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Image-text retrieval has developed rapidly in recent years.
L. Zhang, M. Yang, C. Li, and R. Xu, “Image-text retrieval via contrastive learning with auxiliary generative features and support-set regularization,” in Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval , 2022, pp. 1938–1943
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S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural computation , vol. 9, no. 8, pp. 1735–1780, 1997
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K. E. Joyce, S. E. Belliss, S. V. Samsonov, S. J. McNeill, and P. J. Glassey, “A review of the status of satellite remote sensing and image processing techniques for mapping natural hazards and disasters,” Progress in physical geography , vol. 33, no. 2, pp. 183–207, 2009
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J. Pennington, R. Socher, and C. D. Manning, “Glove: Global vectors for word representation,” in Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP) , 2014, pp. 1532–1543
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2014
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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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A. Karpathy and L. Fei-Fei, “Deep visual-semantic alignments for generating image descriptions,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2015, pp. 3128–3137
2015
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M. Chi, A. Plaza, J. A. Benediktsson, Z. Sun, J. Shen, and Y. Zhu, “Big data for remote sensing: Challenges and opportunities,” Proceedings of the IEEE , vol. 104, no. 11, pp. 2207–2219, 2016
2016
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J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 779–788
2016
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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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X. Lu, B. Wang, X. Zheng, and X. Li, “Exploring models and data for remote sensing image caption generation,” IEEE Transactions on Geoscience and Remote Sensing , vol. 56, no. 4, pp. 2183–2195, 2017
2017
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H. Nam, J.-W. Ha, and J. Kim, “Dual attention networks for multimodal reasoning and matching,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 299–307
2017
Cited alongside, same era.
G.-S. Xia, J. Hu, F. Hu, B. Shi, X. Bai, Y. Zhong, L. Zhang, and X. Lu, “Aid: A benchmark data set for performance evaluation of aerial scene classification,” IEEE Transactions on Geoscience and Remote Sensing , vol. 55, no. 7, pp. 3965–3981, 2017
2017
Cited alongside, same era.
2017
Cited alongside, same era.
G. Mao, Y. Yuan, and L. Xiaoqiang, “Deep cross-modal retrieval for remote sensing image and audio,” in 2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS) . IEEE, 2018, pp. 1–7
2018
Cited alongside, same era.
Q. Cheng, Y. Zhou, P. Fu, Y. Xu, and L. Zhang, “A deep semantic alignment network for the cross-modal image-text retrieval in remote sensing,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , vol. 14, pp. 4284–4297, 2021
2021
Later among the works it cites.
Z. Yuan, W. Zhang, X. Rong, X. Li, J. Chen, H. Wang, K. Fu, and X. Sun, “A lightweight multi-scale crossmodal text-image retrieval method in remote sensing,” IEEE Transactions on Geoscience and Remote Sensing , vol. 60, pp. 1–19, 2021
2021
Later among the works it cites.
2021
Later among the works it cites.
Z. Yuan, W. Zhang, C. Tian, X. Rong, Z. Zhang, H. Wang, K. Fu, and X. Sun, “Remote sensing cross-modal text-image retrieval based on global and local information,” IEEE Transactions on Geoscience and Remote Sensing , vol. 60, pp. 1–16, 2022
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K.-H. Lee, X. Chen, G. Hua, H. Hu, and X. He, “Stacked cross attention for image-text matching,” in Proceedings of the European conference on computer vision (ECCV) , 2018, pp. 201–216
2018
Cited alongside, same era.
Y. Zhao, X. Ni, Y. Ding, and Q. Ke, “Paragraph-level neural question generation with maxout pointer and gated self-attention networks,” in Proceedings of the 2018 conference on empirical methods in natural language processing , 2018, pp. 3901–3910
2018
Cited alongside, same era.
G.-S. Xia, X. Bai, J. Ding, Z. Zhu, S. Belongie, J. Luo, M. Datcu, M. Pelillo, and L. Zhang, “Dota: A large-scale dataset for object detection in aerial images,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 3974–3983
2018
Cited alongside, same era.
