{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T22:57:04Z","timestamp":1772837824852,"version":"3.50.1"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2022,9,19]],"date-time":"2022-09-19T00:00:00Z","timestamp":1663545600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,9,19]],"date-time":"2022-09-19T00:00:00Z","timestamp":1663545600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SIViP"],"published-print":{"date-parts":[[2023,6]]},"DOI":"10.1007\/s11760-022-02354-5","type":"journal-article","created":{"date-parts":[[2022,9,19]],"date-time":"2022-09-19T19:05:38Z","timestamp":1663614338000},"page":"1457-1464","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Graph correlation-refined centroids for unsupervised person re-identification"],"prefix":"10.1007","volume":"17","author":[{"given":"Xin","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Keren","family":"Fu","sequence":"additional","affiliation":[]},{"given":"Yanci","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,9,19]]},"reference":[{"key":"2354_CR1","doi-asserted-by":"crossref","unstructured":"Sun, Y., Zheng, L., Yang, Y., Tian, Q., Wang, S.: Beyond part models: Person retrieval with refined part pooling. In: Proceedings of the European Conference on Computer Vision (2018)","DOI":"10.1007\/978-3-030-01225-0_30"},{"key":"2354_CR2","doi-asserted-by":"crossref","unstructured":"Luo, H., Gu, Y., Liao, X., Lai, S., Jiang, W.: Bag of tricks and a strong baseline for deep person re-identification. In: CVPR Workshops (2019)","DOI":"10.1109\/CVPRW.2019.00190"},{"key":"2354_CR3","doi-asserted-by":"crossref","unstructured":"Chen, H., Lagadec, B., Bremond, F.: Learning discriminative and generalizable representations by spatial-channel partition for person re-identification. In: WACV (2020)","DOI":"10.1109\/WACV45572.2020.9093541"},{"key":"2354_CR4","doi-asserted-by":"crossref","unstructured":"Wu, Q., Dai, P., Chen, P., et al.: Deep adversarial data augmentation with attribute guided for person re-identification. In: Proceedings of the SIViP 15, 655\u2013662 (2021)","DOI":"10.1007\/s11760-019-01523-3"},{"key":"2354_CR5","doi-asserted-by":"crossref","unstructured":"Zou, Y., Yang, X., Yu, Z., Kumar, B.V.K., Kautz, J.: Joint disentangling and adaptation for crossdomain person re-identification. In: Proceedings of the European Conference on Computer Vision (2020)","DOI":"10.1007\/978-3-030-58536-5_6"},{"key":"2354_CR6","doi-asserted-by":"crossref","unstructured":"Zhong, Z., Zheng, L., Luo, Z., Li, S., Yang, Y.: Invariance matters: exemplar memory for domain adaptive person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 598\u2013607 (2019)","DOI":"10.1109\/CVPR.2019.00069"},{"key":"2354_CR7","first-page":"8738","volume":"33","author":"Y Lin","year":"2019","unstructured":"Lin, Y., Dong, X., Zheng, L., Yan, Y., Yang, Y.: A bottom-up clustering approach to unsupervised person re-identification. Proc. AAAI Conf. Artif. Intell. 33, 8738\u20138745 (2019)","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"2354_CR8","doi-asserted-by":"crossref","unstructured":"Lin, Y., Xie, L., Wu, Y., Yan, C., Tian, Q.: Unsupervised person re-identification via softened similarity learning. In: CVPR, pp. 3390\u20133399 (2020)","DOI":"10.1109\/CVPR42600.2020.00345"},{"key":"2354_CR9","doi-asserted-by":"crossref","unstructured":"Zeng, K., Ning, M., Wang, Y., Guo, Y.: Hierarchical clustering with hard-batch triplet loss for person re-identification. In: CVPR, pp. 657\u2013665 (2020)","DOI":"10.1109\/CVPR42600.2020.01367"},{"key":"2354_CR10","unstructured":"Ge, Y., Zhu, F., Chen, D., Zhao, R.: Self-paced contrastive learning with hybrid memory