming to generate images having both high degrees of realism and faithfulness.
N. Tumanyan, M. Geyer, S. Bagon, and T. Dekel, “Plug-and-play diffusion features for text-driven image-to-image translation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 1921–1930
1930
Earlier work this paper cites.
B. Li, K. Xue, B. Liu, and Y.-K. Lai, “Bbdm: Image-to-image translation with brownian bridge diffusion models,” in Proceedings of the IEEE/CVF conference on computer vision and pattern Recognition , 2023, pp. 1952–1961
1961
Earlier work this paper cites.
A. Pitiot, G. Malandain, E. Bardinet, and P. M. Thompson, “Piecewise affine registration of biological images,” in Biomedical Image Registration: Second InternationalWorkshop, WBIR 2003, Philadelphia, PA, USA, June 23-24, 2003. Revised Papers 2 . Springer, 2003, pp. 91–101
2003
Earlier work this paper cites.
Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE transactions on image processing , vol. 13, no. 4, pp. 600–612, 2004
2004
Earlier work this paper cites.
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” in 2009 IEEE conference on computer vision and pattern recognition . Ieee, 2009, pp. 248–255
2009
Earlier work this paper cites.
A. Lahouhou, E. Viennet, and A. Beghdadi, “Selecting low-level features for image quality assessment by statistical methods,” Journal of computing and information technology , vol. 18, no. 2, pp. 183–189, 2010
2010
Earlier work this paper cites.
G. Paolacci, J. Chandler, and P. G. Ipeirotis, “Running experiments on amazon mechanical turk,” Judgment and Decision making , vol. 5, no. 5, pp. 411–419, 2010
2010
Earlier work this paper cites.
A. K. Qin and D. A. Clausi, “Multivariate image segmentation using semantic region growing with adaptive edge penalty,” IEEE transactions on image processing , vol. 19, no. 8, pp. 2157–2170, 2010
2010
Earlier work this paper cites.
R. Tyleček and R. Šára, “Spatial pattern templates for recognition of objects with regular structure,” in Pattern Recognition: 35th German Conference, GCPR 2013, Saarbrücken, Germany, September 3-6, 2013. Proceedings 35 . Springer, 2013, pp. 364–374
2013
Earlier work this paper cites.
D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” in International Conference on Learning Representations , 2014
2014
Earlier work this paper cites.
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 , 2016, pp. 3213–3223
2016
Earlier work this paper cites.
P. Isola, J.-Y. Zhu, T. Zhou, and A. A. Efros, “Image-to-image translation with conditional adversarial networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 1125–1134
2017
Earlier work this paper cites.
M.-Y. Liu, T. Breuel, and J. Kautz, “Unsupervised image-to-image translation networks,” Advances in neural information processing systems , vol. 30, 2017
2017
Earlier work this paper cites.
J.-Y. Zhu, R. Zhang, D. Pathak, T. Darrell, A. A. Efros, O. Wang, and E. Shechtman, “Toward multimodal image-to-image translation,” Advances in neural information processing systems , vol. 30, 2017
2017
Earlier work this paper cites.
J.-Y. Zhu, T. Park, P. Isola, and A. A. Efros, “Unpaired image-to-image translation using cycle-consistent adversarial networks,” in Proceedings of the IEEE international conference on computer vision , 2017, pp. 2223–2232
2017
Earlier work this paper cites.
M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, and S. Hochreiter, “Gans trained by a two time-scale update rule converge to a local nash equilibrium,” Advances in neural information processing systems , vol. 30, 2017
2017
Cited alongside, same era.
Y. Choi, M. Choi, M. Kim, J.-W. Ha, S. Kim, and J. Choo, “Stargan: Unified generative adversarial networks for multi-domain image-to-image translation,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 8789–8797
2018
Cited alongside, same era.
R. Zhang, P. Isola, A. A. Efros, E. Shechtman, and O. Wang, “The unreasonable effectiveness of deep features as a perceptual metric,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 586–595
2018
Cited alongside, same era.
X. Huang, M.-Y. Liu, S. Belongie, and J. Kautz, “Multimodal unsupervised image-to-image translation,” in Proceedings of the European conference on computer vision (ECCV) , 2018, pp. 172–189
2018
2021
Later among the works it cites.
