{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T18:19:10Z","timestamp":1776190750527,"version":"3.50.1"},"reference-count":54,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/100007219","name":"Natural Science Foundation of Shanghai Municipality","doi-asserted-by":"publisher","award":["22ZR1426200"],"award-info":[{"award-number":["22ZR1426200"]}],"id":[{"id":"10.13039\/100007219","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62103257"],"award-info":[{"award-number":["62103257"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Expert Systems with Applications"],"published-print":{"date-parts":[[2026,5]]},"DOI":"10.1016\/j.eswa.2026.131251","type":"journal-article","created":{"date-parts":[[2026,1,18]],"date-time":"2026-01-18T22:49:35Z","timestamp":1768776575000},"page":"131251","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Unveiling pedestrian identities in the Dark: A collaborative Multi-View enhancement Transformer"],"prefix":"10.1016","volume":"309","author":[{"given":"Meifeng","family":"Liu","sequence":"first","affiliation":[]},{"given":"Hua","family":"Han","sequence":"additional","affiliation":[]},{"given":"A.A.M.","family":"Muzahid","sequence":"additional","affiliation":[]},{"given":"Li","family":"Huang","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"issue":"6","key":"10.1016\/j.eswa.2026.131251_b0005","doi-asserted-by":"crossref","first-page":"2872","DOI":"10.1109\/TPAMI.2021.3054775","article-title":"Deep learning for person re-identification: A survey and outlook","volume":"44","author":"Ye","year":"2022","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"10.1016\/j.eswa.2026.131251_b0010","first-page":"770","article-title":"Deep Residual Learning for image Recognition","author":"He","year":"2016","journal-title":"CVPR"},{"key":"10.1016\/j.eswa.2026.131251_b0015","unstructured":"H. Luo et al., \u201cSelf-supervised pre-training for transformer-based person re-identification,\u201d 2021, arXiv:2111.12084."},{"key":"10.1016\/j.eswa.2026.131251_b0020","doi-asserted-by":"crossref","unstructured":"L. Zheng et al., \u201cScalable person re-identification: A benchmark,\u201d in Proc. ICCV, 2015, pp. 1116-1124.","DOI":"10.1109\/ICCV.2015.133"},{"key":"10.1016\/j.eswa.2026.131251_b0025","doi-asserted-by":"crossref","first-page":"406","DOI":"10.1109\/TMM.2023.3266066","article-title":"Illumination distillation framework for nighttime person re-identification and a new benchmark","volume":"26","author":"Lu","year":"2024","journal-title":"IEEE Transactions on Multimedia"},{"key":"10.1016\/j.eswa.2026.131251_b0030","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2025.128533","article-title":"Visible-thermal cross class-incremental","volume":"294","author":"Yao","year":"2025","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2026.131251_b0035","doi-asserted-by":"crossref","unstructured":"A. Wu, W.-S. Zheng, H.-X. Yu, S. Gong, and J. Lai, \u201cRGB-infrared cross-modality person re-identification,\u201d in Proc. ICCV, Oct. 2017, pp. 5380\u20135389.","DOI":"10.1109\/ICCV.2017.575"},{"issue":"7","key":"10.1016\/j.eswa.2026.131251_b0040","doi-asserted-by":"crossref","first-page":"12144","DOI":"10.1609\/aaai.v34i07.6894","article-title":"Cross-modality paired-images generation for RGB infrared person re-identification","volume":"34","author":"Wang","year":"2020","journal-title":"Proc. AAAI"},{"issue":"10","key":"10.1016\/j.eswa.2026.131251_b0045","doi-asserted-by":"crossref","first-page":"10519","DOI":"10.1609\/aaai.v39i10.33142","article-title":"NightReID: A Large-Scale Nighttime Person Re-Identification Benchmark","volume":"39","author":"Zhao","year":"2025","journal-title":"Proc AAAI"},{"key":"10.1016\/j.eswa.2026.131251_b0050","unstructured":"A. Dosovitskiy et al., \u201cAn image is worth 16x16 words: Transformers for image recognition at scale,\u201d in Proc. ICLR. 2020."},{"key":"10.1016\/j.eswa.2026.131251_b0055","doi-asserted-by":"crossref","unstructured":"G. Wang, J.