Reliable Boundary Samples-Based Proxy Pairs for Unsupervised Person Re-identification
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-8555-5_24
Reference24 articles.
1. Chen, H., Lagadec, B., Bremond, F.: Ice: Inter-instance contrastive encoding for unsupervised person re-identification. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14960–14969 (2021)
2. Chen, T., et al.: Abd-net: attentive but diverse person re-identification. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 8351–8361 (2019)
3. Chen, Z., Cui, Z., Zhang, C., Zhou, J., Liu, Y.: Dual clustering co-teaching with consistent sample mining for unsupervised person re-identification. IEEE Trans. Circuits Syst. Video Technol. (2023)
4. Cheng, D., Zhou, J., Wang, N., Gao, X.: Hybrid dynamic contrast and probability distillation for unsupervised person re-id. IEEE Trans. Image Process. 31, 3334–3346 (2022). https://doi.org/10.1109/TIP.2022.3169693
5. Cho, Y., Kim, W.J., Hong, S., Yoon, S.E.: Part-based pseudo label refinement for unsupervised person re-identification. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7308–7318 (2022)
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