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Conventional existing retrieval methods in remote sensing (RS) are often based on a uni-modal data retrieval framework.
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” in 2009 IEEE conference on computer vision and pattern recognition . Ieee, 2009, pp. 248–255
2009
Earlier work this paper cites.
G. Chechik, V. Sharma, U. Shalit, and S. Bengio, “Large scale online learning of image similarity through ranking.” Journal of Machine Learning Research , vol. 11, no. 3, 2010
2010
Earlier work this paper cites.
Y. Yang and S. Newsam, “Bag-of-visual-words and spatial extensions for land-use classification,” in Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems , ser. GIS ’10. New York, NY, USA: ACM, 2010, pp. 270–279. [Online]. Available: http://doi.acm.org/10.1145/1869790.1869829
2010
Earlier work this paper cites.
T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean, “Distributed representations of words and phrases and their compositionality,” in Advances in neural information processing systems , 2013, pp. 3111–3119
2013
Earlier work this paper cites.
T. Jiang, G.-S. Xia, and Q. Lu, “Sketch-based aerial image retrieval,” in 2017 IEEE International Conference on Image Processing (ICIP) . IEEE, 2017, pp. 3690–3694
2017
Earlier work this paper cites.
G. Cheng, Z. Li, X. Yao, L. Guo, and Z. Wei, “Remote sensing image scene classification using bag of convolutional features,” IEEE Geosci. Remote Sens. Lett. , vol. 14, no. 10, pp. 1735–1739, 2017
2017
Cited alongside, same era.
C. Szegedy, S. Ioffe, V. Vanhoucke, and A. A. Alemi, “Inception-v4, inception-resnet and the impact of residual connections on learning.” in AAAI , vol. 4, 2017, p. 12
2017
Cited alongside, same era.
S. Kiran Yelamarthi, S. Krishna Reddy, A. Mishra, and A. Mittal, “A zero-shot framework for sketch based image retrieval,” in Proceedings of the European Conference on Computer Vision (ECCV) , 2018, pp. 300–317
2018
Cited alongside, same era.
Y. Shen, L. Liu, F. Shen, and L. Shao, “Zero-shot sketch-image hashing,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 3598–3607
2018
Cited alongside, same era.
F. Xu, R. Zhang, W. Yang, and G.-S. Xia, “Mental retrieval of large-scale satellite images via learned sketch-image deep features,” in IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium . IEEE, 2019, pp. 3356–3359
2019
Later among the works it cites.
U. Chaudhuri, B. Banerjee, A. Bhattacharya, and M. Datcu, “A simplified framework for zero-shot cross-modal sketch data retrieval,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops , 2020, pp. 182–183
2020
Closest in time.
F. Xu, W. Yang, T. Jiang, S. Lin, H. Luo, and G. Xia, “Mental retrieval of remote sensing images via adversarial sketch-image feature learning,” IEEE Trans. Geosci. Remote Sens. , pp. 1–14, 2020
2020
Closest in time.
U. Chaudhuri, B. Banerjee, A. Bhattacharya, and M. Datcu, “Cmir-net: A deep learning based model for cross-modal retrieval in remote sensing,” Pattern Recognit. Lett. , vol. 131, pp. 456–462, 2020
2020
Closest in time.
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