{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T17:34:50Z","timestamp":1777570490887,"version":"3.51.4"},"publisher-location":"Cham","reference-count":72,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031729034","type":"print"},{"value":"9783031729041","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,11,21]],"date-time":"2024-11-21T00:00:00Z","timestamp":1732147200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,21]],"date-time":"2024-11-21T00:00:00Z","timestamp":1732147200000},"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\/978-3-031-72904-1_16","type":"book-chapter","created":{"date-parts":[[2024,11,20]],"date-time":"2024-11-20T13:27:45Z","timestamp":1732109265000},"page":"268-286","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Made to\u00a0Order: Discovering Monotonic Temporal Changes via\u00a0Self-supervised Video Ordering"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-7044-1901","authenticated-orcid":false,"given":"Charig","family":"Yang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-8609-6826","authenticated-orcid":false,"given":"Weidi","family":"Xie","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8945-8573","authenticated-orcid":false,"given":"Andrew","family":"Zisserman","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,11,21]]},"reference":[{"key":"16_CR1","doi-asserted-by":"crossref","unstructured":"Abnar, S., Zuidema, W.: Quantifying attention flow in transformers. arXiv preprint arXiv:2005.00928 (2020)","DOI":"10.18653\/v1\/2020.acl-main.385"},{"key":"16_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"208","DOI":"10.1007\/978-3-030-58523-5_13","volume-title":"Computer Vision \u2013 ECCV 2020","author":"T Afouras","year":"2020","unstructured":"Afouras, T., Owens, A., Chung, J.S., Zisserman, A.: Self-supervised learning of audio-visual objects from video. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12363, pp. 208\u2013224. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58523-5_13"},{"key":"16_CR3","doi-asserted-by":"crossref","unstructured":"Arandjelovic, R., Zisserman, A.: Objects that sound. In: ECCV (2018)","DOI":"10.1007\/978-3-030-01246-5_27"},{"key":"16_CR4","doi-asserted-by":"crossref","unstructured":"Bain, M., Nagrani, A., Varol, G., Zisserman, A.: Frozen in time: a joint video and image encoder for end-to-end retrieval. In: ICCV (2021)","DOI":"10.1109\/ICCV48922.2021.00175"},{"key":"16_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"654","DOI":"10.1007\/978-3-642-33783-3_47","volume-title":"Computer Vision \u2013 ECCV 2012","author":"T Basha","year":"2012","unstructured":"Basha, T., Moses, Y., Avidan, S.: Photo sequencing. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7577, pp. 654\u2013667. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-33783-3_47"},{"key":"16_CR6","doi-asserted-by":"crossref","unstructured":"Basha, T.D., Moses, Y., Avidan, S.: Space-time tradeoffs in photo sequencing. In: ICCV (2013)","DOI":"10.1007\/978-3-642-33783-3_47"},{"key":"16_CR7","doi-asserted-by":"crossref","unstructured":"Benaim, S., et al.: Speednet: learning the speediness in videos. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.00994"},{"key":"16_CR8","unstructured":"Bertasius, G., Wang, H., Torresani, L.: Is space-time attention all you need for video understanding? In: ICML (2021)"},{"key":"16_CR9","doi-asserted-by":"crossref","unstructured":"Bian, Z., Jabri, A., Efros, A.A., Owens, A.: Learning pixel trajectories with multiscale contrastive random walks. In: CVPR (2022)","DOI":"10.1109\/CVPR52688.2022.00640"},{"key":"16_CR10","unstructured":"Bideau, P., Learned-Miller, E.: A detailed rubric for motion segmentation. arXiv preprint arXiv:1610.10033 (2016)"},{"key":"16_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"433","DOI":"10.1007\/978-3-319-46484-8_26","volume-title":"Computer Vision \u2013 ECCV 2016","author":"P Bideau","year":"2016","unstructured":"Bideau, P., Learned-Miller, E.: It\u2019s moving! A probabilistic model for causal motion segmentation in moving camera videos. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9912, pp. 433\u2013449. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46484-8_26"},{"key":"16_CR12","doi-asserted-by":"crossref","unstructured":"Blinkouskaya, Y., Weickenmeier, J.: Brain shape changes associated with cerebral atrophy in healthy aging and Alzheimer\u2019s disease. Front. Mech. Eng. (2021)","DOI":"10.3389\/fmech.2021.705653"},{"key":"16_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"677","DOI":"10.1007\/978-3-030-58545-7_39","volume-title":"Computer Vision \u2013 ECCV 2020","author":"A Brown","year":"2020","unstructured":"Brown, A., Xie, W., Kalogeiton, V., Zisserman, A.: Smooth-AP: smoothing the path towards large-scale image retrieval. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12354, pp. 677\u2013694. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58545-7_39"},{"key":"16_CR14","doi-asserted-by":"crossref","unstructured":"Chen, H., Xie, W., Afouras, T., Nagrani, A., Vedaldi, A., Zisserman, A.: Localizing visual sounds the hard way. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.01659"},{"key":"16_CR15","unstructured":"Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: ICML (2021)"},{"key":"16_CR16","unstructured":"Chen, X., Qiu, X., Huang, X.: Neural sentence ordering. arXiv preprint arXiv:1607.06952 (2016)"},{"key":"16_CR17","doi-asserted-by":"crossref","unstructured":"Cui, B., Li, Y., Chen, M., Zhang, Z.: Deep attentive sentence ordering network. In: EMNLP (2018)","DOI":"10.18653\/v1\/D18-1465"},{"key":"16_CR18","unstructured":"Cuturi, M., Teboul, O., Vert, J.P.: Differentiable ranking and sorting using optimal transport. In: NeurIPS (2019)"},{"key":"16_CR19","doi-asserted-by":"crossref","unstructured":"Fernando, B., Bilen, H., Gavves, E., Gould, S.: Self-supervised video representation learning with odd-one-out networks. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.607"},{"key":"16_CR20","doi-asserted-by":"crossref","unstructured":"Fernando, B., Gavves, E., Oramas, J.M., Ghodrati, A., Tuytelaars, T.: Modeling video evolution for action recognition. In: CVPR (2015)","DOI":"10.1109\/CVPR.2015.7299176"},{"key":"16_CR21","doi-asserted-by":"crossref","unstructured":"Fong, R., Patrick, M., Vedaldi, A.: Understanding deep networks via extremal perturbations and smooth masks. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00304"},{"key":"16_CR22","doi-asserted-by":"crossref","unstructured":"Fong, R.C., Vedaldi, A.: Interpretable explanations of black boxes by meaningful perturbation. In: ICCV (2017)","DOI":"10.1109\/ICCV.2017.371"},{"key":"16_CR23","doi-asserted-by":"crossref","unstructured":"Godard, C., Mac\u00a0Aodha, O., Firman, M., Brostow, G.J.: Digging into self-supervised monocular depth estimation. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00393"},{"key":"16_CR24","unstructured":"Goodfellow, I.J., Bulatov, Y., Ibarz, J., Arnoud, S., Shet, V.: Multi-digit number recognition from street view imagery using deep convolutional neural networks. arXiv preprint arXiv:1312.6082 (2013)"},{"key":"16_CR25","unstructured":"Grill, J.B., et al.: Bootstrap your own latent: a new approach to self-supervised learning. In: NeurIPS (2021)"},{"key":"16_CR26","unstructured":"Grover, A., Wang, E., Zweig, A., Ermon, S.: Stochastic optimization of sorting networks via continuous relaxations. arXiv preprint arXiv:1903.08850 (2019)"},{"key":"16_CR27","doi-asserted-by":"crossref","unstructured":"Hafner, S., Ban, Y., Nascetti, A.: Urban change detection using a dual-task siamese network and semi-supervised learning. In: IGARSS (2022)","DOI":"10.1109\/IGARSS46834.2022.9883982"},{"key":"16_CR28","unstructured":"Han, T., Xie, W., Zisserman, A.: Self-supervised co-training for video representation learning. In: NeurIPS (2020)"},{"key":"16_CR29","doi-asserted-by":"crossref","unstructured":"He, K., Chen, X., Xie, S., Li, Y., Doll\u00e1r, P., Girshick, R.: Masked autoencoders are scalable vision learners. In: CVPR (2022)","DOI":"10.1109\/CVPR52688.2022.01553"},{"key":"16_CR30","unstructured":"Iashin, V., Xie, W., Rahtu, E., Zisserman, A.: Sparse in space and time: audio-visual synchronisation with trainable selectors. In: