{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T21:39:22Z","timestamp":1774993162120,"version":"3.50.1"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2021,3,15]],"date-time":"2021-03-15T00:00:00Z","timestamp":1615766400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,3,15]],"date-time":"2021-03-15T00:00:00Z","timestamp":1615766400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2021,11]]},"DOI":"10.1007\/s10489-021-02271-z","type":"journal-article","created":{"date-parts":[[2021,3,15]],"date-time":"2021-03-15T12:02:46Z","timestamp":1615809766000},"page":"7679-7689","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Image generation and constrained two-stage feature fusion for person re-identification"],"prefix":"10.1007","volume":"51","author":[{"given":"Tao","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Xing","family":"Sun","sequence":"additional","affiliation":[]},{"given":"Xuan","family":"Li","sequence":"additional","affiliation":[]},{"given":"Zhengming","family":"Yi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,3,15]]},"reference":[{"key":"2271_CR1","doi-asserted-by":"crossref","unstructured":"Gheissari N, Sebastian TB, Hartley R (2006) Person reidentification using spatiotemporal appearance. In: IEEE conference on computer vision and pattern recognition (CVPR), pp 1528\u20131535","DOI":"10.1109\/CVPR.2006.223"},{"issue":"9","key":"2271_CR2","doi-asserted-by":"publisher","first-page":"3436","DOI":"10.1007\/s10489-019-01459-8","volume":"49","author":"J Liu","year":"2019","unstructured":"Liu J, Sun C, Xu X, et al. (2019) A spatial and temporal features mixture model with body parts for video-based person re-identification. Appl Intell 49(9):3436\u20133446","journal-title":"Appl Intell"},{"key":"2271_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/978-1-4471-6296-4","volume-title":"Person Re-identification","author":"S Gong","year":"2014","unstructured":"Gong S, Cristani M, Shuicheng Y, Loy CC, et al. (2014) Person Re-identification. Springer, London, pp 1\u201320"},{"key":"2271_CR4","unstructured":"Zheng L, Yang Y, Hauptmann AG (2016) Person re-identification: Past, present and future. arXiv:1610.02984"},{"key":"2271_CR5","doi-asserted-by":"crossref","unstructured":"Saquib Sarfraz M, Schumann A, Eberle A et al (2018) A pose-sensitive embedding for person re-identification with expanded cross neighborhood re-ranking. In: IEEE conference on computer vision and pattern recognition (CVPR), pp 420\u2013429","DOI":"10.1109\/CVPR.2018.00051"},{"key":"2271_CR6","doi-asserted-by":"crossref","unstructured":"Huang Y, Zha ZJ, Fu X et al (2019) Illumination-invariant person re-identification","DOI":"10.1145\/3343031.3350994"},{"key":"2271_CR7","doi-asserted-by":"crossref","unstructured":"Hou R, Ma B, Chang H et al (2019) Vrstc: Occlusion-free video person re-identification. In: IEEE conference on computer vision and pattern recognition (CVPR), pp 7183\u20137192","DOI":"10.1109\/CVPR.2019.00735"},{"key":"2271_CR8","doi-asserted-by":"crossref","unstructured":"Wang Y, Wang L, You Y et al (2018) Resource aware person re-identification across multiple resolutions","DOI":"10.1109\/CVPR.2018.00839"},{"key":"2271_CR9","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems (NIPS), pp 1097\u20131105"},{"key":"2271_CR10","unstructured":"Goodfellow I, Pouget-Abadie J, Mirza M et al (2014) Generative adversarial nets. In: Advances in neural information processing systems (NIPS), pp 2672\u20132680"},{"issue":"4","key":"2271_CR11","doi-asserted-by":"publisher","first-page":"1155","DOI":"10.1007\/s10489-019-01581-7","volume":"50","author":"W Guo","year":"2020","unstructured":"Guo W, Cai J, Wang S (2020) Unsupervised discriminative feature representation via adversarial auto-encoder. Appl Intell 50(4):1155\u20131171","journal-title":"Appl Intell"},{"key":"2271_CR12","doi-asserted-by":"crossref","unstructured":"Zheng Z, Zheng L, Yang Y (2017) Unlabeled samples generated by gan improve the person re-identification baseline in vitro. In: IEEE international conference on computer vision (ICCV), pp 3754\u20133762","DOI":"10.1109\/ICCV.2017.405"},{"key":"2271_CR13","doi-asserted-by":"crossref","unstructured":"Zhong Z, Zheng L, Zheng Z et al (2018) Camera style adaptation for person re-identification. In: IEEE international conference on computer vision (ICCV), pp 5157\u20135166","DOI":"10.1109\/CVPR.2018.00541"},{"key":"2271_CR14","doi-asserted-by":"crossref","unstructured":"Bak S, Carr P, Lalonde JF (2018) Domain adaptation through synthesis for unsupervised person re-identification. In: European conference on computer vision (ECCV), pp 189\u2013205","DOI":"10.1007\/978-3-030-01261-8_12"},{"key":"2271_CR15","doi-asserted-by":"crossref","unstructured":"Wei L, Zhang S, Gao W et al (2018) Person transfer gan to bridge domain gap for person re-identification. In: IEEE conference on computer vision and pattern recognition (CVPR), pp 79\u201388","DOI":"10.1109\/CVPR.2018.00016"},{"key":"2271_CR16","doi-asserted-by":"crossref","unstructured":"Liu J, Zhou Y, Sun L et al (2019) Similarity preserved camera-to-camera GAN for person re-identification. In: IEEE International conference on multimedia (&) expo workshops (ICMEW), pp 531\u2013536","DOI":"10.1109\/ICMEW.2019.00097"},{"key":"2271_CR17","doi-asserted-by":"crossref","unstructured":"Liu J, Ni B, Yan Y et al (2018) Pose transferrable person re-identification. In: IEEE conference on computer vision and pattern recognition (CVPR), pp 4099\u20134108","DOI":"10.1109\/CVPR.2018.00431"},{"key":"2271_CR18","doi-asserted-by":"crossref","unstructured":"Qian X, Fu Y, Xiang T et al (2018) Pose-normalized image generation for person re-identification. In: European conference on computer vision (ECCV), pp 650\u2013667","DOI":"10.1007\/978-3-030-01240-3_40"},{"key":"2271_CR19","doi-asserted-by":"crossref","unstructured":"Siarohin A, Sangineto E, Lathuiliere S et al (2018) Deformable gans for pose-based human image generation. In: IEEE conference on computer vision and pattern recognition (CVPR), pp 3408\u20133416","DOI":"10.1109\/CVPR.2018.00359"},{"key":"2271_CR20","doi-asserted-by":"crossref","unstructured":"Ho HI, Shim M, Wee D (2020) Learning from dances: pose-invariant re-identification for multi-person tracking. In: IEEE International conference on acoustics, speech and signal processing (ICASSP), pp 2113\u20132117","DOI":"10.1109\/ICASSP40776.2020.9054086"},{"key":"2271_CR21","unstructured":"Ge Y, Li Z, Zhao H et al (2018) Fd-gan: Pose-guided feature distilling gan for robust person re-identification. In: Advances in neural information processing systems (NIPS), pp 1222\u2013 1233"},{"key":"2271_CR22","doi-asserted-by":"publisher","first-page":"391","DOI":"10.1109\/LSP.2020.2972768","volume":"27","author":"L Huang","year":"2020","unstructured":"Huang L, Yang Q, Wu J, et al. (2020) Generated data with sparse regularized multi-pseudo label for person re-identification. IEEE Signal Process Lett 27:391\u2013395","journal-title":"IEEE Signal Process Lett"},{"key":"2271_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10489-020-01648-w","volume":"50","author":"F Qian","year":"2020","unstructured":"Qian F, Li J, Du X, et al. (2020) Generative image inpainting for link prediction. Appl Intell 50:1\u201313","journal-title":"Appl Intell"},{"key":"2271_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10489-020-01751-y","volume":"50","author":"X Xiong","year":"2020","unstructured":"Xiong X, Min W, Zheng W S, et al. (2020) S3D-CNN: skeleton-based 3D consecutive-low-pooling neural network for fall detection. Appl Intell 50:1\u201314","journal-title":"Appl Intell"},{"key":"2271_CR25","doi-asserted-by":"crossref","unstructured":"Zheng L, Shen L, Tian L et al (2015) Scalable person re-identification: A benchmark. In: IEEE international conference on computer vision (ICCV), pp 1116\u20131124","DOI":"10.1109\/ICCV.2015.133"},{"key":"2271_CR26","doi-asserted-by":"crossref","unstructured":"Ristani E, Solera F, Zou R et al (2016) Performance measures and a data set for multi-target, multi-camera tracking. In: European conference on computer vision (ECCV), pp 17\u201335","DOI":"10.1007\/978-3-319-48881-3_2"},{"issue":"5786","key":"2271_CR27","doi-asserted-by":"publisher","first-page":"504","DOI":"10.1126\/science.1127647","volume":"313","author":"GE Hinton","year":"2006","unstructured":"Hinton GE, Salakhutdinov RR (2006) Reducing the dimensionality of data with neural networks. Science 313(5786):504\u2013507","journal-title":"Science"},{"key":"2271_CR28","doi-asserted-by":"crossref","unstructured":"Cao Z, Simon T, Wei SE et al (2017) Realtime multi-person 2d pose estimation using part affinity fields. In: IEEE conference on computer vision and pattern recognition (CVPR), pp 7291\u2013 7299","DOI":"10.1109\/CVPR.2017.143"},{"key":"2271_CR29","unstructured":"Dong H, Liang X, Gong K et al (2018) Soft-gated warping-gan for pose-guided person image synthesis. In: Advances in neural information processing systems (NIPS), pp 474\u2013 484"},{"key":"2271_CR30","doi-asserted-by":"crossref","unstructured":"Yu K, Lang C, Feng S et al (2018) Reasonably assign label distributions to GAN images in Person Re-Identification baseline. In: IEEE Fourth international conference on multimedia big data (BigMM), pp 