-H. Lai, W. Liang, and G. Wang, \u201cSmoothing adversarial domain attack and p-memory reconsolidation for cross-domain person reidentification,\u201d in Proc. CVPR, 2020, pp. 10568\u201310577.","DOI":"10.1109\/CVPR42600.2020.01058"},{"issue":"8","key":"10.1016\/j.eswa.2026.131251_b0060","first-page":"4225","article-title":"Learning to enhance low-light image via zero-reference deep curve estimation","volume":"44","author":"Li","year":"2021","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"10.1016\/j.eswa.2026.131251_b0065","doi-asserted-by":"crossref","unstructured":"M. Farenzena, L. Bazzani, A. Perina, V. Murino, and M. Cristani, \u201cPerson re-identification by symmetry-driven accumulation of local features,\u201d in Proc. CVPR, Jun. 2010, pp. 2360\u20132367.","DOI":"10.1109\/CVPR.2010.5539926"},{"key":"10.1016\/j.eswa.2026.131251_b0070","first-page":"262","article-title":"Viewpoint invariant pedestrian recognition with an ensemble of localized features","author":"Gray","year":"2008","journal-title":"Proc. ECCV"},{"key":"10.1016\/j.eswa.2026.131251_b0075","doi-asserted-by":"crossref","unstructured":"R. Zhao, W. Ouyang, and X. Wang, \u201cPerson re-identification by salience matching,\u201d in Proc. ICCV, Dec. 2013, pp. 2528\u20132535.","DOI":"10.1109\/ICCV.2013.314"},{"key":"10.1016\/j.eswa.2026.131251_b0080","first-page":"1","article-title":"SPSNet: Semantic-guided perspective shift network for robust person re-identification in drone imagery","volume":"61","author":"Wei","year":"2023","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"key":"10.1016\/j.eswa.2026.131251_b0085","unstructured":"A. Hermans, L. Beyer, and B. Leibe, \u201cIn defense of the triplet loss for person re-identification,\u201d arXiv:1703.07737, Nov. 2017."},{"key":"10.1016\/j.eswa.2026.131251_b0090","doi-asserted-by":"crossref","unstructured":"W. Li, X. Zhu, and S. Gong, \u201cHarmonious attention network for person re-identification,\u201d in Proc. CVPR, Jun. 2018, pp. 2285\u20132294.","DOI":"10.1109\/CVPR.2018.00243"},{"key":"10.1016\/j.eswa.2026.131251_b0095","doi-asserted-by":"crossref","unstructured":"W. Zhang, Q. Ding, J. Hu, Y. Ma, and M. Lu, \u201cPixel-wise graph attention networks for person re-identification,\u201d in Proc. 29th ACM Int. Conf. Multimedia, 2021, pp. 5231\u20135238.","DOI":"10.1145\/3474085.3475640"},{"key":"10.1016\/j.eswa.2026.131251_b0100","doi-asserted-by":"crossref","unstructured":"H. Zhao et al., \u201cSpindle Net: Person re-identification with human body region guided feature decomposition and fusion,\u201d in Proc. CVPR, Jul. 2017, pp. 1077\u20131085.","DOI":"10.1109\/CVPR.2017.103"},{"key":"10.1016\/j.eswa.2026.131251_b0105","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1016\/j.neucom.2020.12.016","article-title":"Learning multi-granularity features from multi-granularity regions for person re-identification","volume":"432","author":"Yang","year":"2021","journal-title":"Neurocomputing"},{"key":"10.1016\/j.eswa.2026.131251_b0110","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2025.126968","article-title":"Mixed granularity network for person re-identification","volume":"275","author":"Zhang","year":"2025","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2026.131251_b0115","unstructured":"A. Vaswani et al., \u201cAttention is all you need,\u201d in Proc. Int. Conf. Neural Inf. Process. Syst., 2017, pp. 5998\u20136008."},{"key":"10.1016\/j.eswa.2026.131251_b0120","doi-asserted-by":"crossref","unstructured":"S. He, H. Luo, P. Wang, F. Wang, H. Li, and W. Jiang, \u201cTransReID: Transformer-based object re-identification,\u201d in Proc. CVPR, Oct. 2021, pp. 14993\u201315002.","DOI":"10.1109\/ICCV48922.2021.01474"},{"key":"10.1016\/j.eswa.2026.131251_b0125","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2022.119307","article-title":"Random area pixel variation and random area transform for visible-infrared cross-modal pedestrian re-identification","volume":"215","author":"Zeng","year":"2023","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2026.131251_b0130","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2025.128123","article-title":"Learning discriminative features via deep metric learning for video-based person re-identification","volume":"286","author":"Wang","year":"2025","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2026.131251_b0135","doi-asserted-by":"crossref","first-page":"95496","DOI":"10.1109\/ACCESS.2019.2929854","article-title":"Night person re-identification and a benchmark","volume":"7","author":"Zhang","year":"2019","journal-title":"IEEE Access"},{"key":"10.1016\/j.eswa.2026.131251_b0140","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2025.126645","article-title":"CFET: A Cross-Fusion Enhanced Transformer for Visible-infrared person re-identification","volume":"271","author":"Guo","year":"2025","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2026.131251_b0145","doi-asserted-by":"crossref","first-page":"1305","DOI":"10.1109\/TIFS.2025.3527335","article-title":"Nighttime Person Re-Identification via Collaborative Enhancement Network with Multi-domain Learning","volume":"20","author":"Lu","year":"2025","journal-title":"IEEE Transactions on Information Forensics and Security"},{"key":"10.1016\/j.eswa.2026.131251_b0150","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2025.128150","article-title":"FMFI: Transformer based four branches multi-granularity feature integration for person Re-ID","volume":"288","author":"Zheng","year":"2025","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2026.131251_b0155","doi-asserted-by":"crossref","unstructured":"O.J. Santana, J. Lorenzo-Navarro, D. Freire-Obreg\u00f3n, \u201cApplying Deep Learning Image Enhancement Methods to Improve Person Re-Identification.\u201d Neurocomputing, vol. 598, pp. 128001-128011, Spet. 2024.","DOI":"10.1016\/j.neucom.2024.128011"},{"issue":"2","key":"10.1016\/j.eswa.2026.131251_b0160","doi-asserted-by":"crossref","first-page":"982","DOI":"10.1109\/TIP.2016.2639450","article-title":"LIME: Low-Light image Enhancement via Illumination Map Estimation","volume":"26","author":"Guo","year":"2017","journal-title":"IEEE Transactions on Image Processing"},{"key":"10.1016\/j.eswa.2026.131251_b0165","doi-asserted-by":"crossref","unstructured":"N. Dalal and B. Triggs, \u201cHistograms of oriented gradients for human detection,\u201d in Proc. CVPR, 2005, pp. 886\u2013893.","DOI":"10.1109\/CVPR.2005.177"},{"issue":"6","key":"10.1016\/j.eswa.2026.131251_b0170","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1038\/scientificamerican1277-108","article-title":"The Retinex Theory of Color Vision","volume":"237","author":"Land","year":"1977","journal-title":"Sci. Amer."},{"issue":"1","key":"10.1016\/j.eswa.2026.131251_b0175","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13640-016-0138-1","article-title":"An adaptive gamma correction for image enhancement","volume":"2016","author":"Rahman","year":"2016","journal-title":"EURASIP J. Image Video Process."},{"key":"10.1016\/j.eswa.2026.131251_b0180","doi-asserted-by":"crossref","unstructured":"W. Wu, J. Weng, P. Zhang, X. Wang, W. Yang, and J. Jiang, \u201cURetinexNet: Retinex-based deep unfolding network for low-light image enhancement,\u201d in Proc. CVPR, June, 2022, pp. 5891\u20135900.","DOI":"10.1109\/CVPR52688.2022.00581"},{"key":"10.1016\/j.eswa.2026.131251_b0185","doi-asserted-by":"crossref","first-page":"2340","DOI":"10.1109\/TIP.2021.3051462","article-title":"EnlightenGAN: Deep light enhancement without paired supervision","volume":"30","author":"Jiang","year":"2021","journal-title":"IEEE Transactions on Image Processing"},{"issue":"3","key":"10.1016\/j.eswa.2026.131251_b0190","doi-asserted-by":"crossref","first-page":"2654","DOI":"10.1609\/aaai.v37i3.25364","article-title":"Ultra-high-definition low-light image enhancement: A benchmark and transformer-based method","volume":"37","author":"Wang","year":"2023","journal-title":"Proc. AAAI"},{"key":"10.1016\/j.eswa.2026.131251_b0195","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2024.111033","article-title":"An illumination-guided dual attention vision transformer for low-light image enhancement","volume":"158","author":"Wen","year":"2025","journal-title":"Pattern Recognition"},{"key":"10.1016\/j.eswa.2026.131251_b0200","unstructured":"P.K.A. Vasu, J. Gabriel, J. Zhu, O. Tuzel, and A. Ranjan, \u201cFastViT: A Fast Hybrid Vision Transformer Using Structural Reparameterization.\u201d in Proc. ICCV, pp. 5785-5795, Oct. 2023."},{"key":"10.1016\/j.eswa.2026.131251_b0205","volume":"arXiv:2408.03703v2","author":"Zhang","year":"2024","journal-title":"\u201cCAS-ViT: Convolutional Additive Self-attention Vision Transformers for Efficient Mobile Applications\u201d."},{"key":"10.1016\/j.eswa.2026.131251_b0210","doi-asserted-by":"crossref","unstructured":"L. Meng et al., \u201cAdaViT: Adaptive vision transformers for efficient image recognition,\u201d in Proc. CVPR, Jun. 2022, pp. 12299\u201312308.","DOI":"10.1109\/CVPR52688.2022.01199"},{"key":"10.1016\/j.eswa.2026.131251_b0215","unstructured":"Y. Rao, W. Zhao, B. Liu, J. Lu, J. Zhou, and C.