{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,30]],"date-time":"2026-05-30T02:06:39Z","timestamp":1780106799362,"version":"3.54.0"},"publisher-location":"Cham","reference-count":62,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031729195","type":"print"},{"value":"9783031729201","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"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":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-72920-1_19","type":"book-chapter","created":{"date-parts":[[2024,9,30]],"date-time":"2024-09-30T08:02:57Z","timestamp":1727683377000},"page":"333-351","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["DGR-MIL: Exploring Diverse Global Representation in\u00a0Multiple Instance Learning for\u00a0Whole Slide Image Classification"],"prefix":"10.1007","author":[{"given":"Wenhui","family":"Zhu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiwen","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peijie","family":"Qiu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aristeidis","family":"Sotiras","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abolfazl","family":"Razi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yalin","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,10,1]]},"reference":[{"key":"19_CR1","unstructured":"Andrews, S., Tsochantaridis, I., Hofmann, T.: Support vector machines for multiple-instance learning. In: Advances in Neural Information Processing Systems, vol. 15 (2002)"},{"issue":"8","key":"19_CR2","doi-asserted-by":"publisher","first-page":"1619","DOI":"10.1109\/TPAMI.2010.226","volume":"33","author":"B Babenko","year":"2010","unstructured":"Babenko, B., Yang, M.H., Belongie, S.: Robust object tracking with online multiple instance learning. IEEE Trans. Pattern Anal. Mach. Intell. 33(8), 1619\u20131632 (2010)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"19_CR3","doi-asserted-by":"crossref","unstructured":"Balntas, V., Riba, E., Ponsa, D., Mikolajczyk, K.: Learning local feature descriptors with triplets and shallow convolutional neural networks. In: Bmvc, vol.\u00a01, p.\u00a03 (2016)","DOI":"10.5244\/C.30.119"},{"issue":"22","key":"19_CR4","doi-asserted-by":"publisher","first-page":"2199","DOI":"10.1001\/jama.2017.14585","volume":"318","author":"BE Bejnordi","year":"2017","unstructured":"Bejnordi, B.E., et al.: Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. JAMA 318(22), 2199\u20132210 (2017)","journal-title":"JAMA"},{"key":"19_CR5","doi-asserted-by":"crossref","unstructured":"Bhattamishra, S., Patel, A., Goyal, N.: On the computational power of transformers and its implications in sequence modeling. arXiv preprint arXiv:2006.09286 (2020)","DOI":"10.18653\/v1\/2020.conll-1.37"},{"issue":"7467","key":"19_CR6","doi-asserted-by":"publisher","first-page":"338","DOI":"10.1038\/nature12625","volume":"501","author":"RA Burrell","year":"2013","unstructured":"Burrell, R.A., McGranahan, N., Bartek, J., Swanton, C.: The causes and consequences of genetic heterogeneity in cancer evolution. Nature 501(7467), 338\u2013345 (2013)","journal-title":"Nature"},{"issue":"8","key":"19_CR7","doi-asserted-by":"publisher","first-page":"1301","DOI":"10.1038\/s41591-019-0508-1","volume":"25","author":"G Campanella","year":"2019","unstructured":"Campanella, G., et al.: Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. Nat. Med. 25(8), 1301\u20131309 (2019)","journal-title":"Nat. Med."},{"key":"19_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1007\/978-3-030-58452-8_13","volume-title":"Computer Vision \u2013 ECCV 2020","author":"N Carion","year":"2020","unstructured":"Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12346, pp. 213\u2013229. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58452-8_13"},{"key":"19_CR9","unstructured":"Chen, L., Zhang, G., Zhou, E.: Fast greedy map inference for determinantal point process to improve recommendation diversity. In: Advances in Neural Information Processing Systems, vol. 31 (2018)"},{"issue":"9","key":"19_CR10","doi-asserted-by":"publisher","first-page":"1453","DOI":"10.1038\/s41591-019-0539-7","volume":"25","author":"PHC Chen","year":"2019","unstructured":"Chen, P.H.C., et al.: An augmented reality microscope with real-time artificial intelligence integration for cancer diagnosis. Nat. Med. 25(9), 1453\u20131457 (2019)","journal-title":"Nat. Med."},{"key":"19_CR11","unstructured":"Chen, X., Li, H., Amin, R., Razi, A.: Rd-dpp: rate-distortion theory meets determinantal point process to diversify learning data samples. arXiv preprint arXiv:2304.04137 (2023)"},{"key":"19_CR12","doi-asserted-by":"publisher","unstructured":"Chen, X., Li, H., Amin, R., Razi, A.: Learning on bandwidth constrained multi-source data with MIMO-inspired DPP map inference. IEEE Trans. Mach. Learn. Commun. Netw. 1\u20131 (2024). https:\/\/doi.org\/10.1109\/TMLCN.2024.3421907","DOI":"10.1109\/TMLCN.2024.3421907"},{"key":"19_CR13","unstructured":"Chen, X., et al.: TimeMIL: advancing multivariate time series classification via a time-aware multiple instance learning. In: Forty-First International Conference on Machine Learning (2024)"},{"key":"19_CR14","volume-title":"Elements of Information Theory","author":"TM Cover","year":"1999","unstructured":"Cover, T.M.: Elements of Information Theory. Wiley, Hoboken (1999)"},{"issue":"1","key":"19_CR15","first-page":"34","volume":"68","author":"M Derezinski","year":"2021","unstructured":"Derezinski, M., Mahoney, M.W.: Determinantal point processes in randomized numerical linear algebra. Not. Am. Math. Soc. 68(1), 34\u201345 (2021)","journal-title":"Not. Am. Math. Soc."},{"key":"19_CR16","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)"},{"issue":"1\u20132","key":"19_CR17","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1016\/S0004-3702(96)00034-3","volume":"89","author":"TG Dietterich","year":"1997","unstructured":"Dietterich, T.G., Lathrop, R.H., Lozano-P\u00e9rez, T.: Solving the multiple instance problem with axis-parallel rectangles. Artif. Intell. 89(1\u20132), 31\u201371 (1997)","journal-title":"Artif. Intell."},{"key":"19_CR18","unstructured":"Dosovitskiy, A., et\u00a0al.: An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)"},{"key":"19_CR19","unstructured":"Early, J., Cheung, G., Cutajar, K., Xie, H., Kandola, J., Twomey, N.: Inherently interpretable time series classification via multiple instance learning. In: The Twelfth International Conference on Learning Representations (2024)"},{"key":"19_CR20","doi-asserted-by":"crossref","unstructured":"Feng, J., Zhou, Z.H.: Deep MIML network. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a031 (2017)","DOI":"10.1609\/aaai.v31i1.10890"},{"issue":"5","key":"19_CR21","first-page":"5436","volume":"45","author":"MH Guo","year":"2022","unstructured":"Guo, M.H., Liu, Z.N., Mu, T.J., Hu, S.M.: Beyond self-attention: external attention using two linear layers for visual tasks. IEEE Trans. Pattern Anal. Mach. Intell. 45(5), 5436\u20135447 (2022)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"1","key":"19_CR22","doi-asserted-by":"publisher","first-page":"e1007516","DOI":"10.1371\/journal.pcbi.1007516","volume":"16","author":"J Hannig","year":"2020","unstructured":"Hannig, J., et al.: Bioinformatics analysis of whole slide images reveals significant neighborhood preferences of tumor cells in hodgkin lymphoma. PLoS Comput. Biol. 16(1), e1007516 (2020)","journal-title":"PLoS Comput. Biol."