A novel image restoration solution for cross-resolution person re-identification
Author:
Funder
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s00371-024-03471-7.pdf
Reference51 articles.
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2. Zhang, G., Ge, Y., Dong, Z., Wang, H., Zheng, Y., Chen, S.: Deep high-resolution representation learning for cross-resolution person re-identification. IEEE Trans. Image Process. 30, 8913–8925 (2021). https://doi.org/10.1109/TIP.2021.3120054
3. Sun, Y., Zheng, L., Yang, Y., Tian, Q., Wang, S.: Beyond part models: Person retrieval with refined part pooling (and a strong convolutional baseline). In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 480–496 (2018). https://doi.org/10.1007/978-3-030-01225-0_30
4. Zhang, J., Lei, J., Yang, S., Yang, X.: Semantic interaction learning for fine-grained vehicle recognition. Comput. Anim. Virtual Worlds 33(1), 2036 (2022). https://doi.org/10.1002/cav.2036
5. Xia, D., Guo, F., Liu, H., Yu, S.: Unsupervised learning of visual invariant features for person re-identification. J. Intell. Fuzzy Syst. 39(5), 7495–7503 (2020). https://doi.org/10.3233/JIFS-200793
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