{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T05:42:14Z","timestamp":1757310134528},"reference-count":14,"publisher":"Institute of Electronics, Information and Communications Engineers (IEICE)","issue":"7","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEICE Trans. Inf. &amp; Syst."],"published-print":{"date-parts":[[2022,7,1]]},"DOI":"10.1587\/transinf.2021edl8101","type":"journal-article","created":{"date-parts":[[2022,6,30]],"date-time":"2022-06-30T22:18:39Z","timestamp":1656627519000},"page":"1356-1360","source":"Crossref","is-referenced-by-count":5,"title":["Gray Augmentation Exploration with All-Modality Center-Triplet Loss for Visible-Infrared Person Re-Identification"],"prefix":"10.1587","volume":"E105.D","author":[{"given":"Xiaozhou","family":"CHENG","sequence":"first","affiliation":[{"name":"School of Information and Control Engineering, China University of Mining and Technology"},{"name":"Sinostell Maanshan General Institute of Mining Research Co., Ltd."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rui","family":"LI","sequence":"additional","affiliation":[{"name":"School of Information and Control Engineering, China University of Mining and Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanjing","family":"SUN","sequence":"additional","affiliation":[{"name":"School of Information and Control Engineering, China University of Mining and Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu","family":"ZHOU","sequence":"additional","affiliation":[{"name":"School of Information and Control Engineering, China University of Mining and Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kaiwen","family":"DONG","sequence":"additional","affiliation":[{"name":"School of Information and Control Engineering, China University of Mining and Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"532","reference":[{"key":"1","doi-asserted-by":"crossref","unstructured":"[1] Z. Zhong, L. Zheng, Z.-D. Zheng, S.-Z. Li, and Y. Yang, \u201cCamera style adaptation for person re-identification,\u201d Proc. IEEE Conf. Comput. Vis. Pattern Recognit., pp.5157-5166, 2018. 10.1109\/cvpr.2018.00541","DOI":"10.1109\/CVPR.2018.00541"},{"key":"2","doi-asserted-by":"crossref","unstructured":"[2] A. Wu, W.-S. Zheng, H.-X. Yu, S. Gong, and J. Lai, \u201cRgb-infrared cross-modality person re-identification,\u201d Proc. IEEE Int. Conf. Comput. Vis., pp.5380-5389, 2017. 10.1109\/iccv.2017.575","DOI":"10.1109\/ICCV.2017.575"},{"key":"3","doi-asserted-by":"publisher","unstructured":"[3] M. Ye, J.-B. Shen, G.-J. Lin, T. Xiang, L. Shao, and S.C.H. Hoi, \u201cDeep learning for person re-identification: A survey and outlook,\u201d IEEE Trans. Pattern Anal. Mach. Intell., vol.44, no.6, pp.2872-2893, 2021. DOI: 10.1109\/TPAMI.2021.3054775. 10.1109\/tpami.2021.3054775","DOI":"10.1109\/TPAMI.2021.3054775"},{"key":"4","doi-asserted-by":"crossref","unstructured":"[4] Z. Wang, Z. Wang, Y. Zheng, Y.-Y. Chuang, and S. Satoh, \u201cLearning to reduce dual-level discrepancy for infrared-visible person reidentification,\u201d Proc. IEEE Conf. Comput. Vis. Pattern Recognit., pp.618-626, 2019. 10.1109\/cvpr.2019.00071","DOI":"10.1109\/CVPR.2019.00071"},{"key":"5","doi-asserted-by":"crossref","unstructured":"[5] M. Ye, X. Lan, J. Li, and P.C. Yuen, \u201cHierarchical discriminative learning for visible thermal person re-identification,\u201d Proc. AAAI, pp.7501-7508, 2018.","DOI":"10.1609\/aaai.v32i1.12293"},{"key":"6","doi-asserted-by":"crossref","unstructured":"[6] M. Ye, X.