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Recent studies have used unsupervised domain adaptive object detection (UDAOD) methods to bridge the domain gap in remote sensing (RS) images.
L. Van der Maaten and G. Hinton, “Visualizing data using t-sne.” Journal of machine learning research , vol. 9, no. 11, 2008
2008
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G. Cheng, J. Han, P. Zhou, and L. Guo, “Multi-class geospatial object detection and geographic image classification based on collection of part detectors,” ISPRS Journal of Photogrammetry and Remote Sensing , vol. 98, pp. 119–132, 2014
2014
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2014
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K. Liu and G. Mattyus, “Fast multiclass vehicle detection on aerial images,” IEEE Geoscience and Remote Sensing Letters , vol. 12, no. 9, pp. 1938–1942, 2015
2015
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H. Zhu, X. Chen, W. Dai, K. Fu, Q. Ye, and J. Jiao, “Orientation robust object detection in aerial images using deep convolutional neural network,” in 2015 IEEE International Conference on Image Processing (ICIP) . IEEE, 2015, pp. 3735–3739
2015
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S. Ren, K. He, R. Girshick, and J. Sun, “Faster r-cnn: Towards real-time object detection with region proposal networks,” Advances in neural information processing systems , vol. 28, 2015
2015
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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
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2016
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G. Cheng, P. Zhou, and J. Han, “Learning rotation-invariant convolutional neural networks for object detection in vhr optical remote sensing images,” IEEE Transactions on Geoscience and Remote Sensing , vol. 54, no. 12, pp. 7405–7415, 2016
2016
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Y. Ganin, E. Ustinova, H. Ajakan, P. Germain, H. Larochelle, F. Laviolette, M. Marchand, and V. Lempitsky, “Domain-adversarial training of neural networks,” The journal of machine learning research , vol. 17, no. 1, pp. 2096–2030, 2016
2016
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K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 770–778
2016
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Y. Li, C. Fang, J. Yang, Z. Wang, X. Lu, and M.-H. Yang, “Universal style transfer via feature transforms,” Advances in neural information processing systems , vol. 30, 2017
2017
Earlier work this paper cites.
A. Tarvainen and H. Valpola, “Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results,” Advances in neural information processing systems , vol. 30, 2017
2017
Earlier work this paper cites.
J.-Y. Zhu, T. Park, P. Isola, and A. A. Efros, “Unpaired image-to-image translation using cycle-consistent adversarial networks,” in Proceedings of the IEEE international conference on computer vision , 2017, pp. 2223–2232
2017
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X. Huang and S. Belongie, “Arbitrary style transfer in real-time with adaptive instance normalization,” in Proceedings of the IEEE international conference on computer vision , 2017, pp. 1501–1510
2017
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2017
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G. Chen, W. Choi, X. Yu, T. Han, and M. Chandraker, “Learning efficient object detection models with knowledge distillation,” Advances in neural information processing systems , vol. 30, 2017
2017
Earlier work this paper cites.
2018
Earlier work this paper cites.
Y. Chen, W. Li, C. Sakaridis, D. Dai, and L. Van Gool, “Domain adaptive faster r-cnn for object detection in the wild,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 3339–3348
2018
Earlier work this paper cites.
T. Miyato, S.-i. Maeda, M. Koyama, and S. Ishii, “Virtual adversarial training: a regularization method for supervised and semi-supervised learning,” IEEE transactions on pattern analysis and machine intelligence , vol. 41, no. 8, pp. 1979–1993, 2018
2018
Earlier work this paper cites.
M. Y. Yang, W. Liao, X. Li, Y. Cao, and B. Rosenhahn, “Vehicle detection in aerial images,” Photogrammetric Engineering & Remote Sensing , vol. 85, no. 4, pp. 297–304, 2019
2019
Cited alongside, same era.
A. RoyChowdhury, P. Chakrabarty, A. Singh, S. Jin, H. Jiang, L. Cao, and E. Learned-Miller, “Automatic adaptation of object detectors to new domains using self-training,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 780–790
2019
Cited alongside, same era.
K. Saito, Y. Ushiku, T. Harada, and K. Saenko, “Strong-weak distribution alignment for adaptive object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 6956–6965
2019
Cited alongside, same era.
2019
W. Ma, N. Li, H. Zhu, L. Jiao, X. Tang, Y. Guo, and B. Hou, “Feature split–merge–enhancement network for remote sensing object detection,” IEEE Transactions on Geoscience and Remote Sensing , vol. 60, pp. 1–17, 2022
2022
Later among the works it cites.
P. Yuan, W. Chen, S. Yang, Y. Xuan, D. Xie, Y. Zhuang, and S. Pu, “Simulation-and-mining: towards accurate source-free unsupervised domain adaptive object detection,” in ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2022, pp. 3843–3847
2022
Later among the works it cites.
S. Li, M. Ye, X. Zhu, L. Zhou, and L. Xiong, “Source-free object detection by learning to overlook domain style,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 8014–8023
2022
Later among the works it cites.
T. Xu, X. Sun, W. Diao, L. Zhao, K. Fu, and H. Wang, “Fada: Feature aligned domain adaptive object detection in remote sensing imagery,” IEEE Transactions on Geoscience and Remote Sensing , vol. 60, pp. 1–16, 2022
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Cited alongside, same era.
J. Liang, D. Hu, and J. Feng, “Do we really need to access the source data? source hypothesis transfer for unsupervised domain adaptation,” in International conference on machine learning . PMLR, 2020, pp. 6028–6039
2020
Cited alongside, same era.
