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We explore the capability of plain Vision Transformers (ViTs) for semantic segmentation and propose the SegVit.
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2018
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2019
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X. Li, Z. Zhong, J. Wu, Y. Yang, Z. Lin, and H. Liu, “Expectation-maximization attention networks for semantic segmentation,” in
2019
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K. Sun, Y. Zhao, B. Jiang, T. Cheng, B. Xiao, D. Liu, Y. Mu, X. Wang, W. Liu, and J. Wang, “High-resolution representations for labeling pixels and regions,”
2019
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2021
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2021
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B. Cheng, A. Schwing, and A. Kirillov, “Per-pixel classification is not all you need for semantic segmentation,”
2021
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2021
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W. Wang, E. Xie, X. Li, D.-P. Fan, K. Song, D. Liang, T. Lu, P. Luo, and L. Shao, “Pyramid vision transformer: A versatile backbone for dense prediction without convolutions,” in
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Y. Yuan, X. Chen, and J. Wang, “Object-contextual representations for semantic segmentation,” in
2020
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J. Fu, J. Liu, J. Jiang, Y. Li, Y. Bao, and H. Lu, “Scene segmentation with dual relation-aware attention network,”
2020
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C. Yu, J. Wang, C. Gao, G. Yu, C. Shen, and N. Sang, “Context prior for scene segmentation,” in
2020
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N. Carion, F. Massa, G. Synnaeve, N. Usunier, A. Kirillov, and S. Zagoruyko, “End-to-end object detection with transformers,” in
2020
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MMSegmentation, “MMSegmentation: OpenMMLab semantic segmentation toolbox and benchmark.”
2020
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X. Li, Y. Yang, Q. Zhao, T. Shen, Z. Lin, and H. Liu, “Spatial pyramid based graph reasoning for semantic segmentation,” in
2020
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T. Wu, Y. Lu, Y. Zhu, C. Zhang, M. Wu, Z. Ma, and G. Guo, “Ginet: Graph interaction network for scene parsing,” in
2020
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2021
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2021
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2021
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2021
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2021
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2021
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2021
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2021
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Y.-H. Wu, Y. Liu, X. Zhan, and M.-M. Cheng, “P2t: Pyramid pooling transformer for scene understanding,”
2022
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F. Lin, Z. Liang, J. He, M. Zheng, S. Tian, and K. Chen, “Structtoken: Rethinking semantic segmentation with structural prior,”
2022
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B. Cheng, I. Misra, A. G. Schwing, A. Kirillov, and R. Girdhar, “Masked-attention mask transformer for universal image segmentation,” 2022
2022
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