Z. Wang, X. Liu, H. Li, L. Sheng, J. Yan, X. Wang, and J. Shao, “Camp: Cross-modal adaptive message passing for text-image retrieval,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2019, pp. 5764–5773
2019
Cited alongside, same era.
Z. Ji, H. Wang, J. Han, and Y. Pang, “Saliency-guided attention network for image-sentence matching,” in Proceedings of the IEEE/CVF international conference on computer vision , 2019, pp. 5754–5763
2019
Cited alongside, same era.
J. Ding, N. Xue, Y. Long, G.-S. Xia, and Q. Lu, “Learning roi transformer for oriented object detection in aerial images,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 2849–2858
2019
Cited alongside, same era.
2020
Cited alongside, same era.
T. Abdullah, Y. Bazi, M. M. Al Rahhal, M. L. Mekhalfi, L. Rangarajan, and M. Zuair, “Textrs: Deep bidirectional triplet network for matching text to remote sensing images,” Remote Sensing , vol. 12, no. 3, p. 405, 2020
2020
Cited alongside, same era.
2022
Later among the works it cites.
L. Mi, S. Li, C. Chappuis, and D. Tuia, “Knowledge-aware cross-modal text-image retrieval for remote sensing images,” in Proceedings of the Second Workshop on Complex Data Challenges in Earth Observation (CDCEO 2022) , 2022
2022
Later among the works it cites.
Q. Cheng, H. Huang, Y. Xu, Y. Zhou, H. Li, and Z. Wang, “Nwpu-captions dataset and mlca-net for remote sensing image captioning,” IEEE Transactions on Geoscience and Remote Sensing , vol. 60, pp. 1–19, 2022
2022
Later among the works it cites.
C. Liu, R. Zhao, H. Chen, Z. Zou, and Z. Shi, “Remote sensing image change captioning with dual-branch transformers: A new method and a large scale dataset,” IEEE Transactions on Geoscience and Remote Sensing , vol. 60, pp. 1–20, 2022
2022
Later among the works it cites.
2022
Later among the works it cites.
L. Li, X. Yao, G. Cheng, and J. Han, “Aifs-dataset for few-shot aerial image scene classification,” IEEE Transactions on Geoscience and Remote Sensing , vol. 60, pp. 1–11, 2022
2022
Later among the works it cites.
2022
Later among the works it cites.
H. Zhang, Y. Sun, Y. Liao, S. Xu, R. Yang, S. Wang, B. Hou, and L. Jiao, “A transformer-based cross-modal image-text retrieval method using feature decoupling and reconstruction,” in IGARSS 2022-2022 IEEE International Geoscience and Remote Sensing Symposium . IEEE, 2022, pp. 1796–1799
2022
Later among the works it cites.
J. Li, L. Niu, and L. Zhang, “Action-aware embedding enhancement for image-text retrieval,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 36, no. 2, 2022, pp. 1323–1331
2022
Later among the works it cites.
L. Li, X. Yao, G. Cheng, M. Xu, J. Han, and J. Han, “Solo-to-collaborative dual-attention network for one-shot object detection in remote sensing images,” IEEE Transactions on Geoscience and Remote Sensing , vol. 60, pp. 1–11, 2022
2022
Later among the works it cites.
2022
Later among the works it cites.
L. Li, X. Yao, X. Wang, D. Hong, G. Cheng, and J. Han, “Robust few-shot aerial image object detection via unbiased proposals filtration,” IEEE Transactions on Geoscience and Remote Sensing , vol. 61, pp. 1–11, 2023
2023
Closest in time.
W. Zhang, J. Li, S. Li, J. Chen, W. Zhang, X. Gao, and X. Sun, “Hypersphere-based remote sensing cross-modal text-image retrieval via curriculum learning,” IEEE Transactions on Geoscience and Remote Sensing , 2023
2023
Closest in time.
J. Pan, Q. Ma, and C. Bai, “Reducing semantic confusion: Scene-aware aggregation network for remote sensing cross-modal retrieval,” in Proceedings of the 2023 ACM International Conference on Multimedia Retrieval , 2023, pp. 398–406
2023
Closest in time.