K. Li, G. Wan, G. Cheng, L. Meng, and J. Han, “Object detection in optical remote sensing images: A survey and a new benchmark,” ISPRS journal of photogrammetry and remote sensing , vol. 159, pp. 296–307, 2020
2020
Cited alongside, same era.
Y. Koga, H. Miyazaki, and R. Shibasaki, “A method for vehicle detection in high-resolution satellite images that uses a region-based object detector and unsupervised domain adaptation,” Remote Sensing , vol. 12, no. 3, p. 575, 2020
2020
Cited alongside, same era.
X. Li, M. Luo, S. Ji, L. Zhang, and M. Lu, “Evaluating generative adversarial networks based image-level domain transfer for multi-source remote sensing image segmentation and object detection,” International Journal of Remote Sensing , vol. 41, no. 19, pp. 7343–7367, 2020
2020
Cited alongside, same era.
C. Chen, Z. Zheng, X. Ding, Y. Huang, and Q. Dou, “Harmonizing transferability and discriminability for adapting object detectors,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 8869–8878
2020
Cited alongside, same era.
K. Fu, Z. Chang, Y. Zhang, G. Xu, K. Zhang, and X. Sun, “Rotation-aware and multi-scale convolutional neural network for object detection in remote sensing images,” ISPRS Journal of Photogrammetry and Remote Sensing , vol. 161, pp. 294–308, 2020
2020
Cited alongside, same era.
B. Rasti, Y. Chang, E. Dalsasso, L. Denis, and P. Ghamisi, “Image restoration for remote sensing: Overview and toolbox,” IEEE Geoscience and Remote Sensing Magazine , vol. 10, no. 2, pp. 201–230, 2021
2021
Cited alongside, same era.
Y. Liu, Q. Li, Y. Yuan, Q. Du, and Q. Wang, “Abnet: Adaptive balanced network for multiscale object detection in remote sensing imagery,” IEEE Transactions on Geoscience and Remote Sensing , vol. 60, pp. 1–14, 2021
2021
Cited alongside, same era.
2022
Later among the works it cites.
Y. Shi, L. Du, Y. Guo, and Y. Du, “Unsupervised domain adaptation based on progressive transfer for ship detection: From optical to sar images,” IEEE Transactions on Geoscience and Remote Sensing , vol. 60, pp. 1–17, 2022
2022
Later among the works it cites.
J.-H. Kim and Y. Hwang, “Gan-based synthetic data augmentation for infrared small target detection,” IEEE Transactions on Geoscience and Remote Sensing , vol. 60, pp. 1–12, 2022
2022
Later among the works it cites.
L. Xiong, M. Ye, D. Zhang, Y. Gan, and Y. Liu, “Source data-free domain adaptation for a faster r-cnn,” Pattern Recognition , vol. 124, p. 108436, 2022
2022
Later among the works it cites.
V. Vs, P. Oza, V. A. Sindagi, and V. M. Patel, “Mixture of teacher experts for source-free domain adaptive object detection,” in 2022 IEEE International Conference on Image Processing (ICIP) . IEEE, 2022, pp. 3606–3610
2022
Later among the works it cites.
M. He, Y. Wang, J. Wu, Y. Wang, H. Li, B. Li, W. Gan, W. Wu, and Y. Qiao, “Cross domain object detection by target-perceived dual branch distillation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 9570–9580
2022
Later among the works it cites.
C. Park, S. Yun, and S. Chun, “A unified analysis of mixed sample data augmentation: A loss function perspective,” Advances in Neural Information Processing Systems , vol. 35, pp. 35 504–35 518, 2022
2022
Later among the works it cites.
Q. Liu, L. Lin, Z. Shen, and Z. Yang, “Periodically exchange teacher-student for source-free object detection,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 6414–6424
2023
Later among the works it cites.
J. He, N. Su, C. Xu, Y. Liao, Y. Yan, C. Zhao, W. Hou, and S. Feng, “A cross-modality feature transfer method for target detection in sar images,” IEEE Transactions on Geoscience and Remote Sensing , 2023
2023
Later among the works it cites.
B. Zou, J. Qin, and L. Zhang, “Cross-scene target detection based on feature adaptation and uncertainty-aware pseudo-label learning for high resolution sar images,” ISPRS Journal of Photogrammetry and Remote Sensing , vol. 200, pp. 173–190, 2023
2023
Later among the works it cites.
Y. Zhu, X. Sun, W. Diao, H. Wei, and K. Fu, “Dualda-net: Dual-head rectification for cross domain object detection of remote sensing,” IEEE Transactions on Geoscience and Remote Sensing , 2023
2023
Later among the works it cites.
W. Liu, J. Liu, and B. Luo, “Unsupervised domain adaptation for remote sensing vehicle detection using domain-specific channel recalibration,” IEEE Geoscience and Remote Sensing Letters , 2023
2023
Later among the works it cites.
V. Vibashan, P. Oza, and V. M. Patel, “Instance relation graph guided source-free domain adaptive object detection,” in 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . IEEE, 2023, pp. 3520–3530
2023
Later among the works it cites.
V. VS, P. Oza, and V. M. Patel, “Towards online domain adaptive object detection,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , 2023, pp. 478–488
2023
Later among the works it cites.
Z. Chen, Z. Wang, and Y. Zhang, “Exploiting low-confidence pseudo-labels for source-free object detection,” in Proceedings of the 31st ACM International Conference on Multimedia , 2023, pp. 5370–5379
2023
Later among the works it cites.
X. Zheng, H. Cui, C. Xu, and X. Lu, “Dual teacher: A semi-supervised co-training framework for cross-domain ship detection,” IEEE Transactions on Geoscience and Remote Sensing , 2023
2023
Later among the works it cites.