A Triplet-loss Dilated Residual Network for High-Resolution Representation Learning in Image Retrieval
Author:
Publisher
Springer Science and Business Media LLC
Subject
Hardware and Architecture,Modeling and Simulation,Information Systems,Signal Processing,Theoretical Computer Science,Control and Systems Engineering
Link
https://link.springer.com/content/pdf/10.1007/s11265-023-01865-9.pdf
Reference54 articles.
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3. Nair, L. R., Subramaniam, K., and Prasannavenkatesan, G. (2020). A review on multiple approaches to medical image retrieval system. Intelligent Computing in Engineering: Select Proceedings of RICE 2019 (pp. 501–509). https://doi.org/10.1007/978-981-15-2780-7_55
4. Zheng, L., Shen, L., Tian, L., Wang, S., Wang, J., and Tian, Q. (2015). Scalable person re-identification: A benchmark. In Proceedings of the IEEE International Conference on Computer Vision (pp. 1116–1124). https://doi.org/10.1109/iccv.2015.133
5. Schonberger, J. L., Radenovic, F., Chum, O., and Frahm, J.-M. (2015). From single image query to detailed 3d reconstruction. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 5126–5134). https://doi.org/10.1109/cvpr.2015.7299148
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