Optoelectronics Letters, Volume. 20, Issue 12, 749(2024)
Representation strategy for unsupervised domain adaptation on person re-identification
[1] [1] XIONG L, TANG G. Multi-object tracking based on deep associated features for UAV applications[J]. Optoelectronics letters, 2023, 19(2): 105-111.
[2] [2] YU H X, ZHENG W S, WU A, et al. Unsupervised person re-identification by soft multilabel learning[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 15-20, 2019, Long Beach, CA, USA. New York: IEEE, 2019: 2148-2157.
[3] [3] LI X, ZHANG T, ZHAO X, et al. Learning fused features with parallel training for person re-identification[J]. Knowledge-based systems, 2021, 220: 106941.
[4] [4] SONG L, WANG C, ZHANG L, et al. Unsupervised domain adaptive re-identification: theory and practice[J]. Pattern recognition, 2020, 102: 107173.
[5] [5] FU Y, WEI Y, WANG G, et al. Self-similarity grouping: a simple unsupervised cross domain adaptation approach for person re-identification[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision, October 27-November 2, 2019, Seoul, Korea (South). New York: IEEE, 2019: 6112-6121.
[6] [6] YANG F, LI K, ZHONG Z, et al. Asymmetric co-teaching for unsupervised cross-domain person re-identification[C]//Proceedings of the AAAI Conference on Artificial Intelligence, February 7-12, 2020, New York, NY, USA. AAAI Press, 2020, 34(07): 12597-12604.
[7] [7] YU H X, WU A, ZHENG W S. Cross-view asymmetric metric learning for unsupervised person re-identification[C]//Proceedings of the IEEE International Conference on Computer Vision, October 22-29, 2017, Venice, Italy. New York: IEEE, 2017: 994-1002.
[8] [8] FAN H, ZHENG L, YAN C, et al. Unsupervised person re-identification: clustering and fine-tuning[J]. ACM transactions on multimedia computing, communications, and applications (TOMM), 2018, 14(4): 1-18.
[9] [9] HAN B, YAO Q, YU X, et al. Co-teaching: robust training of deep neural networks with extremely noisy labels[J]. Advances in neural information processing systems, 2018, 31.
[10] [10] DING S, LIN L, WANG G, et al. Deep feature learning with relative distance comparison for person re-identification[J]. Pattern recognition, 2015, 48(10): 2993-3003.
[11] [11] LI W, ZHAO R, XIAO T, et al. Deepreid: deep filter pairing neural network for person re-identification[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, June 23-28, 2014, Columbus, OH, USA. New York: IEEE, 2014: 152-159.
[12] [12] AHMED E, JONES M, MARKS T K. An improved deep learning architecture for person re-identification[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, June 7-12, 2015, Boston, MA, USA. New York: IEEE, 2015: 3908-3916.
[13] [13] WANG G, YUAN Y, CHEN X, et al. Learning discriminative features with multiple granularities for person re-identification[C]//Proceedings of the 26th ACM International Conference on Multimedia, October 22-26, 2018, Seoul, Korea (South). Association for Computing Machinery, 2018: 274-282.
[14] [14] DAI Z, WANG G, YUAN W, et al. Cluster contrast for unsupervised person re-identification[C]//Proceedings of the Asian Conference on Computer Vision, December 4-8, 2022, Macao, China. Springer Science and Business Media Deutschland GmbH, 2022: 1142-1160.
[15] [15] YAN Y, QIN J, NI B, et al. Learning multi-attention context graph for group-based re-identification[J]. IEEE transactions on pattern analysis and machine intelligence, 2020, 45(6): 7001-7018.
[16] [16] ZHONG Z, ZHENG L, LI S, et al. Generalizing a person retrieval model hetero-and homogeneously[C]//Proceedings of the European Conference on Computer Vision (ECCV), September 8-14, 2018, Munich, Germany. Berlin, Heidelberg: Springer-Verlag, 2018: 172-188.
[17] [17] LIU J, ZHA Z J, CHEN D, et al. Adaptive transfer network for cross-domain person re-identification[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 15-20, 2019, Long Beach, CA, USA. New York: IEEE, 2019: 7202-7211.
[18] [18] HUANG Y, WU Q, XU J S, et al. SBSGAN: suppression of inter-domain background shift for person re-identification[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision, October 27-November 2, 2019, Seoul, Korea (South). New York: IEEE, 2019: 9527-9536.
[19] [19] QI L, WANG L, HUO J, et al. A novel unsupervised camera-aware domain adaptation framework for person re-identification[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision, October 27-November 2, 2019, Seoul, Korea (South). New York: IEEE, 2019: 8080-8089.
[20] [20] ZHONG Z, ZHENG L, LUO Z, et al. Invariance matters: exemplar memory for domain adaptive person re-identification[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 15-20, 2019, Long Beach, CA, USA. New York: IEEE, 2019: 598-607.
[21] [21] LI Y J, LIN C S, LIN Y B, et al. Cross-dataset person re-identification via unsupervised pose disentanglement and adaptation[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision, June 15-20, 2019, Long Beach, CA, USA. New York: IEEE, 2019: 7919-7929.
[22] [22] FU Y, WEI Y, WANG G, et al. Self-similarity grouping: a simple unsupervised cross domain adaptation approach for person re-identification[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision, October 27-November 2, 2019, Seoul, Korea (South). New York: IEEE, 2019: 6112-6121.
[23] [23] ZHANG X, CAO J, SHEN C, et al. Self-training with progressive augmentation for unsupervised cross-domain person re-identification[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision, October 27-November 2, 2019, Seoul, Korea (South). New York: IEEE, 2019: 8222-8231.
[24] [24] ZENG K, NING M, WANG Y, et al. Hierarchical clustering with hard-batch triplet loss for person re-identification[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 13-19, 2020, Seattle, WA, USA. New York: IEEE, 2020: 13657-13665.
[25] [25] JIN X, LAN C, ZENG W, et al. Style normalization and restitution for generalizable person re-identification[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 13-19, 2020, Seattle, WA, USA. New York: IEEE, 2020: 3143-3152.
[26] [26] ZHAI Y, YE Q, LU S, et al. Multiple expert brainstorming for domain adaptive person re-identification[C]//16th European Conference on Computer Vision, August 23-28, 2020, Glasgow, UK. Berlin, Heidelberg: Springer-Verlag, 2020: 594-611.
[27] [27] GE Y, ZHU F, CHEN D, et al. Self-paced contrastive learning with hybrid memory for domain adaptive object re-id[J]. Advances in neural information processing systems, 2020, 33: 11309-11321.
[28] [28] BAI Z, WANG Z, WANG J, et al. Unsupervised multi-source domain adaptation for person re-identification[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 20-25, 2021, Nashville, TN, USA. New York: IEEE, 2021: 12914-12923.
[29] [29] GE Y, ZHU F, CHEN D, et al. Structured domain adaptation with online relation regularization for unsupervised person re-id[J]. IEEE transactions on neural networks and learning systems, 2022.
Get Citation
Copy Citation Text
LI Hao, ZHANG Tao, LI Shuang, LI Xuan, ZHAO Xin. Representation strategy for unsupervised domain adaptation on person re-identification[J]. Optoelectronics Letters, 2024, 20(12): 749
Received: Oct. 19, 2023
Accepted: Dec. 25, 2024
Published Online: Dec. 25, 2024
The Author Email: