{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T18:49:49Z","timestamp":1780598989457,"version":"3.54.1"},"reference-count":53,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2024,4,30]],"date-time":"2024-04-30T00:00:00Z","timestamp":1714435200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,4,30]],"date-time":"2024-04-30T00:00:00Z","timestamp":1714435200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"the Public Welfare Research Program of Huzhou Science and Technology Bureau","award":["2022GZ01"],"award-info":[{"award-number":["2022GZ01"]}]},{"name":"the Public Welfare Research Program of Huzhou Science and Technology Bureau","award":["2022GZ01"],"award-info":[{"award-number":["2022GZ01"]}]},{"name":"the Public Welfare Research Program of Huzhou Science and Technology Bureau","award":["2022GZ01"],"award-info":[{"award-number":["2022GZ01"]}]},{"name":"the Public Welfare Research Program of Huzhou Science and Technology Bureau","award":["2022GZ01"],"award-info":[{"award-number":["2022GZ01"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Vis Comput"],"published-print":{"date-parts":[[2024,6]]},"DOI":"10.1007\/s00371-024-03412-4","type":"journal-article","created":{"date-parts":[[2024,5,1]],"date-time":"2024-05-01T00:02:54Z","timestamp":1714521774000},"page":"4023-4038","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Revisiting segmentation-guided denoising student\u2013teacher in anomaly detection"],"prefix":"10.1007","volume":"40","author":[{"given":"Ying","family":"Zang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ankang","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bing","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenjun","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,4,30]]},"reference":[{"issue":"4","key":"3412_CR1","doi-asserted-by":"publisher","first-page":"947","DOI":"10.1007\/s11263-022-01578-9","volume":"130","author":"P Bergmann","year":"2022","unstructured":"Bergmann, P., Batzner, K., Fauser, M., Sattlegger, D., Steger, C.: Beyond dents and scratches: logical constraints in unsupervised anomaly detection and localization. Int. J. Comput. Vis. 130(4), 947\u2013969 (2022). https:\/\/doi.org\/10.1007\/s11263-022-01578-9","journal-title":"Int. J. Comput. Vis."},{"key":"3412_CR2","unstructured":"Cohen, N., Hoshen, Y.: Sub-image anomaly detection with deep pyramid correspondences. arXiv preprint arXiv:2005.02357 (2020)"},{"key":"3412_CR3","doi-asserted-by":"publisher","unstructured":"Defard, T., Setkov, A., Loesch, A., Audigier, R.: Padim: a patch distribution modeling framework for anomaly detection and localization. In: International Conference on Pattern Recognition, pp. 475\u2013489. Springer (2021). https:\/\/doi.org\/10.1007\/978-3-030-68799-1_35","DOI":"10.1007\/978-3-030-68799-1_35"},{"key":"3412_CR4","doi-asserted-by":"publisher","unstructured":"Zavrtanik, V., Kristan, M., Sko\u010daj, D.: Draem-a discriminatively trained reconstruction embedding for surface anomaly detection. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 8330\u20138339 (2021). https:\/\/doi.org\/10.1109\/iccv48922.2021.00822","DOI":"10.1109\/iccv48922.2021.00822"},{"key":"3412_CR5","doi-asserted-by":"publisher","unstructured":"Zou, Y., Jeong, J., Pemula, L., Zhang, D., Dabeer, O.: Spot-the-difference self-supervised pre-training for anomaly detection and segmentation. In: European Conference on Computer Vision, pp. 392\u2013408 (2022). Springer. https:\/\/doi.org\/10.1007\/978-3-031-20056-4_23","DOI":"10.1007\/978-3-031-20056-4_23"},{"key":"3412_CR6","doi-asserted-by":"publisher","unstructured":"Bergmann, P., Fauser, M., Sattlegger, D., Steger, C.: Mvtec ad\u2014a comprehensive real-world dataset for unsupervised anomaly detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9592\u20139600 (2019). https:\/\/doi.org\/10.1109\/cvpr.2019.00982","DOI":"10.1109\/cvpr.2019.00982"},{"key":"3412_CR7","unstructured":"Seeb\u00f6ck, P., Waldstein, S., Klimscha, S., Gerendas, B.S., Donner, R., Schlegl, T., Schmidt-Erfurth, U., Langs, G.: