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We propose a simple yet effective framework for instance and panoptic segmentation, termed CondInst (conditional convolutions for instance and panoptic segmentation).
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2019
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2019
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K. He, R. Girshick, and P. Dollár, “Rethinking imagenet pre-training,” in Proc. IEEE Int. Conf. Comp. Vis
2019
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2019
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2019
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2020
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2020
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H. Chen, K. Sun, Z. Tian, C. Shen, Y. Huang, and Y. Yan, “Blendmask: Top-down meets bottom-up for instance segmentation,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn
2020
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X. Wang, T. Kong, C. Shen, Y. Jiang, and L. Li, “SOLO: Segmenting objects by locations,” in Proc. Eur. Conf. Comp. Vis
2020
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X. Wang, R. Zhang, T. Kong, L. Li, and C. Shen, “SOLOv2: Dynamic and fast instance segmentation,” in Proc. Advances in Neural Inf. Process. Syst
2020
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E. Xie, P. Sun, X. Song, W. Wang, D. Liang, C. Shen, and P. Luo, “PolarMask: Single shot instance segmentation with polar representation,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn
2020
Later among the works it cites.
H. Wang, Y. Zhu, B. Green, H. Adam, A. Yuille, and L.-C. Chen, “Axial-DeepLab: Stand-alone axial-attention for panoptic segmentation,” in Proc. Eur. Conf. Comp. Vis
2020
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Z. Tian, C. Shen, and H. Chen, “Conditional convolutions for instance segmentation,” in Proc. Eur. Conf. Comp. Vis
2020
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Q. Li, X. Qi, and P. H. S. Torr, “Unifying training and inference for panoptic segmentation,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn
2020
Later among the works it cites.
B. Cheng, M. D. Collins, Y. Zhu, T. Liu, T. S. Huang, H. Adam, and L.-C. Chen, “Panoptic-DeepLab: A simple, strong, and fast baseline for bottom-up panoptic segmentation,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn
2020
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Z. Tian, C. Shen, H. Chen, and T. He, “FCOS: A simple and strong anchor-free object detector,” IEEE Trans. Pattern Anal. Mach. Intell
2021
Closest in time.
Y. Li, H. Zhao, X. Qi, L. Wang, Z. Li, J. Sun, and J. Jia, “Fully convolutional networks for panoptic segmentation,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn
2021
Closest in time.