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Foundation models have exhibited unprecedented capabilities in tackling many domains and tasks.
2006
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 , 2009, pp. 248–255
2009
Earlier work this paper cites.
P. Krähenbühl and V. Koltun, “Efficient inference in fully connected crfs with gaussian edge potentials,” Advances in neural information processing systems , vol. 24, 2011
2011
Earlier work this paper cites.
I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial networks,” 2014
2014
Earlier work this paper cites.
T.-Y. Lin, M. Maire, S. Belongie, L. Bourdev, R. Girshick, J. Hays, P. Perona, D. Ramanan, C. L. Zitnick, and P. Dollár, “Microsoft coco: Common objects in context,” 2014
2014
Earlier work this paper cites.
M. Everingham, S. M. A. Eslami, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman, “The pascal visual object classes challenge: A retrospective,” International Journal of Computer Vision , vol. 111, no. 1, pp. 98–136, Jan. 2015
2015
Earlier work this paper cites.
T.-Y. Lin, P. Dollár, R. Girshick, K. He, B. Hariharan, and S. Belongie, “Feature pyramid networks for object detection,” 2017
2017
Earlier work this paper cites.
Z. Huang, X. Wang, J. Wang, W. Liu, and J. Wang, “Weakly-supervised semantic segmentation network with deep seeded region growing,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2018, pp. 7014–7023
2018
Earlier work this paper cites.
J.-J. Hwang, S. X. Yu, J. Shi, M. D. Collins, T.-J. Yang, X. Zhang, and L.-C. Chen, “Segsort: Segmentation by discriminative sorting of segments,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2019, pp. 7334–7344
2019
Earlier work this paper cites.
M. Bucher, T.-H. VU, M. Cord, and P. Pérez, “Zero-shot semantic segmentation,” in Advances in Neural Information Processing Systems , H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox, and R. Garnett, Eds., vol. 32. Curran Associates, Inc., 2019. [Online]. Available: https://proceedings.neurips.cc/paper_files/paper/2019/file/0266e33d3f546cb5436a10798e657d97-Paper.pdf
2019
Earlier work this paper cites.
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
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
B. Pan, J. Sun, H. Y. T. Leung, A. Andonian, and B. Zhou, “Cross-view semantic segmentation for sensing surroundings,” IEEE Robotics and Automation Letters , vol. 5, no. 3, pp. 4867–4873, 2020
2020
Earlier work this paper cites.
K. He, H. Fan, Y. Wu, S. Xie, and R. Girshick, “Momentum contrast for unsupervised visual representation learning,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 9729–9738
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
N. Araslanov and S. Roth, “Single-stage semantic segmentation from image labels,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 4253–4262
2020
Earlier work this paper cites.
2021
Earlier work this paper cites.
A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark et al. , “Learning transferable visual models from natural language supervision,” in International conference on machine learning . PMLR, 2021, pp. 8748–8763
2021
Earlier work this paper cites.
M. Tzelepi and A. Tefas, “Semantic scene segmentation for robotics applications,” in 2021 12th International Conference on Information, Intelligence, Systems & Applications (IISA) . IEEE, 2021, pp. 1–4
2021
Earlier work this paper cites.
H. Touvron, M. Cord, M. Douze, F. Massa, A. Sablayrolles, and H. Jégou, “Training data-efficient image transformers & distillation through attention,” in International conference on machine learning . PMLR, 2021, pp. 10 347–10 357
2021
Earlier work this paper cites.
M. Caron, H. Touvron, I. Misra, H. Jégou, J. Mairal, P. Bojanowski, and A. Joulin, “Emerging properties in self-supervised vision transformers,” in Proceedings of the IEEE/CVF international conference on computer vision , 2021, pp. 9650–9660
2021
Earlier work this paper cites.
C. Jia, Y. Yang, Y. Xia, Y.-T. Chen, Z. Parekh, H. Pham, Q. Le, Y.-H. Sung, Z. Li, and T. Duerig, “Scaling up visual and vision-language representation learning with noisy text supervision,” in International conference on machine learning . PMLR, 2021, pp. 4904–4916
2021
Earlier work this paper cites.
J.-B. Alayrac, J. Donahue, P. Luc, A. Miech, I. Barr, Y. Hasson, K. Lenc, A. Mensch, K. Millican, M. Reynolds et al. , “Flamingo: a visual language model for few-shot learning,” Advances in Neural Information Processing Systems , vol. 35, pp. 23 716–23 736, 2022
2022
Earlier work this paper cites.
2022
Earlier work this paper cites.
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 , 2022, pp. 10 684–10 695
2022
Cited alongside, same era.
G. Csurka, R. Volpi, B. Chidlovskii et al. , “Semantic image segmentation: Two decades of research,” Foundations and Trends® in Computer Graphics and Vision , vol. 14, no. 1-2, pp. 1–162, 2022
2022
Cited alongside, same era.
Y. Wang, H. Wang, Y. Shen, J. Fei, W. Li, G. Jin, L. Wu, R. Zhao, and X. Le, “Semi-supervised semantic segmentation using unreliable pseudo-labels,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 4248–4257
2022
Cited alongside, same era.
M. Xu, Z. Zhang, F. Wei, Y. Lin, Y. Cao, H. Hu, and X. Bai, “A simple baseline for open-vocabulary semantic segmentation with pre-trained vision-language model,” in European Conference on Computer Vision . Springer, 2022, pp. 736–753
2023
Later among the works it cites.
