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In recent years, advanced research has focused on the direct learning and analysis of remote sensing images using natural language processing (NLP) techniques.
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2018
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2019
Cited alongside, same era.
L. Huang, W. Wang, J. Chen, and X.-Y. Wei, “Attention on attention for image captioning,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2019, pp. 4634–4643
2019
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Y.-L. Li and S. Wang, “HAR-Net: Joint learning of hybrid attention for single-stage object detection,” IEEE Transactions on Image Processing , vol. 29, pp. 3092–3103, 2020
2020
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L. Ru, B. Du, and C. Wu, “Multi-temporal scene classification and scene change detection with correlation based fusion,” IEEE Transactions on Image Processing , vol. 30, pp. 1382–1394, 2020
2020
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G. Hoxha, S. Chouaf, F. Melgani, and Y. Smara, “Change captioning: A new paradigm for multitemporal remote sensing image analysis,” IEEE Transactions on Geoscience and Remote Sensing , vol. 60, pp. 1–14, 2022
2022
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2022
Later among the works it cites.
Q. Wang, W. Huang, X. Zhang, and X. Li, “GLCM: Global–local captioning model for remote sensing image captioning,” IEEE Transactions on Cybernetics , 2022
2022
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S. Chang, M. Kopp, and P. Ghamisi, “A deep feature retrieved network for bitemporal remote sensing image change detection,” 2022
2022
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Y. Xu, B. Du, and L. Zhang, “Assessing the threat of adversarial examples on deep neural networks for remote sensing scene classification: Attacks and defenses,” IEEE Transactions on Geoscience and Remote Sensing , vol. 59, no. 2, pp. 1604–1617, 2020
2020
Cited alongside, same era.
M. Yang, J. Liu, Y. Shen, Z. Zhao, X. Chen, Q. Wu, and C. Li, “An ensemble of generation-and retrieval-based image captioning with dual generator generative adversarial network,” IEEE Transactions on Image Processing , vol. 29, pp. 9627–9640, 2020
2020
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X. Shi, X. Yang, J. Gu, S. Joty, and J. Cai, “Finding it at another side: A viewpoint-adapted matching encoder for change captioning,” in Proceedings of the European Conference on Computer Vision . Springer, 2020, pp. 574–590
2020
Cited alongside, same era.
Y. Huang, J. Chen, W. Ouyang, W. Wan, and Y. Xue, “Image captioning with end-to-end attribute detection and subsequent attributes prediction,” IEEE Transactions on Image Processing , vol. 29, pp. 4013–4026, 2020
2020
Cited alongside, same era.
J. Ji, C. Xu, X. Zhang, B. Wang, and X. Song, “Spatio-temporal memory attention for image captioning,” IEEE Transactions on Image Processing , vol. 29, pp. 7615–7628, 2020
2020
Cited alongside, same era.
H. Chen and Z. Shi, “A spatial-temporal attention-based method and a new dataset for remote sensing image change detection,” Remote Sensing , vol. 12, no. 10, p. 1662, 2020
2020
Cited alongside, same era.
Y. Qiu, Y. Satoh, R. Suzuki, K. Iwata, and H. Kataoka, “3d-aware scene change captioning from multiview images,” IEEE Robotics and Automation Letters , vol. 5, no. 3, pp. 4743–4750, 2020
2020
Cited alongside, same era.
M. Hosseinzadeh and Y. Wang, “Image change captioning by learning from an auxiliary task,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 2725–2734
2021
Cited alongside, same era.
Y. Xu and P. Ghamisi, “Consistency-regularized region-growing network for semantic segmentation of urban scenes with point-level annotations,” IEEE Transactions on Image Processing , vol. 31, pp. 5038–5051, 2022
2022
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L. Yao, W. Wang, and Q. Jin, “Image difference captioning with pre-training and contrastive learning,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 36, no. 3, 2022, pp. 3108–3116
2022
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Z. Guo, T.-J. Wang, and J. Laaksonen, “Clip4idc: Clip for image difference captioning,” in Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing , 2022, pp. 33–42
2022
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2022
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2022
Later among the works it cites.
C. Liu, R. Zhao, and Z. Shi, “Remote-sensing image captioning based on multilayer aggregated transformer,” IEEE Geoscience and Remote Sensing Letters , vol. 19, pp. 1–5, 2022
2022
Later among the works it cites.
J. Ji, Y. Ma, X. Sun, Y. Zhou, Y. Wu, and R. Ji, “Knowing what to learn: a metric-oriented focal mechanism for image captioning,” IEEE Transactions on Image Processing , vol. 31, pp. 4321–4335, 2022
2022
Later among the works it cites.
K. E. Ak, Y. Sun, and J. H. Lim, “Learning by imagination: A joint framework for text-based image manipulation and change captioning,” IEEE Transactions on Multimedia , 2022
2022
Later among the works it cites.
S. Yue, Y. Tu, L. Li, Y. Yang, S. Gao, and Z. Yu, “I3n: Intra-and inter-representation interaction network for change captioning,” IEEE Transactions on Multimedia , 2023
2023
Closest in time.
Y. Tu, L. Li, L. Su, J. Du, K. Lu, and Q. Huang, “Adaptive representation disentanglement network for change captioning,” IEEE Transactions on Image Processing , 2023
2023
Closest in time.
2023
Closest in time.
Y. Tu, L. Li, L. Su, K. Lu, and Q. Huang, “Neighborhood contrastive transformer for change captioning,” IEEE Transactions on Multimedia , 2023
2023
Closest in time.
K. Xu, J. Ba, R. Kiros, K. Cho, A. Courville, R. Salakhudinov, R. Zemel, and Y. Bengio, “Show, attend and tell: Neural image caption generation with visual attention,” in International Conference on Machine Learning . PMLR, 2015, pp. 2048–2057
2057
Closest in time.