Identifying and categorizing anomalies in retinal imaging data. arXiv preprint arXiv:1612.00686 (2016)"},{"key":"3412_CR8","doi-asserted-by":"publisher","unstructured":"Schlegl, T., Seeb\u00f6ck, P., Waldstein, S.M., Schmidt-Erfurth, U., Langs, G.: Unsupervised anomaly detection with generative adversarial networks to guide marker discovery. In: International Conference on Information Processing in Medical Imaging, pp. 146\u2013157. Springer (2017). https:\/\/doi.org\/10.1007\/978-3-319-59050-9_12","DOI":"10.1007\/978-3-319-59050-9_12"},{"key":"3412_CR9","doi-asserted-by":"publisher","unstructured":"Li, Z., Wang, C., Han, M., Xue, Y., Wei, W., Li, L.-J., Fei-Fei, L.: Thoracic disease identification and localization with limited supervision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8290\u20138299 (2018). https:\/\/doi.org\/10.1109\/cvpr.2018.00865","DOI":"10.1109\/cvpr.2018.00865"},{"key":"3412_CR10","doi-asserted-by":"crossref","unstructured":"Chalapathy, R., Chawla, S.: Deep learning for anomaly detection: a survey. arXiv preprint arXiv:1901.03407 (2019)","DOI":"10.1145\/3394486.3406704"},{"key":"3412_CR11","doi-asserted-by":"publisher","unstructured":"Ruff, L., Vandermeulen, R., Goernitz, N., Deecke, L., Siddiqui, S.A., Binder, A., M\u00fcller, E., Kloft, M.: Deep one-class classification. In: International Conference on Machine Learning, pp. 4393\u20134402. PMLR (2018). https:\/\/doi.org\/10.1109\/dsw.2019.8755576","DOI":"10.1109\/dsw.2019.8755576"},{"issue":"11","key":"3412_CR12","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1145\/3422622","volume":"63","author":"I Goodfellow","year":"2020","unstructured":"Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial networks. Commun. ACM 63(11), 139\u2013144 (2020). https:\/\/doi.org\/10.1145\/3422622","journal-title":"Commun. ACM"},{"key":"3412_CR13","doi-asserted-by":"publisher","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst. 25 (2012). https:\/\/doi.org\/10.1145\/3065386","DOI":"10.1145\/3065386"},{"key":"3412_CR14","doi-asserted-by":"crossref","unstructured":"Andrews, J., Tanay, T., Morton, E.J., Griffin, L.D.: Transfer representation-learning for anomaly detection. JMLR (2016)","DOI":"10.1117\/12.2261101"},{"key":"3412_CR15","unstructured":"Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 (2015)"},{"key":"3412_CR16","doi-asserted-by":"publisher","unstructured":"Bergmann, P., Fauser, M., Sattlegger, D., Steger, C.: Uninformed students: Student-teacher anomaly detection with discriminative latent embeddings. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4183\u20134192 (2020). https:\/\/doi.org\/10.1109\/cvpr42600.2020.00424","DOI":"10.1109\/cvpr42600.2020.00424"},{"key":"3412_CR17","doi-asserted-by":"publisher","unstructured":"Salehi, M., Sadjadi, N., Baselizadeh, S., Rohban, M.H., Rabiee, H.R.: Multiresolution knowledge distillation for anomaly detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 14902\u201314912 (2021). https:\/\/doi.org\/10.1109\/cvpr46437.2021.01466","DOI":"10.1109\/cvpr46437.2021.01466"},{"key":"3412_CR18","unstructured":"Wang, G., Han, S., Ding, E., Huang, D.: Student-teacher feature pyramid matching for anomaly detection. arXiv preprint arXiv:2103.04257 (2021)"},{"key":"3412_CR19","unstructured":"Yamada, S., Hotta, K.: Reconstruction student with attention for student-teacher pyramid matching. arXiv preprint arXiv:2111.15376 (2021)"},{"key":"3412_CR20","doi-asserted-by":"publisher","unstructured":"Zhang, X., Li, S., Li, X., Huang, P., Shan, J., Chen, T.: Destseg: segmentation guided denoising student-teacher for anomaly detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3914\u20133923 (2023). https:\/\/doi.org\/10.1109\/cvpr52729.2023.00381","DOI":"10.1109\/cvpr52729.2023.00381"},{"key":"3412_CR21","doi-asserted-by":"publisher","unstructured":"Tung, F., Mori, G.: Similarity-preserving knowledge distillation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 1365\u20131374 (2019). https:\/\/doi.org\/10.1109\/iccv.2019.00145","DOI":"10.1109\/iccv.2019.00145"},{"key":"3412_CR22","doi-asserted-by":"publisher","unstructured":"Chen, P., Liu, S., Zhao, H., Jia, J.: Distilling knowledge via knowledge review. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5008\u20135017 (2021). https:\/\/doi.org\/10.1109\/cvpr46437.2021.00497","DOI":"10.1109\/cvpr46437.2021.00497"},{"key":"3412_CR23","doi-asserted-by":"publisher","unstructured":"Tokozume, Y., Ushiku, Y., Harada, T.: Between-class learning for image classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5486\u20135494 (2018). https:\/\/doi.org\/10.11517\/pjsai.JSAI2019.0_3E4OS12b02","DOI":"10.11517\/pjsai.JSAI2019.0_3E4OS12b02"},{"key":"3412_CR24","doi-asserted-by":"publisher","unstructured":"Yun, S., Han, D., Oh, S.J., Chun, S., Choe, J., Yoo, Y.: Cutmix: regularization strategy to train strong classifiers with localizable features. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 6023\u20136032 (2019). https:\/\/doi.org\/10.1109\/iccv.2019.00612","DOI":"10.1109\/iccv.2019.00612"},{"key":"3412_CR25","unstructured":"Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013)"},{"key":"3412_CR26","doi-asserted-by":"publisher","unstructured":"Akcay, S., Atapour-Abarghouei, A., Breckon, T.P.: Ganomaly: semi-supervised anomaly detection via adversarial training. In: Computer Vision\u2013ACCV 2018: 14th Asian Conference on Computer Vision, Perth, Australia, December 2\u20136, 2018, Revised Selected Papers, Part III 14, pp. 622\u2013637. Springer (2019). https:\/\/doi.org\/10.1007\/978-3-030-20893-6_39","DOI":"10.1007\/978-3-030-20893-6_39"},{"key":"3412_CR27","doi-asserted-by":"crossref","unstructured":"Bergmann, P., L\u00f6we, S., Fauser, M., Sattlegger, D., Steger, C.: Improving unsupervised defect segmentation by applying structural similarity to autoencoders. arXiv preprint arXiv:1807.02011 (2018)","DOI":"10.5220\/0007364503720380"},{"key":"3412_CR28","doi-asserted-by":"publisher","DOI":"10.1109\/jiot.2023.3268226","author":"W Zhai","year":"2023","unstructured":"Zhai, W., Gao, M., Guo, X., Li, Q.: Scale-context perceptive network for crowd counting and localization in smart city system. IEEE Internet Things J. (2023). https:\/\/doi.org\/10.1109\/jiot.2023.3268226","journal-title":"IEEE Internet Things J."},{"key":"3412_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2023.102037","volume":"102","author":"Towards multimodal disinformation detection by vision-language knowledge interaction","year":"2024","unstructured":"Towards multimodal disinformation detection by vision-language knowledge interaction: Inf. Fus. 102, 102037 (2024). https:\/\/doi.org\/10.1016\/j.inffus.2023.102037","journal-title":"Inf. Fus."},{"key":"3412_CR30","doi-asserted-by":"publisher","DOI":"10.1109\/tce.2023.3337207","author":"G Zhang","year":"2023","unstructured":"Zhang, G., Gao, M., Li, Q., Zhai, W., Zou, G., Jeon, G.: Disrupting deepfakes via union-saliency adversarial attack. IEEE Trans. Consum. Electron. (2023). https:\/\/doi.org\/10.1109\/tce.2023.3337207","journal-title":"IEEE Trans. Consum. Electron."},{"key":"3412_CR31","doi-asserted-by":"crossref","unstructured":"Liu, T., Li, B., Du, X., Jiang, B., Geng, L., Wang, F., Zhao, Z.: Fair: frequency-aware image restoration for industrial visual anomaly detection. arXiv preprint arXiv:2309.07068 (2023)","DOI":"10.2139\/ssrn.4742821"},{"key":"3412_CR32","doi-asserted-by":"crossref","unstructured":"Fu\u010dka, M., Zavrtanik, V., Sko\u010daj, D.: Transfusion\u2014a transparency-based diffusion model for anomaly detection. arXiv preprint arXiv:2311.09999 (2023)","DOI":"10.1007\/978-3-031-72761-0_6"},{"key":"3412_CR33","doi-asserted-by":"publisher","unstructured":"Liu, W., Chang, H., Ma, B., Shan, S., Chen, X.: Diversity-measurable anomaly detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12147\u201312156 (2023). https:\/\/doi.org\/10.1109\/cvpr52729.2023.01169","DOI":"10.1109\/cvpr52729.2023.01169"},{"key":"3412_CR34","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107706","volume":"112","author":"V Zavrtanik","year":"2021","unstructured":"Zavrtanik, V., Kristan, M., Sko\u010daj, D.: Reconstruction by inpainting for visual anomaly detection. Pattern Recogn. 