2023
Later among the works it cites.
A. Rahman, J. M. J. Valanarasu, I. Hacihaliloglu, and V. M. Patel, “Ambiguous medical image segmentation using diffusion models,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2023, pp. 11 536–11 546
2023
Later among the works it cites.
C. Chen, C. Wang, B. Liu, C. He, L. Cong, and S. Wan, “Edge intelligence empowered vehicle detection and image segmentation for autonomous vehicles,” IEEE Transactions on Intelligent Transportation Systems , 2023
2023
Later among the works it cites.
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2022
Cited alongside, same era.
D. Baranchuk, I. Rubachev, A. Voynov, V. Khrulkov, and A. Babenko, “Label-efficient semantic segmentation with diffusion models,” International Conference on Learning Representations (ICLR) , 2022
2022
Cited alongside, same era.
C. Schuhmann, R. Beaumont, R. Vencu, C. Gordon, R. Wightman, M. Cherti, T. Coombes, A. Katta, C. Mullis, M. Wortsman, P. Schramowski, S. Kundurthy, K. Crowson, L. Schmidt, R. Kaczmarczyk, and J. Jitsev, “Laion-5b: An open large-scale dataset for training next generation image-text models,” 2022
2022
Cited alongside, same era.
X. Dong, J. Bao, Y. Zheng, T. Zhang, D. Chen, H. Yang, M. Zeng, W. Zhang, L. Yuan, D. Chen, F. Wen, and N. Yu, “Maskclip: Masked self-distillation advances contrastive language-image pretraining,” 2022
2022
Cited alongside, same era.
T. Luddecke and A. Ecker, “Image segmentation using text and image prompts,” in 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . IEEE, Jun. 2022. [Online]. Available: http://dx.doi.org/10.1109/CVPR52688.2022.00695
2022
Cited alongside, same era.
G. Shin, W. Xie, and S. Albanie, “Reco: Retrieve and co-segment for zero-shot transfer,” Advances in Neural Information Processing Systems , vol. 35, pp. 33 754–33 767, 2022
2022
Cited alongside, same era.
J. Xu, S. De Mello, S. Liu, W. Byeon, T. Breuel, J. Kautz, and X. Wang, “Groupvit: Semantic segmentation emerges from text supervision,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 18 134–18 144
2022
Cited alongside, same era.
2022
Cited alongside, same era.
2022
Cited alongside, same era.
F. Liang, B. Wu, X. Dai, K. Li, Y. Zhao, H. Zhang, P. Zhang, P. Vajda, and D. Marculescu, “Open-vocabulary semantic segmentation with mask-adapted clip,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 7061–7070
2023
Later among the works it cites.
J. Xu, S. Liu, A. Vahdat, W. Byeon, X. Wang, and S. De Mello, “Open-vocabulary panoptic segmentation with text-to-image diffusion models,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 2955–2966
2023
Later among the works it cites.
S. Wang, C. Saharia, C. Montgomery, J. Pont-Tuset, S. Noy, S. Pellegrini, Y. Onoe, S. Laszlo, D. J. Fleet, R. Soricut et al. , “Imagen editor and editbench: Advancing and evaluating text-guided image inpainting,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 18 359–18 369
2023
Later among the works it cites.
Q. Yu, J. He, X. Deng, X. Shen, and L.-C. Chen, “Convolutions die hard: Open-vocabulary segmentation with single frozen convolutional clip,” 2023
2023
Later among the works it cites.
P. Ren, C. Li, H. Xu, Y. Zhu, G. Wang, J. Liu, X. Chang, and X. Liang, “Viewco: Discovering text-supervised segmentation masks via multi-view semantic consistency,” in The Eleventh International Conference on Learning Representations , 2023. [Online]. Available: https://openreview.net/forum?id=2XLRBjY46O6
2023
Later among the works it cites.
J. Xu, J. Hou, Y. Zhang, R. Feng, Y. Wang, Y. Qiao, and W. Xie, “Learning open-vocabulary semantic segmentation models from natural language supervision,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 2935–2944
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
S. Chen, P. Sun, Y. Song, and P. Luo, “Diffusiondet: Diffusion model for object detection,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 19 830–19 843
2023
Later among the works it cites.
2023
Later among the works it cites.
W. Wu, Y. Zhao, M. Z. Shou, H. Zhou, and C. Shen, “Diffumask: Synthesizing images with pixel-level annotations for semantic segmentation using diffusion models,” 2023
2023
Later among the works it cites.
C. Ma, Y. Yang, C. Ju, F. Zhang, J. Liu, Y. Wang, Y. Zhang, and Y. Wang, “Diffusionseg: Adapting diffusion towards unsupervised object discovery,” 2023
2023
Later among the works it cites.
K. Pnvr, B. Singh, P. Ghosh, B. Siddiquie, and D. Jacobs, “Ld-znet: A latent diffusion approach for text-based image segmentation,” 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
J. Li, D. Li, S. Savarese, and S. Hoi, “Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models,” 2023
2023
Later among the works it cites.
J. Cha, J. Mun, and B. Roh, “Learning to generate text-grounded mask for open-world semantic segmentation from only image-text pairs,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 11 165–11 174
2023
Later among the works it cites.
M. Kang, J.-Y. Zhu, R. Zhang, J. Park, E. Shechtman, S. Paris, and T. Park, “Scaling up gans for text-to-image synthesis,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 10 124–10 134
2023
Later among the works it cites.
2023
Later among the works it cites.