},{"key":"19_CR23","doi-asserted-by":"crossref","unstructured":"He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.: Momentum contrast for unsupervised visual representation learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9729\u20139738 (2020)","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"19_CR24","doi-asserted-by":"crossref","unstructured":"Hou, L., Samaras, D., Kurc, T.M., Gao, Y., Davis, J.E., Saltz, J.H.: Patch-based convolutional neural network for whole slide tissue image classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2424\u20132433 (2016)","DOI":"10.1109\/CVPR.2016.266"},{"key":"19_CR25","unstructured":"Ilse, M., Tomczak, J., Welling, M.: Attention-based deep multiple instance learning. In: International Conference on Machine Learning, pp. 2127\u20132136. PMLR (2018)"},{"key":"19_CR26","doi-asserted-by":"crossref","unstructured":"Kulesza, A., Taskar, B., et\u00a0al.: Determinantal point processes for machine learning. Found. Trends\u00ae Mach. Learn. 5(2\u20133), 123\u2013286 (2012)","DOI":"10.1561\/2200000044"},{"key":"19_CR27","doi-asserted-by":"crossref","unstructured":"Li, B., Li, Y., Eliceiri, K.W.: Dual-stream multiple instance learning network for whole slide image classification with self-supervised contrastive learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 14318\u201314328 (2021)","DOI":"10.1109\/CVPR46437.2021.01409"},{"key":"19_CR28","doi-asserted-by":"crossref","unstructured":"Lin, T., Yu, Z., Hu, H., Xu, Y., Chen, C.W.: Interventional bag multi-instance learning on whole-slide pathological images. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 19830\u201319839 (2023)","DOI":"10.1109\/CVPR52729.2023.01899"},{"key":"19_CR29","doi-asserted-by":"crossref","unstructured":"Liu, K., et al.: Multiple instance learning via iterative self-paced supervised contrastive learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3355\u20133365 (2023)","DOI":"10.1109\/CVPR52729.2023.00327"},{"key":"19_CR30","doi-asserted-by":"crossref","unstructured":"Liu, Z., et al.: Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 10012\u201310022 (2021)","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"19_CR31","doi-asserted-by":"crossref","unstructured":"Lu, M.Y., Chen, R.J., Wang, J., Dillon, D., Mahmood, F.: Semi-supervised histology classification using deep multiple instance learning and contrastive predictive coding. arXiv preprint arXiv:1910.10825 (2019)","DOI":"10.1117\/12.2549627"},{"issue":"6","key":"19_CR32","doi-asserted-by":"publisher","first-page":"555","DOI":"10.1038\/s41551-020-00682-w","volume":"5","author":"MY Lu","year":"2021","unstructured":"Lu, M.Y., Williamson, D.F., Chen, T.Y., Chen, R.J., Barbieri, M., Mahmood, F.: Data-efficient and weakly supervised computational pathology on whole-slide images. Nat. Biomed. Eng. 5(6), 555\u2013570 (2021)","journal-title":"Nat. Biomed. Eng."},{"key":"19_CR33","doi-asserted-by":"crossref","unstructured":"Marusyk, A., Polyak, K.: Tumor heterogeneity: causes and consequences. Biochimica et Biophysica Acta (BBA)-Rev. Cancer 1805(1), 105\u2013117 (2010)","DOI":"10.1016\/j.bbcan.2009.11.002"},{"issue":"9","key":"19_CR34","doi-asserted-by":"publisher","first-page":"1372","DOI":"10.1001\/jamaoncol.2020.2485","volume":"6","author":"K Nagpal","year":"2020","unstructured":"Nagpal, K., et al.: Development and validation of a deep learning algorithm for Gleason grading of prostate cancer from biopsy specimens. JAMA Oncol. 6(9), 1372\u20131380 (2020)","journal-title":"JAMA Oncol."},{"issue":"15","key":"19_CR35","first-page":"510","volume":"7","author":"KB Petersen","year":"2008","unstructured":"Petersen, K.B., Pedersen, M.S., et al.: The matrix cookbook. Tech. Univ. Denmark 7(15), 510 (2008)","journal-title":"Tech. Univ. Denmark"},{"key":"19_CR36","unstructured":"Qiu, P., Xiao, P., Zhu, W., Wang, Y., Sotiras, A.: SC-MIL: sparsely coded multiple instance learning for whole slide image classification. arXiv preprint arXiv:2311.00048 (2023)"},{"key":"19_CR37","doi-asserted-by":"crossref","unstructured":"Qu, L., et al.: Boosting whole slide image classification from the perspectives of distribution, correlation and magnification. In: Proceedings of the IEEE\/CVF International Conference Computer Vision (ICCV), pp. 21463\u201321473 (2023)","DOI":"10.1109\/ICCV51070.2023.01962"},{"key":"19_CR38","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1109\/RBME.2017.2651164","volume":"10","author":"G Quellec","year":"2017","unstructured":"Quellec, G., Cazuguel, G., Cochener, B., Lamard, M.: Multiple-instance learning for medical image and video analysis. IEEE Rev. Biomed. Eng. 10, 213\u2013234 (2017)","journal-title":"IEEE Rev. Biomed. Eng."