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Remote sensing segmentation has a wide range of applications in environmental protection, and urban change detection, etc.
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2019
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J. Wang, K. Sun, T. Cheng, B. Jiang, C. Deng, Y. Zhao, D. Liu, Y. Mu, M. Tan, X. Wang et al. , “Deep high-resolution representation learning for visual recognition,” IEEE transactions on pattern analysis and machine intelligence , vol. 43, no. 10, pp. 3349–3364, 2020
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A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly et al. , “An image is worth 16x16 words: Transformers for image recognition at scale,” in International Conference on Learning Representations , 2020
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W. Shi and R. Rajkumar, “Point-gnn: Graph neural network for 3d object detection in a point cloud,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 1711–1719
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2020
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Y. Lyu, G. Vosselman, G.-S. Xia, A. Yilmaz, and M. Y. Yang, “Uavid: A semantic segmentation dataset for uav imagery,” ISPRS journal of photogrammetry and remote sensing , vol. 165, pp. 108–119, 2020
2020
Cited alongside, same era.
H. Li, K. Qiu, L. Chen, X. Mei, L. Hong, and C. Tao, “Scattnet: Semantic segmentation network with spatial and channel attention mechanism for high-resolution remote sensing images,” IEEE Geoscience and Remote Sensing Letters , vol. 18, no. 5, pp. 905–909, 2020
2020
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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
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X. Hao, J. Li, Y. Guo, T. Jiang, and M. Yu, “Hypergraph neural network for skeleton-based action recognition,” IEEE Transactions on Image Processing , vol. 30, pp. 2263–2275, 2021
2021
Y. Shou, T. Meng, W. Ai, C. Xie, H. Liu, and Y. Wang, “Object detection in medical images based on hierarchical transformer and mask mechanism,” Computational Intelligence and Neuroscience , vol. 2022, 2022
2022
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N. Yin, L. Shen, B. Li, M. Wang, X. Luo, C. Chen, Z. Luo, and X.-S. Hua, “Deal: An unsupervised domain adaptive framework for graph-level classification,” in Proceedings of the 30th ACM International Conference on Multimedia , ser. MM ’22. New York, NY, USA: Association for Computing Machinery, 2022, p. 3470–3479
2022
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Y. Shou, T. Meng, W. Ai, S. Yang, and K. Li, “Conversational emotion recognition studies based on graph convolutional neural networks and a dependent syntactic analysis,” Neurocomputing , vol. 501, pp. 629–639, 2022
2022
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Cited alongside, same era.
A. Boguszewski, D. Batorski, N. Ziemba-Jankowska, T. Dziedzic, and A. Zambrzycka, “Landcover. ai: Dataset for automatic mapping of buildings, woodlands, water and roads from aerial imagery,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 1102–1110
2021
Cited alongside, same era.
M. Y. Yang, S. Kumaar, Y. Lyu, and F. Nex, “Real-time semantic segmentation with context aggregation network,” ISPRS journal of photogrammetry and remote sensing , vol. 178, pp. 124–134, 2021
2021
Cited alongside, same era.
M. Oršić and S. Šegvić, “Efficient semantic segmentation with pyramidal fusion,” Pattern Recognition , vol. 110, p. 107611, 2021
2021
Cited alongside, same era.
R. Li, S. Zheng, C. Zhang, C. Duan, J. Su, L. Wang, and P. M. Atkinson, “Multiattention network for semantic segmentation of fine-resolution remote sensing images,” IEEE Transactions on Geoscience and Remote Sensing , vol. 60, pp. 1–13, 2021
2021
Cited alongside, same era.
R. Li, S. Zheng, C. Zhang, C. Duan, L. Wang, and P. M. Atkinson, “Abcnet: Attentive bilateral contextual network for efficient semantic segmentation of fine-resolution remotely sensed imagery,” ISPRS journal of photogrammetry and remote sensing , vol. 181, pp. 84–98, 2021
2021
Cited alongside, same era.
R. Strudel, R. Garcia, I. Laptev, and C. Schmid, “Segmenter: Transformer for semantic segmentation,” in Proceedings of the IEEE/CVF international conference on computer vision , 2021, pp. 7262–7272
2021
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E. Xie, W. Wang, Z. Yu, A. Anandkumar, J. M. Alvarez, and P. Luo, “Segformer: Simple and efficient design for semantic segmentation with transformers,” Advances in Neural Information Processing Systems , vol. 34, pp. 12 077–12 090, 2021
2021
Cited alongside, same era.
L. Wang, R. Li, D. Wang, C. Duan, T. Wang, and X. Meng, “Transformer meets convolution: A bilateral awareness network for semantic segmentation of very fine resolution urban scene images,” Remote Sensing , vol. 13, no. 16, p. 3065, 2021
2021
Cited alongside, same era.
2022
Later among the works it cites.
H. Cao, Y. Wang, J. Chen, D. Jiang, X. Zhang, Q. Tian, and M. Wang, “Swin-unet: Unet-like pure transformer for medical image segmentation,” in European conference on computer vision . Springer, 2022, pp. 205–218
2022
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L. Ding, D. Lin, S. Lin, J. Zhang, X. Cui, Y. Wang, H. Tang, and L. Bruzzone, “Looking outside the window: Wide-context transformer for the semantic segmentation of high-resolution remote sensing images,” IEEE Transactions on Geoscience and Remote Sensing , vol. 60, pp. 1–13, 2022
2022
Later among the works it cites.
C. Zhang, W. Jiang, Y. Zhang, W. Wang, Q. Zhao, and C. Wang, “Transformer and cnn hybrid deep neural network for semantic segmentation of very-high-resolution remote sensing imagery,” IEEE Transactions on Geoscience and Remote Sensing , vol. 60, pp. 1–20, 2022
2022
Later among the works it cites.
Y. Mo, L. Peng, J. Xu, X. Shi, and X. Zhu, “Simple unsupervised graph representation learning,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 36, no. 7, 2022, pp. 7797–7805
2022
Later among the works it cites.
Z. Wen and Y. Fang, “Trend: Temporal event and node dynamics for graph representation learning,” in Proceedings of the ACM Web Conference 2022 , 2022, pp. 1159–1169
2022
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K. Han, Y. Wang, J. Guo, Y. Tang, and E. Wu, “Vision gnn: An image is worth graph of nodes,” Advances in Neural Information Processing Systems , vol. 35, pp. 8291–8303, 2022
2022
Later among the works it cites.
S. Saha, S. Zhao, and X. X. Zhu, “Multitarget domain adaptation for remote sensing classification using graph neural network,” IEEE Geoscience and Remote Sensing Letters , vol. 19, pp. 1–5, 2022
2022
Later among the works it cites.
L. Wang, R. Li, C. Zhang, S. Fang, C. Duan, X. Meng, and P. M. Atkinson, “Unetformer: A unet-like transformer for efficient semantic segmentation of remote sensing urban scene imagery,” ISPRS Journal of Photogrammetry and Remote Sensing , vol. 190, pp. 196–214, 2022
2022
Later among the works it cites.
N. Yin, L. Shen, M. Wang, X. Luo, Z. Luo, and D. Tao, “Omg: Towards effective graph classification against label noise,” IEEE Transactions on Knowledge and Data Engineering , vol. 35, no. 12, pp. 12 873–12 886, 2023
2023
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