-J. Hsieh, \u201cDynamicViT: Efficient vision transformers with dynamic token sparsification,\u201d in Proc. Adv. Neural Inf. Process. Syst., vol. 34, 2021, pp. 13937\u201313949."},{"key":"10.1016\/j.eswa.2026.131251_b0220","doi-asserted-by":"crossref","unstructured":"X. Liu, T. Wu, and G. Guo, \u201cAdaptive sparse ViT: Towards learnable adaptive token pruning by fully exploiting self-attention,\u201d in Proc. 32nd Int. Joint Conf. Artif. Intell., Aug. 2023, pp. 1222\u20131230.","DOI":"10.24963\/ijcai.2023\/136"},{"key":"10.1016\/j.eswa.2026.131251_b0225","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.procs.2015.07.362","article-title":"Comparative study of skin color detection and segmentation in HSV and YCbCr color space","volume":"57","author":"Shaik","year":"2015","journal-title":"Proc. Comput. Sci."},{"issue":"1","key":"10.1016\/j.eswa.2026.131251_b0230","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/0016-0032(80)90058-7","article-title":"A spatial processor model for object colour perception","volume":"310","author":"Buchsbaum","year":"1980","journal-title":"Journal of the Franklin Institute"},{"key":"10.1016\/j.eswa.2026.131251_b0235","doi-asserted-by":"crossref","unstructured":"H. Luo, Y. Gu, X. Liao, S. Lai, and W. Jiang, \u201cBag of tricks and a strong baseline for deep person re-identification,\u201d in Proc. CVPR, June., 2019, pp. 1487\u20131495.","DOI":"10.1109\/CVPRW.2019.00190"},{"issue":"2","key":"10.1016\/j.eswa.2026.131251_b0240","doi-asserted-by":"crossref","first-page":"1415","DOI":"10.1609\/aaai.v37i2.25226","article-title":"DC-Former: Diverse and Compact Transformer for Person Re-identification","volume":"37","author":"Li","year":"2023","journal-title":"Proc AAAI"},{"issue":"1","key":"10.1016\/j.eswa.2026.131251_b0245","doi-asserted-by":"crossref","first-page":"1405","DOI":"10.1609\/aaai.v37i1.25225","article-title":"CLIP-ReID: Exploiting Vision-Language Model for image Re-identification without Concrete Text Labels","volume":"37","author":"Li","year":"2023","journal-title":"AAAI"},{"key":"10.1016\/j.eswa.2026.131251_b0250","doi-asserted-by":"crossref","unstructured":"D. Fu et al., \u201cUnsupervised pre-training for person re-identification,\u201d in Proc. CVPR, 2021, pp. 14745-14754.","DOI":"10.1109\/CVPR46437.2021.01451"},{"key":"10.1016\/j.eswa.2026.131251_b0255","unstructured":"L. Van der Maaten and G. Hinton, \u201cVisualizing data using t-sne.\u201d Journal of machine learning research, vol. 9, no. 11, 2008."},{"key":"10.1016\/j.eswa.2026.131251_b0260","doi-asserted-by":"crossref","unstructured":"S. Garc\u00eda, A. Fernandez, \u0301 J. Luengo, F. Herrera, \u201cAdvanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power\u201d, Inf. Sci. vol. 180, pp. 2044-2064, May. 2010.","DOI":"10.1016\/j.ins.2009.12.010"},{"key":"10.1016\/j.eswa.2026.131251_b0265","series-title":"Handbook of parametric and nonparametric statistical procedures","author":"Sheskin","year":"2020"},{"key":"10.1016\/j.eswa.2026.131251_b0270","doi-asserted-by":"crossref","unstructured":"A. Zheng, Z. Wang, Z. Chen, C. Li, and J. Tang, \u201cRobust multi-modality person re-identification\u201d, in Proc. AAAI, vol. no. 4, 2021, pp. 3529\u20133537.","DOI":"10.1609\/aaai.v35i4.16467"}],"container-title":["Expert Systems with Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417426001648?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417426001648?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T17:26:30Z","timestamp":1776187590000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0957417426001648"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5]]},"references-count":54,"alternative-id":["S0957417426001648"],"URL":"https:\/\/doi.org\/10.1016\/j.eswa.2026.131251","relation":{},"ISSN":["0957-4174"],"issn-type":[{"value":"0957-4174","type":"print"}],"subject":[],"published":{"date-parts":[[2026,5]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Unveiling pedestrian identities in the Dark: A collaborative Multi-View enhancement Transformer","name":"articletitle","label":"Article Title"},{"value":"Expert Systems with Applications","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.eswa.2026.131251","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"131251"}}