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Person re-identification (ReID) aims at searching the same identity person among images captured by various cameras.
2010
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J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2015, pp. 3431–3440
2015
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S. Liao, Y. Hu, X. Zhu, and S. Z. Li, “Person re-identification by local maximal occurrence representation and metric learning,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2015, pp. 2197–2206
2015
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L. Zheng, L. Shen, L. Tian, S. Wang, J. Wang, and Q. Tian, “Scalable person re-identification: A benchmark,” in Proceedings of the IEEE international conference on computer vision , 2015, pp. 1116–1124
2015
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L. Zheng, L. Shen, L. Tian, S. Wang, J. Wang, and Q. Tian, “Scalable person re-identification: A benchmark,” in 2015 IEEE International Conference on Computer Vision (ICCV) , 2015, pp. 1116–1124
2015
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E. Ristani, F. Solera, R. Zou, R. Cucchiara, and C. Tomasi, “Performance measures and a data set for multi-target, multi-camera tracking,” in European Conference on Computer Vision workshop on Benchmarking Multi-Target Tracking , 2016
2016
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L. Zheng, Z. Bie, Y. Sun, J. Wang, C. Su, S. Wang, and Q. Tian, “Mars: A video benchmark for large-scale person re-identification,” in Computer Vision – ECCV 2016 , B. Leibe, J. Matas, N. Sebe, and M. Welling, Eds. Cham: Springer International Publishing, 2016, pp. 868–884
2016
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L. Zhao, X. Li, Y. Zhuang, and J. Wang, “Deeply-learned part-aligned representations for person re-identification,” in The IEEE International Conference on Computer Vision (ICCV) , 2017, pp. 3219–3228
2017
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J. Wang, X. Zhu, S. Gong, and W. Li, “Transferable joint attribute-identity deep learning for unsupervised person re-identification,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 2275–2284
2018
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W. Deng, L. Zheng, Q. Ye, G. Kang, Y. Yang, and J. Jiao, “Image-image domain adaptation with preserved self-similarity and domain-dissimilarity for person re-identification,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 994–1003
2018
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Y. Sun, L. Zheng, Y. Yang, Q. Tian, and S. Wang, “Beyond part models: Person retrieval with refined part pooling (and a strong convolutional baseline),” in Proceedings of the European Conference on Computer Vision (ECCV) , 2018, pp. 480–496
2018
Earlier work this paper cites.
Z. Zhong, L. Zheng, S. Li, and Y. Yang, “Generalizing a person retrieval model hetero-and homogeneously,” in Proceedings of the European Conference on Computer Vision (ECCV) , 2018, pp. 172–188
2018
Cited alongside, same era.
Y. Wu, Y. Lin, X. Dong, Y. Yan, and Y. Yang, “Exploit the unknown gradually: One-shot video-based person re-identification by stepwise learning,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2018
2018
Cited alongside, same era.
M. Ye, X. Lan, and P. C. Yuen, “Robust anchor embedding for unsupervised video person re-identification in the wild,” in Proceedings of the European Conference on Computer Vision (ECCV) , 2018, pp. 170–186
2018
Cited alongside, same era.
S. Gidaris, P. Singh, and N. Komodakis, “Unsupervised representation learning by predicting image rotations,” in The International Conference on Learning Representations , 2018
2018
Cited alongside, same era.
Y. Wu, Y. Lin, X. Dong, Y. Yan, W. Bian, and Y. Yang, “Progressive learning for person re-identification with one example,” IEEE Transactions on Image Processing , vol. PP, no. 6, pp. 1–1, 2019
2019
Later among the works it cites.
G. Ding, S. H. Khan, and Z. Tang, “Dispersion based clustering for unsupervised person re-identification.” in BMVC , 2019, p. 264
2019
Later among the works it cites.
Y. Ge, F. Zhu, D. Chen, R. Zhao, and H. Li, “Self-paced contrastive learning with hybrid memory for domain adaptive object re-id,” in Advances in Neural Information Processing Systems , 2020
2020
Closest in time.
D. Wang and S. Zhang, “Unsupervised person re-identification via multi-label classification,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 10 981–10 990
2020
Closest in time.
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Z. Wu, Y. Xiong, S. Yu, and D. Lin, “Unsupervised feature learning via non-parametric instance-level discrimination,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018
2018
Cited alongside, same era.
L. Wei, S. Zhang, W. Gao, and Q. Tian, “Person transfer gan to bridge domain gap for person re-identification,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2018
2018
Cited alongside, same era.
Y. Wu, Y. Lin, X. Dong, Y. Yan, W. Ouyang, and Y. Yang, “Exploit the unknown gradually: One-shot video-based person re-identification by stepwise learning,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2018, pp. 5177–5186
2018
Cited alongside, same era.
Y. Fu, Y. Wei, G. Wang, Y. Zhou, H. Shi, and T. Huang, “Self-similarity grouping: A simple unsupervised cross domain adaptation approach for person re-identification,” in 2019 International Conference on Computer Vision (ICCV) , 2018
2018
Cited alongside, same era.
2018
Cited alongside, same era.
J. Liu, Z.-J. Zha, D. Chen, R. Hong, and M. Wang, “Adaptive transfer network for cross-domain person re-identification,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 7202–7211
2019
Cited alongside, same era.
Y. Lin, X. Dong, L. Zheng, Y. Yan, and Y. Yang, “A bottom-up clustering approach to unsupervised person re-identification,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 33, 2019, pp. 8738–8745
2019
Cited alongside, same era.
Y. Lin, L. Xie, Y. Wu, C. Yan, and Q. Tian, “Unsupervised person re-identification via softened similarity learning,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 3390–3399
2020
Closest in time.
2020
Closest in time.
K. He, H. Fan, Y. Wu, S. Xie, and R. Girshick, “Momentum contrast for unsupervised visual representation learning,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 9729–9738
2020
Closest in time.
X. Jin, C. Lan, W. Zeng, Z. Chen, and L. Zhang, “Style normalization and restitution for generalizable person re-identification,” in 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2020, pp. 3140–3149
2020
Closest in time.
G. Wu, X. Zhu, and S. Gong, “Tracklet self-supervised learning for unsupervised person re-identification,” Proceedings of the AAAI Conference on Artificial Intelligence , vol. 34, no. 7, pp. 12 362–12 369, 2020
2020
Closest in time.
Z. Zhong, L. Zheng, Z. Luo, S. Li, and Y. Yang, “Invariance matters: Exemplar memory for domain adaptive person re-identification,” in 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2020
2020
Closest in time.
D. Wang and S. Zhang, “Unsupervised person re-identification via multi-label classification,” in 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2020
2020
Closest in time.