SimWorld: A Unified Benchmark for Simulator-Conditioned Scene Generation via World Model

H. Tang, S. Bai, and N. Sebe, “Dual attention gans for semantic image synthesis,” in Proceedings of the 28th ACM International Conference on Multimedia , 2020, pp. 1994–2002

2002

Earlier work this paper cites.

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 . Springer, 2012, pp. 611–625

2012

Earlier work this paper cites.

A. Geiger, P. Lenz, and R. Urtasun, “Are we ready for autonomous driving? the kitti vision benchmark suite,” in 2012 IEEE conference on computer vision and pattern recognition . IEEE, 2012, pp. 3354–3361

2012

Earlier work this paper cites.

D. P. Kingma, “Auto-encoding variational bayes,” arXiv preprint arXiv:1312.6114 , 2013

Original

2013

Earlier work this paper cites.

I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in Advances in Neural Information Processing Systems , Z. Ghahramani, M. Welling, C. Cortes, N. Lawrence, and K. Weinberger, Eds., vol. 27. Curran Associates, Inc., 2014

2014

Earlier work this paper cites.

O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in International Conference on Medical image computing and computer-assisted intervention . Springer, 2015, pp. 234–241

2015

Earlier work this paper cites.

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 , 2016, pp. 4340–4349

2016

Earlier work this paper cites.

S. R. Richter, V. Vineet, S. Roth, and V. Koltun, “Playing for data: Ground truth from computer games,” in Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part II 14 . Springer, 2016, pp. 102–118

2016

Earlier work this paper cites.

G. Ros, L. Sellart, J. Materzynska, D. Vazquez, and A. M. Lopez, “The synthia dataset: A large collection of synthetic images for semantic segmentation of urban scenes,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 3234–3243

2016

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.

C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the inception architecture for computer vision,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 2818–2826

2016

Earlier work this paper cites.

W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg, “Ssd: Single shot multibox detector,” ECCV , 2016

2016

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

Earlier work this paper cites.

S. Ren, K. He, R. Girshick, and J. Sun, “Faster r-cnn: Towards real-time object detection with region proposal networks,” IEEE Transactions on Pattern Analysis and Machine Intelligence , Jun 2017

2017

Earlier work this paper cites.

H. Zhao, J. Shi, X. Qi, X. Wang, and J. Jia, “Pyramid scene parsing network,” in CVPR , 2017

2017

Earlier work this paper cites.

L.-C. Chen, G. Papandreou, F. Schroff, and H. Adam, “Rethinking atrous convolution for semantic image segmentation,” arXiv preprint arXiv:1706.05587 , 2017

Original

2017

Cited alongside, same era.

M. Wrenninge and J. Unger, “Synscapes: A photorealistic synthetic dataset for street scene parsing,” arXiv preprint arXiv:1810.08705 , 2018

Original

2018

Cited alongside, same era.

Q. Wang, J. Gao, and X. Li, “Weakly supervised adversarial domain adaptation for semantic segmentation in urban scenes,” IEEE Transactions on Image Processing , vol. 28, no. 9, pp. 4376–4386, 2019

2019

Cited alongside, same era.

H. Tang, D. Xu, N. Sebe, Y. Wang, J. J. Corso, and Y. Yan, “Multi-channel attention selection gan with cascaded semantic guidance for cross-view image translation,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019, pp. 2417–2426

2019

Cited alongside, same era.

Y. Li, Z. Li, S. Teng, Y. Zhang, Y. Zhou, Y. Zhu, D. Cao, B. Tian, Y. Ai, Z. Xuanyuan et al. , “Automine: An unmanned mine dataset,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 21 308–21 317

2022

Later among the works it cites.

S. Ettedgui, S. Abu-Hussein, and R. Giryes, “Procst: Boosting semantic segmentation using progressive cyclic style-transfer,” arXiv preprint arXiv:2204.11891 , 2022

Original

2022

Later among the works it cites.

S. Chen, P. Sun, Y. Song, and P. Luo, “Diffusiondet: Diffusion model for object detection,” arXiv preprint arXiv:2211.09788 , 2022

Original

2022

Later among the works it cites.

Z. Song, Z. He, X. Li, Q. Ma, R. Ming, Z. Mao, H. Pei, L. Peng, J. Hu, D. Yao et al. , “Synthetic datasets for autonomous driving: A survey,” IEEE Transactions on Intelligent Vehicles , 2023

2023

Later among the works it cites.