},{"key":"19_CR39","unstructured":"Radford, A., et\u00a0al.: Learning transferable visual models from natural language supervision. In: International Conference on Machine Learning, pp. 8748\u20138763. PMLR (2021)"},{"issue":"8","key":"19_CR40","first-page":"9","volume":"1","author":"A Radford","year":"2019","unstructured":"Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. OpenAI Blog 1(8), 9 (2019)","journal-title":"OpenAI Blog"},{"key":"19_CR41","doi-asserted-by":"crossref","unstructured":"Ruoss, A., et al.: Randomized positional encodings boost length generalization of transformers. arXiv preprint arXiv:2305.16843 (2023)","DOI":"10.18653\/v1\/2023.acl-short.161"},{"issue":"1","key":"19_CR42","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1746-1596-1-40","volume":"1","author":"T Schrader","year":"2006","unstructured":"Schrader, T., et al.: The diagnostic path, a useful visualisation tool in virtual microscopy. Diagn. Pathol. 1(1), 1\u20137 (2006)","journal-title":"Diagn. Pathol."},{"key":"19_CR43","first-page":"2136","volume":"34","author":"Z Shao","year":"2021","unstructured":"Shao, Z., Bian, H., Chen, Y., Wang, Y., Zhang, J., Ji, X., et al.: Transmil: transformer based correlated multiple instance learning for whole slide image classification. Adv. Neural. Inf. Process. Syst. 34, 2136\u20132147 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"19_CR44","unstructured":"Shen, Z., Zhang, M., Zhao, H., Yi, S., Li, H.: Efficient attention: attention with linear complexities. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 3531\u20133539 (2021)"},{"key":"19_CR45","doi-asserted-by":"crossref","unstructured":"Sun, R., Li, Y., Zhang, T., Mao, Z., Wu, F., Zhang, Y.: Lesion-aware transformers for diabetic retinopathy grading. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10938\u201310947 (2021)","DOI":"10.1109\/CVPR46437.2021.01079"},{"issue":"168","key":"19_CR46","first-page":"1","volume":"20","author":"N Tremblay","year":"2019","unstructured":"Tremblay, N., Barthelm\u00e9, S., Amblard, P.O.: Determinantal point processes for coresets. J. Mach. Learn. Res. 20(168), 1\u201370 (2019)","journal-title":"J. Mach. Learn. Res."},{"key":"19_CR47","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"19_CR48","unstructured":"Wang, S., Li, B.Z., Khabsa, M., Fang, H., Ma, H.: Linformer: self-attention with linear complexity. arXiv preprint arXiv:2006.04768 (2020)"},{"key":"19_CR49","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1016\/j.patcog.2017.08.026","volume":"74","author":"X Wang","year":"2018","unstructured":"Wang, X., Yan, Y., Tang, P., Bai, X., Liu, W.: Revisiting multiple instance neural networks. Pattern Recogn. 74, 15\u201324 (2018)","journal-title":"Pattern Recogn."},{"key":"19_CR50","first-page":"18009","volume":"35","author":"X Wang","year":"2022","unstructured":"Wang, X., et al.: Scl-wc: cross-slide contrastive learning for weakly-supervised whole-slide image classification. Adv. Neural. Inf. Process. Syst. 35, 18009\u201318021 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"19_CR51","doi-asserted-by":"publisher","first-page":"102559","DOI":"10.1016\/j.media.2022.102559","volume":"81","author":"X Wang","year":"2022","unstructured":"Wang, X., et al.: Transformer-based unsupervised contrastive learning for histopathological image classification. Med. Image Anal. 81, 102559 (2022)","journal-title":"Med. Image Anal."