for domain adaptive object re-id. Adv. Neural Inf. Process. Syst., 33, 11309-11321 (2020)"},{"key":"2354_CR11","unstructured":"Dai, Z., Wang, G., Yuan, W., et al.: Cluster contrast for unsupervised person re-identification. arXiv:2103.11568 (2021)"},{"key":"2354_CR12","doi-asserted-by":"crossref","unstructured":"Chen, H., Lagadec, B., Bremond, F.: ICE: inter-instance contrastive encoding for unsupervised person re-identification. CoRR, arXiv:2103.16364 (2021)","DOI":"10.1109\/ICCV48922.2021.01469"},{"key":"2354_CR13","doi-asserted-by":"crossref","unstructured":"Wang, D., Zhang, S.: Unsupervised person re-identification via multi-label classification. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.01099"},{"key":"2354_CR14","doi-asserted-by":"crossref","unstructured":"Wang, M., Lai, B., Huang, J., Gong, X., Hua, X.-S.: Camera-aware proxies for unsupervised person re-identification. In: AAAI (2021)","DOI":"10.1109\/WACV48630.2021.00327"},{"key":"2354_CR15","doi-asserted-by":"crossref","unstructured":"Wu, Y., Wu, X., Li, X., Tian, J.: MGH:metadata guided hypergraph modeling for unsupervised person re-identification. In: ACM Multimedia, pp.1571\u20131580 (2021)","DOI":"10.1145\/3474085.3475296"},{"key":"2354_CR16","unstructured":"Chen, T., Kornblith, S., Norouzi, M., et al.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, PMLR, pp. 1597\u20131607 (2020)"},{"key":"2354_CR17","doi-asserted-by":"crossref","unstructured":"He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.: Momentum contrast for unsupervised visual representation learning. In: CVPR, pp. 9729\u20139738 (2020)","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"2354_CR18","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"2354_CR19","unstructured":"MacQueen, J., et al.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability 1, 281\u2013297 (1967)"},{"key":"2354_CR20","unstructured":"Ester, M., Kriegel, H.-P., Sander, J., Xiaowei, X., et al.: A density-based algorithm for discovering clusters in large spatial databases with noise. ACM SIGKDD Conf. Knowl. Discov. Data Mining 96, 226\u2013231 (1996)"},{"key":"2354_CR21","doi-asserted-by":"crossref","unstructured":"Li, S., Bak, S., Carr, P., Wang, X.: Diversity regularized spatiotemporal attention for video-based person reidentification. In: CVPR, 369\u2013378 (2018)","DOI":"10.1109\/CVPR.2018.00046"},{"key":"2354_CR22","doi-asserted-by":"crossref","unstructured":"Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 7794\u20137803 (2018)","DOI":"10.1109\/CVPR.2018.00813"},{"key":"2354_CR23","doi-asserted-by":"crossref","unstructured":"Zhang, X., Hou, M., Deng, X. et al.: Multi-cascaded attention and overlapping part features network for person re-identification. In: Proceedings of the SIViP (2022)","DOI":"10.1007\/s11760-021-02106-x"},{"key":"2354_CR24","unstructured":"van den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding. arXiv:1807.03748, (2018)"},{"key":"2354_CR25","doi-asserted-by":"crossref","unstructured":"Zheng, L., Shen, L., Tian, L., Wang, S., Wang, J., Tian, Q.: Scalable person re-identification: A benchmark. In: ICCV, pp. 1116\u20131124 (2015)","DOI":"10.1109\/ICCV.2015.133"},{"key":"2354_CR26","doi-asserted-by":"crossref","unstructured":"Ristani, E., Solera, F., Zou, R., Cucchiara, R., Tomasi, C.: Performance measures and a data set for multi-target, multi-camera tracking. In: ECCV, pp. 17\u201335 (2016)","DOI":"10.1007\/978-3-319-48881-3_2"},{"key":"2354_CR27","doi-asserted-by":"crossref","unstructured":"Zhang, Z.., Lan, C., Zeng, W., Jin, X., Chen, Z.: Relation-aware global attention for person re-identification. In: CVPR, pp. 3186\u20133195 (2020)","DOI":"10.1109\/CVPR42600.2020.00325"},{"key":"2354_CR28","doi-asserted-by":"crossref","unstructured":"Fu, Y., Wei, Y., Wang, G., Zhou, Y., Shi, H., Huang, T.S.: Self-similarity grouping: A simple unsupervised cross domain adaptation approach for person re-identification. In: ICCV, 6112\u20136121 (2019)","DOI":"10.1109\/ICCV.2019.00621"},{"key":"2354_CR29","doi-asserted-by":"crossref","unstructured":"Zhai, Y., Lu, S., Ye, Q., Shan, X., Tian, Y.: Ad-cluster: Augmented discriminative clustering for domain adaptive person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2020)","DOI":"10.1109\/CVPR42600.2020.00904"},{"key":"2354_CR30","unstructured":"Ge, Y., Chen, D., Li, H.: Mutual mean-teaching: Pseudo label refinery for unsupervised domain adaptation on person re-identification. In: International Conference on Learning Representations (2020)"},{"key":"2354_CR31","doi-asserted-by":"crossref","unstructured":"Liu, X., Zhang, S.: Graph consistency based mean-teaching for unsupervised domain adaptive person re-identification. In: IJCAI, 874\u2013880 (2021)","DOI":"10.24963\/ijcai.2021\/121"},{"key":"2354_CR32","doi-asserted-by":"crossref","unstructured":"Zheng, K., Lan, C., Zeng, W., Zhang, Z., Zha, Z.-J.: Exploiting sample uncertainty for domain adaptive person re-identification. In: AAAI, 3538\u20133546 (2021)","DOI":"10.1609\/aaai.v35i4.16468"},{"key":"2354_CR33","doi-asserted-by":"crossref","unstructured":"Zheng, K., Liu, W., He, L., Mei, T., Luo, J., Zha, Z.-J.: Group-aware label transfer for domain adaptive person re-identification. In: CVPR, 5310\u20135319 (2021)","DOI":"10.1109\/CVPR46437.2021.00527"},{"key":"2354_CR34","doi-asserted-by":"crossref","unstructured":"Zheng, Y., Tang, S., Teng, G., Ge, Y., Liu, K., Qin, J., Qi, D., Chen, D.: Online pseudo label generation by hierarchical cluster dynamics for adaptive person re-identification. In: ICCV, pp. 8371\u20138381 (2021)","DOI":"10.1109\/ICCV48922.2021.00826"},{"key":"2354_CR35","doi-asserted-by":"crossref","unstructured":"Wang, Z., Zhang, J., Zheng, L., Liu, Y., Sun, Y., Li, Y., Wang, S.: Cycas: Self-supervised cycle association for learning re-identifiable descriptions. In: CycAs: Self-supervised Cycle Association for Learning Re-identifiable Descriptions (2020)","DOI":"10.1007\/978-3-030-58621-8_5"},{"key":"2354_CR36","doi-asserted-by":"crossref","unstructured":"Xuan, S., Zhang, S.: Intra-inter camera similarity for unsupervised person re-identification. In: CVPR, 11926\u201311935 ( 2021)","DOI":"10.1109\/CVPR46437.2021.01175"}],"container-title":["Signal, Image and Video Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-022-02354-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11760-022-02354-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-022-02354-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,24]],"date-time":"2023-04-24T05:23:29Z","timestamp":1682313809000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11760-022-02354-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,19]]},"references-count":36,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2023,6]]}},"alternative-id":["2354"],"URL":"https:\/\/doi.org\/10.1007\/s11760-022-02354-5","relation":{},"ISSN":["1863-1703","1863-1711"],"issn-type":[{"value":"1863-1703","type":"print"},{"value":"1863-1711","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9,19]]},"assertion":[{"value":"15 June 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 June 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 August 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 September 2022","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}]}}