1\u20135","DOI":"10.1109\/BigMM.2018.8499058"},{"issue":"3","key":"2271_CR31","doi-asserted-by":"publisher","first-page":"1391","DOI":"10.1109\/TIP.2018.2874715","volume":"28","author":"Y Huang","year":"2018","unstructured":"Huang Y, Xu J, Wu Q, et al. (2018) Multi-pseudo regularized label for generated data in person re-identification. IEEE Trans Image Process 28(3):1391\u20131403","journal-title":"IEEE Trans Image Process"},{"key":"2271_CR32","unstructured":"Salimans T, Goodfellow I, Zaremba W et al (2016) Improved techniques for training gans. In: Advances in neural information processing systems (NIPS), pp 2234\u20132242"},{"key":"2271_CR33","doi-asserted-by":"crossref","unstructured":"Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: IEEE conference on computer vision and pattern recognition (CVPR), pp 7132\u20137141","DOI":"10.1109\/CVPR.2018.00745"},{"key":"2271_CR34","doi-asserted-by":"crossref","unstructured":"Wen Y, Zhang K, Li Z, et al. (2016) A discriminative feature learning approach for deep face recognition. In: European conference on computer vision (ECCV)","DOI":"10.1007\/978-3-319-46478-7_31"},{"key":"2271_CR35","doi-asserted-by":"crossref","unstructured":"Cheng D, Gong Y, Zhou S, et al. (2016) Person re-identification by multi-channel parts-based CNN with improved triplet loss function. In: IEEE conference on computer vision and pattern recognition (CVPR)","DOI":"10.1109\/CVPR.2016.149"},{"key":"2271_CR36","doi-asserted-by":"crossref","unstructured":"Chen W, Chen X, Zhang J, et al. (2017) Beyond triplet loss: a deep quadruplet network for person re-identification. In: IEEE conference on computer vision and pattern recognition (CVPR)","DOI":"10.1109\/CVPR.2017.145"},{"key":"2271_CR37","doi-asserted-by":"crossref","unstructured":"Deng J, Dong W, Socher R et al (2009) Imagenet: A large-scale hierarchical image database. In: IEEE conference on computer vision and pattern recognition (CVPR), pp 248\u2013255","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"2271_CR38","unstructured":"Paszke A, Gross S, Chintala S et al (2017) Automatic differentiation in pytorch. In NIPS-W"},{"key":"2271_CR39","unstructured":"Heusel M, Ramsauer H, Unterthiner T et al (2017) GANs trained by a two time-scale update rule converge to a local Nash equilibrium. In: Advances in neural information processing systems (NIPS), pp 6626\u20136637"},{"issue":"4","key":"2271_CR40","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1109\/TIP.2003.819861","volume":"13","author":"Z Wang","year":"2004","unstructured":"Wang Z, Bovik A C, Sheikh H R, et al. (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600\u2013612","journal-title":"IEEE Trans Image Process"},{"key":"2271_CR41","unstructured":"Salimans T, Goodfellow I, Zaremba W, et al. (2016) Improved techniques for training gans. In: Advances in neural information processing systems (NIPS)"},{"key":"2271_CR42","doi-asserted-by":"crossref","unstructured":"Isola P, Zhu JY, Zhou T et al (2017) Image-to-image translation with conditional adversarial networks. In: IEEE conference on computer vision and pattern recognition (CVPR), pp 1125\u20131134","DOI":"10.1109\/CVPR.2017.632"},{"key":"2271_CR43","doi-asserted-by":"crossref","unstructured":"Ma L, Sun Q, Georgoulis S et al (2018) Disentangled person image generation. In: IEEE conference on computer vision and pattern recognition (CVPR), pp 99\u2013108","DOI":"10.1109\/CVPR.2018.00018"},{"key":"2271_CR44","unstructured":"Xudong M, Qing L, Haoran X, Raymond L, Zhen W, Stephen S et al (2017) Least squares generative adversarial networks. In: IEEE international conference on computer vision (ICCV), pp 2794\u20132802"},{"key":"2271_CR45","unstructured":"Ma L, Jia X, Sun Q et al (2017) Pose guided person image generation. In: Advances in neural information processing systems (NIPS), pp 406\u2013416"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-021-02271-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-021-02271-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-021-02271-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,10,7]],"date-time":"2021-10-07T05:26:23Z","timestamp":1633584383000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-021-02271-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,15]]},"references-count":45,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2021,11]]}},"alternative-id":["2271"],"URL":"https:\/\/doi.org\/10.1007\/s10489-021-02271-z","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,15]]},"assertion":[{"value":"10 February 2021","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 March 2021","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}