C. Meng, Y. He, Y. Song, J. Song, J. Wu, J.-Y. Zhu, and S. Ermon, “Sdedit: Guided image synthesis and editing with stochastic differential equations,” in International Conference on Learning Representations , 2021
2021
Later among the works it cites.
N. Shusharina, M. P. Heinrich, and R. Huang, Segmentation, Classification, and Registration of Multi-modality Medical Imaging Data: MICCAI 2020 Challenges, ABCs 2020, L2R 2020, TN-SCUI 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4–8, 2020, Proceedings . Springer Nature, 2021, vol. 12587
2021
Later among the works it cites.
A. Q. Nichol and P. Dhariwal, “Improved denoising diffusion probabilistic models,” in International Conference on Machine Learning , 2021, pp. 8162–8171
2021
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
S. Kaji and S. Kida, “Overview of image-to-image translation by use of deep neural networks: denoising, super-resolution, modality conversion, and reconstruction in medical imaging,” Radiological physics and technology , vol. 12, pp. 235–248, 2019
2019
Cited alongside, same era.
A. Borji, “Pros and cons of gan evaluation measures,” Computer Vision and Image Understanding , vol. 179, pp. 41–65, 2019
2019
Cited alongside, same era.
Y. Zhang, S. Wang, B. Chen, and J. Cao, “Gcgan: Generative adversarial nets with graph cnn for network-scale traffic prediction,” in 2019 International Joint Conference on Neural Networks (IJCNN) . IEEE, 2019, pp. 1–8
2019
Cited alongside, same era.
K. Armanious, C. Jiang, M. Fischer, T. Küstner, T. Hepp, K. Nikolaou, S. Gatidis, and B. Yang, “Medgan: Medical image translation using gans,” Computerized medical imaging and graphics , vol. 79, p. 101684, 2020
2020
Cited alongside, same era.
A. Alotaibi, “Deep generative adversarial networks for image-to-image translation: A review,” Symmetry , vol. 12, no. 10, p. 1705, 2020
2020
Cited alongside, same era.
M. Arar, Y. Ginger, D. Danon, A. H. Bermano, and D. Cohen-Or, “Unsupervised multi-modal image registration via geometry preserving image-to-image translation,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 13 410–13 419
2020
Cited alongside, same era.
T. Park, A. A. Efros, R. Zhang, and J.-Y. Zhu, “Contrastive learning for unpaired image-to-image translation,” in Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part IX 16 . Springer, 2020, pp. 319–345
2020
Cited alongside, same era.
Y. Choi, Y. Uh, J. Yoo, and J.-W. Ha, “Stargan v2: Diverse image synthesis for multiple domains,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 8188–8197
2020
Cited alongside, same era.
Later among the works it cites.
Z. Liu, Y. Lin, Y. Cao, H. Hu, Y. Wei, Z. Zhang, S. Lin, and B. Guo, “Swin transformer: Hierarchical vision transformer using shifted windows,” in Proceedings of the IEEE/CVF international conference on computer vision , 2021, pp. 10 012–10 022
2021
Later among the works it cites.
D. Bashkirova, B. Usman, and K. Saenko, “Evaluation of correctness in unsupervised many-to-many image translation,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , 2022, pp. 1776–1785
2022
Later among the works it cites.
2022
Later among the works it cites.
2022
Later among the works it cites.
2022
Later among the works it cites.
2023
Closest in time.
J. Ma and B. Wang, “Segment anything in medical images,” arXiv preprint arXiv:2304.12306 , 2023
2023
Closest in time.
A. Thummerer, E. van der Bijl, A. Galapon Jr, J. J. Verhoeff, J. A. Langendijk, S. Both, C. N. A. van den Berg, and M. Maspero, “Synthrad2023 grand challenge dataset: Generating synthetic ct for radiotherapy,” Medical physics , vol. 50, no. 7, pp. 4664–4674, 2023
2023
Closest in time.
H. Lee, M. Kang, and B. Han, “Conditional score guidance for text-driven image-to-image translation,” Advances in Neural Information Processing Systems , vol. 36, 2024
2024
Closest in time.
S. Xu, Z. Ma, Y. Huang, H. Lee, and J. Chai, “Cyclenet: Rethinking cycle consistency in text-guided diffusion for image manipulation,” Advances in Neural Information Processing Systems , vol. 36, 2024
2024
Closest in time.