-Y. Lan, and Q.-M. Leng, \u201cModality-aware collaborative learning for visible thermal person re-identification,\u201d ACM Multimedia, pp.347-355, 2019. 10.1145\/3343031.3351043","DOI":"10.1145\/3343031.3351043"},{"key":"7","doi-asserted-by":"publisher","unstructured":"[7] H.-J. Liu, J. Cheng, W. Wang, Y. Su, and H. Bai, \u201cEnhancing the discriminative feature learning for visible-thermal cross-modality person re-identification,\u201d Neurocomputing, vol.398, pp.11-19, 2020. 10.1016\/j.neucom.2020.01.089","DOI":"10.1016\/j.neucom.2020.01.089"},{"key":"8","doi-asserted-by":"publisher","unstructured":"[8] Y. Zhu, Z. Yang, L. Wang, S. Zhao, X. Hu, and D. Tao, \u201cHetero-center loss for cross-modality person re-identification,\u201d Neurocomputing, vol.386, pp.97-109, 2020. 10.1016\/j.neucom.2019.12.100","DOI":"10.1016\/j.neucom.2019.12.100"},{"key":"9","doi-asserted-by":"crossref","unstructured":"[9] M. Ye, J. Shen, D.J. Crandall, L. Shao, and J.-B. Luo, \u201cDynamic Dual-Attentive Aggregation Learning for Visible-Infrared Person Re-Identification,\u201d Proc. IEEE Int. Conf. Eur. Conf. Comput. Vis., pp.229-247, 2020. 10.1007\/978-3-030-58520-4_14","DOI":"10.1007\/978-3-030-58520-4_14"},{"key":"10","doi-asserted-by":"publisher","unstructured":"[10] H.-R. Ye, H. Liu, F.-Y. Meng, and X. Li, \u201cBi-directional Exponential Angular Triplet Loss for RGB-Infrared Person Re-Identification,\u201d IEEE Trans. Image Process., vol.30, pp.1583-1595, 2020. DOI: 10.1109\/TIP.2020.3045261. 10.1109\/tip.2020.3045261","DOI":"10.1109\/TIP.2020.3045261"},{"key":"11","doi-asserted-by":"crossref","unstructured":"[11] G. Wang, T. Zhang, J. Cheng, S. Liu, Y. Yang, and Z. Hou, \u201cRgb-infrared cross-modality person re-identification via joint pixel and feature alignment,\u201d Proc. IEEE Int. Conf. Comput. Vis., pp.3623-3632, 2019. 10.1109\/iccv.2019.00372","DOI":"10.1109\/ICCV.2019.00372"},{"key":"12","doi-asserted-by":"publisher","unstructured":"[12] D.-G. Li, X. Wei, X. Hong, and Y. Gong, \u201cInfrared-visible cross-modal person re-identification with an X modality,\u201d Proc. AAAI, pp.4610-4617, 2020. 10.1609\/aaai.v34i04.5891","DOI":"10.1609\/aaai.v34i04.5891"},{"key":"13","unstructured":"[13] Y. Ganin, E. Ustinova, H. Ajakan, P. Germain, H. Larochelle, F. Laviolette, M. Marchand, and V. Lempitsky, \u201cDomain-adversarial training of neural networks,\u201d Journal of Machine Learning Research, vol.17, no.1, pp.2096-2030, 2016."},{"key":"14","doi-asserted-by":"crossref","unstructured":"[14] K. He, X. Zhang, S. Ren, and J. Sun, \u201cDeep residual learning for image recognition,\u201d Proc. IEEE Conf. Comput. Vis. Pattern Recognit., pp.770-778, 2016. 10.1109\/cvpr.2016.90","DOI":"10.1109\/CVPR.2016.90"}],"container-title":["IEICE Transactions on Information and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E105.D\/7\/E105.D_2021EDL8101\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,9]],"date-time":"2024-05-09T04:55:49Z","timestamp":1715230549000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E105.D\/7\/E105.D_2021EDL8101\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,1]]},"references-count":14,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2022]]}},"URL":"https:\/\/doi.org\/10.1587\/transinf.2021edl8101","relation":{},"ISSN":["0916-8532","1745-1361"],"issn-type":[{"value":"0916-8532","type":"print"},{"value":"1745-1361","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,1]]},"article-number":"2021EDL8101"}}