112, 107706 (2021). https:\/\/doi.org\/10.1016\/j.patcog.2020.107706","journal-title":"Pattern Recogn."},{"key":"3412_CR35","doi-asserted-by":"publisher","unstructured":"Hyun, J., Kim, S., Jeon, G., Kim, S.H., Bae, K., Kang, B.J.: Reconpatch: Contrastive patch representation learning for industrial anomaly detection. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 2052\u20132061 (2024). https:\/\/doi.org\/10.48550\/arXiv.2305.16713","DOI":"10.48550\/arXiv.2305.16713"},{"key":"3412_CR36","doi-asserted-by":"publisher","unstructured":"Roth, K., Pemula, L., Zepeda, J., Sch\u00f6lkopf, B., Brox, T., Gehler, P.: Towards total recall in industrial anomaly detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 14318\u201314328 (2022). https:\/\/doi.org\/10.1109\/cvpr52688.2022.01392","DOI":"10.1109\/cvpr52688.2022.01392"},{"key":"3412_CR37","doi-asserted-by":"publisher","first-page":"78446","DOI":"10.1109\/ACCESS.2022.3193699","volume":"10","author":"S Lee","year":"2022","unstructured":"Lee, S., Lee, S., Song, B.C.: Cfa: Coupled-hypersphere-based feature adaptation for target-oriented anomaly localization. IEEE Access 10, 78446\u201378454 (2022). https:\/\/doi.org\/10.1109\/ACCESS.2022.3193699","journal-title":"IEEE Access"},{"key":"3412_CR38","doi-asserted-by":"crossref","unstructured":"Li, H., Hu, J., Li, B., Chen, H., Zheng, Y., Shen, C.: Target before shooting: Accurate anomaly detection and localization under one millisecond via cascade patch retrieval. arXiv preprint arXiv:2308.06748 (2023)","DOI":"10.1109\/TIP.2024.3448263"},{"key":"3412_CR39","doi-asserted-by":"crossref","unstructured":"Wang, S., Li, Q., Luo, H., Lv, C., Zhang, Z.: Produce once, utilize twice for anomaly detection. arXiv preprint arXiv:2312.12913 (2023)","DOI":"10.1109\/TCSVT.2024.3420775"},{"key":"3412_CR40","doi-asserted-by":"publisher","unstructured":"Liu, Z., Zhou, Y., Xu, Y., Wang, Z.: Simplenet: A simple network for image anomaly detection and localization. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 20402\u201320411 (2023). https:\/\/doi.org\/10.1109\/cvpr52729.2023.01954","DOI":"10.1109\/cvpr52729.2023.01954"},{"key":"3412_CR41","doi-asserted-by":"publisher","DOI":"10.1109\/tnnls.2023.3344118","author":"Y Zhou","year":"2024","unstructured":"Zhou, Y., Xu, X., Song, J., Shen, F., Shen, H.T.: Msflow: Multiscale flow-based framework for unsupervised anomaly detection. IEEE Trans. Neural Netw. Learn. Syst. (2024). https:\/\/doi.org\/10.1109\/tnnls.2023.3344118","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"3412_CR42","doi-asserted-by":"publisher","unstructured":"Zavrtanik, V., Kristan, M., Sko\u010daj, D.: Dsr\u2013a dual subspace re-projection network for surface anomaly detection. In: European Conference on Computer Vision, pp. 539\u2013554. Springer (2022). https:\/\/doi.org\/10.1007\/978-3-031-19821-2_31","DOI":"10.1007\/978-3-031-19821-2_31"},{"key":"3412_CR43","doi-asserted-by":"publisher","unstructured":"Deng, H., Li, X.: Anomaly detection via reverse distillation from one-class embedding. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9737\u20139746 (2022). https:\/\/doi.org\/10.1109\/cvpr52688.2022.00951","DOI":"10.1109\/cvpr52688.2022.00951"},{"key":"3412_CR44","doi-asserted-by":"publisher","unstructured":"Tien, T.D., Nguyen, A.T., Tran, N.H., Huy, T.D., Duong, S., Nguyen, C.D.T., Truong, S.Q.: Revisiting reverse distillation for anomaly detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 24511\u201324520 (2023). https:\/\/doi.org\/10.1109\/cvpr52729.2023.02348","DOI":"10.1109\/cvpr52729.2023.02348"},{"key":"3412_CR45","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TIM.2022.3204332","volume":"71","author":"Z Liu","year":"2022","unstructured":"Liu, Z., Lyu, W., Wang, C., Guo, Q., Zhou, D., Xu, W.: D-centernet: an anchor-free detector with knowledge distillation for industrial defect detection. IEEE Trans. Instrum. Meas. 71, 1\u201312 (2022). https:\/\/doi.org\/10.1109\/TIM.2022.3204332","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"3412_CR46","doi-asserted-by":"publisher","unstructured":"Lang, J., Tang, C., Gao, Y., Lv, J.: Knowledge distillation method for surface defect detection. In: International Conference on Neural Information Processing, pp. 644\u2013655. Springer (2021). https:\/\/doi.org\/10.1007\/978-3-030-92273-3_53","DOI":"10.1007\/978-3-030-92273-3_53"},{"key":"3412_CR47","doi-asserted-by":"publisher","unstructured":"Liu, H., Wang, S., Meng, C., Zhang, H., Xiao, X., Li, X.: Unsupervised fabric defect detection framework based on knowledge distillation. In: International Conference on Neural Information Processing, pp. 339\u2013351. Springer (2023). https:\/\/doi.org\/10.1007\/978-981-99-8181-6_26","DOI":"10.1007\/978-981-99-8181-6_26"},{"key":"3412_CR48","unstructured":"DeVries, T., Taylor, G.W.: Improved regularization of convolutional neural networks with cutout. arXiv preprint arXiv:1708.04552 (2017)"},{"key":"3412_CR49","doi-asserted-by":"publisher","unstructured":"Zhong, Z., Zheng, L., Kang, G., Li, S., Yang, Y.: Random erasing data augmentation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 13001\u201313008 (2020). https:\/\/doi.org\/10.1609\/aaai.v34i07.7000","DOI":"10.1609\/aaai.v34i07.7000"},{"key":"3412_CR50","doi-asserted-by":"publisher","unstructured":"Li, C.-L., Sohn, K., Yoon, J., Pfister, T.: Cutpaste: Self-supervised learning for anomaly detection and localization. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9664\u20139674 (2021). https:\/\/doi.org\/10.1109\/cvpr46437.2021.00954","DOI":"10.1109\/cvpr46437.2021.00954"},{"key":"3412_CR51","doi-asserted-by":"publisher","unstructured":"Chen, L., Bentley, P., Mori, K., Misawa, K., Fujiwara, M., Rueckert, D.: Self-supervised learning for medical image analysis using image context restoration. Med. Image Anal. 58, 101539 (2019). https:\/\/doi.org\/10.1016\/j.media.2019.101539","DOI":"10.1016\/j.media.2019.101539"},{"key":"3412_CR52","doi-asserted-by":"publisher","unstructured":"Jenni, S., Jin, H., Favaro, P.: Steering self-supervised feature learning beyond local pixel statistics. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 6408\u20136417 (2020). https:\/\/doi.org\/10.1109\/cvpr42600.2020.00644","DOI":"10.1109\/cvpr42600.2020.00644"},{"key":"3412_CR53","doi-asserted-by":"publisher","unstructured":"Lin, T.-Y., Goyal, P., Girshick, R., He, K., Doll\u00e1r, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980\u20132988 (2017). https:\/\/doi.org\/10.1109\/iccv.2017.324","DOI":"10.1109\/iccv.2017.324"}],"container-title":["The Visual Computer"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-024-03412-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00371-024-03412-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-024-03412-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,17]],"date-time":"2024-11-17T12:44:21Z","timestamp":1731847461000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00371-024-03412-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,30]]},"references-count":53,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2024,6]]}},"alternative-id":["3412"],"URL":"https:\/\/doi.org\/10.1007\/s00371-024-03412-4","relation":{},"ISSN":["0178-2789","1432-2315"],"issn-type":[{"value":"0178-2789","type":"print"},{"value":"1432-2315","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,30]]},"assertion":[{"value":"7 April 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 April 2024","order":2,"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 conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}