},{"key":"19_CR52","first-page":"22419","volume":"34","author":"H Wu","year":"2021","unstructured":"Wu, H., Xu, J., Wang, J., Long, M.: Autoformer: decomposition transformers with auto-correlation for long-term series forecasting. Adv. Neural. Inf. Process. Syst. 34, 22419\u201322430 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"19_CR53","unstructured":"Xiang, J., Zhang, J.: Exploring low-rank property in multiple instance learning for whole slide image classification. In: The Eleventh International Conference on Learning Representations (2023)"},{"key":"19_CR54","doi-asserted-by":"crossref","unstructured":"Xiong, Y., et al.: Nystr\u00f6mformer: a nystr\u00f6m-based algorithm for approximating self-attention. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a035, pp. 14138\u201314148 (2021)","DOI":"10.1609\/aaai.v35i16.17664"},{"key":"19_CR55","doi-asserted-by":"crossref","unstructured":"Xu, G., et al.: Camel: a weakly supervised learning framework for histopathology image segmentation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 10682\u201310691 (2019)","DOI":"10.1109\/ICCV.2019.01078"},{"key":"19_CR56","unstructured":"Yang, L., Mehta, D., Liu, S., Mahapatra, D., Di\u00a0Ieva, A., Ge, Z.: Tpmil: trainable prototype enhanced multiple instance learning for whole slide image classification. arXiv preprint arXiv:2305.00696 (2023)"},{"key":"19_CR57","doi-asserted-by":"publisher","first-page":"102748","DOI":"10.1016\/j.media.2023.102748","volume":"85","author":"JG Yu","year":"2023","unstructured":"Yu, J.G., et al.: Prototypical multiple instance learning for predicting lymph node metastasis of breast cancer from whole-slide pathological images. Med. Image Anal. 85, 102748 (2023)","journal-title":"Med. Image Anal."},{"key":"19_CR58","first-page":"9422","volume":"33","author":"Y Yu","year":"2020","unstructured":"Yu, Y., Chan, K.H.R., You, C., Song, C., Ma, Y.: Learning diverse and discriminative representations via the principle of maximal coding rate reduction. Adv. Neural. Inf. Process. Syst. 33, 9422\u20139434 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"19_CR59","doi-asserted-by":"crossref","unstructured":"Zhang, H., et al.: Dtfd-mil: double-tier feature distillation multiple instance learning for histopathology whole slide image classification. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 18802\u201318812 (2022)","DOI":"10.1109\/CVPR52688.2022.01824"},{"issue":"1","key":"19_CR60","doi-asserted-by":"publisher","first-page":"6796","DOI":"10.1038\/s41467-023-42504-y","volume":"14","author":"S Zhao","year":"2023","unstructured":"Zhao, S., et al.: Single-cell morphological and topological atlas reveals the ecosystem diversity of human breast cancer. Nat. Commun. 14(1), 6796 (2023)","journal-title":"Nat. Commun."},{"key":"19_CR61","doi-asserted-by":"crossref","unstructured":"Zhou, H., et al.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a035, pp. 11106\u201311115 (2021)","DOI":"10.1609\/aaai.v35i12.17325"},{"key":"19_CR62","unstructured":"Zhu, W., Qiu, P., Dumitrascu, O.M., Wang, Y.: Pdl: regularizing multiple instance learning with progressive dropout layers. arXiv preprint arXiv:2308.10112 (2023)"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-72920-1_19","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,30]],"date-time":"2024-09-30T08:15:29Z","timestamp":1727684129000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-72920-1_19"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,1]]},"ISBN":["9783031729195","9783031729201"],"references-count":62,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-72920-1_19","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,1]]},"assertion":[{"value":"1 October 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Milan","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2024.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}