BMVC (2022)"},{"key":"16_CR31","unstructured":"Jabri, A., Owens, A., Efros, A.A.: Space-time correspondence as a contrastive random walk. In: NeurIPS (2020)"},{"key":"16_CR32","unstructured":"Kim, H., Sabuncu, M.R.: Learning to compare longitudinal images. arXiv preprint arXiv:2304.02531 (2023)"},{"key":"16_CR33","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"16_CR34","doi-asserted-by":"crossref","unstructured":"Kirillov, A., et al.: Segment anything. In: ICCV (2023)","DOI":"10.1109\/ICCV51070.2023.00371"},{"key":"16_CR35","doi-asserted-by":"crossref","unstructured":"Lai, Z., Lu, E., Xie, W.: Mast: a memory-augmented self-supervised tracker. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.00651"},{"key":"16_CR36","unstructured":"Lamdouar, H., Xie, W., Zisserman, A.: Segmenting invisible moving objects. In: BMVC (2021)"},{"key":"16_CR37","doi-asserted-by":"crossref","unstructured":"Lamdouar, H., Yang, C., Xie, W., Zisserman, A.: Betrayed by motion: camouflaged object discovery via motion segmentation. In: ACCV (2020)","DOI":"10.1007\/978-3-030-69532-3_30"},{"key":"16_CR38","doi-asserted-by":"crossref","unstructured":"LaMontagne, P.J., et\u00a0al.: Oasis-3: longitudinal neuroimaging, clinical, and cognitive dataset for normal aging and Alzheimer disease. MedRxiv (2019)","DOI":"10.1101\/2019.12.13.19014902"},{"key":"16_CR39","doi-asserted-by":"crossref","unstructured":"LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE (1998)","DOI":"10.1109\/5.726791"},{"key":"16_CR40","doi-asserted-by":"crossref","unstructured":"Lee, H.Y., Huang, J.B., Singh, M., Yang, M.H.: Unsupervised representation learning by sorting sequences. In: ICCV (2017)","DOI":"10.1109\/ICCV.2017.79"},{"key":"16_CR41","doi-asserted-by":"crossref","unstructured":"Liu, J., Ju, C., Xie, W., Zhang, Y.: Exploiting transformation invariance and equivariance for self-supervised sound localisation. In: ACM MM (2022)","DOI":"10.1145\/3503161.3548317"},{"key":"16_CR42","doi-asserted-by":"crossref","unstructured":"Liu, P., Lyu, M., King, I., Xu, J.: Selflow: self-supervised learning of optical flow. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00470"},{"key":"16_CR43","unstructured":"Malila, W.A.: Change vector analysis: an approach for detecting forest changes with landsat. In: LARS Symposia (1980)"},{"key":"16_CR44","unstructured":"Mall, U., Hariharan, B., Bala, K.: Change event dataset for discovery from spatio-temporal remote sensing imagery. In: NeurIPS (2022)"},{"key":"16_CR45","doi-asserted-by":"crossref","unstructured":"Mall, U., Hariharan, B., Bala, K.: Change-aware sampling and contrastive learning for satellite images. In: CVPR (2023)","DOI":"10.1109\/CVPR52729.2023.00509"},{"key":"16_CR46","doi-asserted-by":"crossref","unstructured":"Meister, S., Hur, J., Roth, S.: Unflow: unsupervised learning of optical flow with a bidirectional census loss. In: AAAI (2018)","DOI":"10.1609\/aaai.v32i1.12276"},{"key":"16_CR47","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"527","DOI":"10.1007\/978-3-319-46448-0_32","volume-title":"Computer Vision \u2013 ECCV 2016","author":"I Misra","year":"2016","unstructured":"Misra, I., Zitnick, C.L., Hebert, M.: Shuffle and learn: unsupervised learning using temporal order verification. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 527\u2013544. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46448-0_32"},{"key":"16_CR48","doi-asserted-by":"crossref","unstructured":"Neff, R., Schwartz, S., Stork, D.G.: Electronics for generating simultaneous random-dot cyclopean and monocular stimuli. Behav. Res. Methods Instrum. Comput. (1985)","DOI":"10.3758\/BF03200943"},{"key":"16_CR49","unstructured":"Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., Ng, A.Y.: Reading digits in natural images with unsupervised feature learning. In: NeurIPS (2011)"},{"key":"16_CR50","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1007\/978-3-319-46466-4_5","volume-title":"Computer Vision \u2013 ECCV 2016","author":"M Noroozi","year":"2016","unstructured":"Noroozi, M., Favaro, P.: Unsupervised learning of visual representations by solving jigsaw puzzles. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9910, pp. 69\u201384. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46466-4_5"},{"key":"16_CR51","doi-asserted-by":"crossref","unstructured":"Patriarche, J., Erickson, B.: A review of the automated detection of change in serial imaging studies of the brain. J. Digit. Imaging (2004)","DOI":"10.1007\/s10278-004-1010-x"},{"key":"16_CR52","unstructured":"Petersen, F., Borgelt, C., Kuehne, H., Deussen, O.: Differentiable sorting networks for scalable sorting and ranking supervision. In: ICML (2021)"},{"key":"16_CR53","unstructured":"Petersen, F., Borgelt, C., Kuehne, H., Deussen, O.: Monotonic differentiable sorting networks. arXiv preprint arXiv:2203.09630 (2022)"},{"key":"16_CR54","doi-asserted-by":"crossref","unstructured":"Sachdeva, R., Zisserman, A.: The change you want to see. In: WACV (2023)","DOI":"10.1109\/WACV56688.2023.00398"},{"issue":"6","key":"16_CR55","doi-asserted-by":"publisher","first-page":"3677","DOI":"10.1109\/TGRS.2018.2886643","volume":"57","author":"S Saha","year":"2019","unstructured":"Saha, S., Bovolo, F., Bruzzone, L.: Unsupervised deep change vector analysis for multiple-change detection in VHR images. IEEE Trans. Geosci. Remote Sens. 57(6), 3677\u20133693 (2019)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"16_CR56","doi-asserted-by":"crossref","unstructured":"Sakurada, K., Okatani, T.: Change detection from a street image pair using CNN features and superpixel segmentation. In: BMVC (2015)","DOI":"10.5244\/C.29.61"},{"issue":"7","key":"16_CR57","doi-asserted-by":"publisher","first-page":"989","DOI":"10.1001\/archneur.60.7.989","volume":"60","author":"RI Scahill","year":"2003","unstructured":"Scahill, R.I., Frost, C., Jenkins, R., Whitwell, J.L., Rossor, M.N., Fox, N.C.: A longitudinal study of brain volume changes in normal aging using serial registered magnetic resonance imaging. Arch. Neurol. 60(7), 989\u2013994 (2003)","journal-title":"Arch. Neurol."},{"key":"16_CR58","doi-asserted-by":"crossref","unstructured":"Sevilla-Lara, L., Zha, S., Yan, Z., Goswami, V., Feiszli, M., Torresani, L.: Only time can tell: discovering temporal data for temporal modeling. In: WACV (2021)","DOI":"10.1109\/WACV48630.2021.00058"},{"key":"16_CR59","doi-asserted-by":"crossref","unstructured":"Shvetsova, N., Petersen, F., Kukleva, A., Schiele, B., Kuehne, H.: Learning by sorting: self-supervised learning with group ordering constraints. ICCV (2023)","DOI":"10.1109\/ICCV51070.2023.01508"},{"key":"16_CR60","doi-asserted-by":"crossref","unstructured":"Stent, S., Gherardi, R., Stenger, B., Cipolla, R.: Detecting change for multi-view, long-term surface inspection. In: BMVC (2015)","DOI":"10.5244\/C.29.127"},{"key":"16_CR61","doi-asserted-by":"crossref","unstructured":"Svennerholm, L., Bostr\u00f6m, K., Jungbjer, B.: Changes in weight and compositions of major membrane components of human brain during the span of adult human life of swedes. Acta neuropathologica (1997)","DOI":"10.1007\/s004010050717"},{"key":"16_CR62","doi-asserted-by":"crossref","unstructured":"Van\u00a0Etten, A., Hogan, D., Manso, J.M., Shermeyer, J., Weir, N., Lewis, R.: The multi-temporal urban development spacenet dataset. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.00633"},{"key":"16_CR63","unstructured":"Vinyals, O., Fortunato, M., Jaitly, N.: Pointer networks. In: NeurIPS (2015)"},{"key":"16_CR64","doi-asserted-by":"crossref","unstructured":"Wang, X., Jabri, A., Efros, A.A.: Learning correspondence from the cycle-consistency of time. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00267"},{"key":"16_CR65","doi-asserted-by":"crossref","unstructured":"Wei, D., Lim, J.J., Zisserman, A., Freeman, W.T.: Learning and using the arrow of time. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00840"},{"key":"16_CR66","unstructured":"Xie, J., Xie, W., Zisserman, A.: Segmenting moving objects via an object-centric layered representation. In: NeurIPS (2022)"},{"key":"16_CR67","doi-asserted-by":"crossref","unstructured":"Yang, C., Lamdouar, H., Lu, E., Zisserman, A., Xie, W.: Self-supervised video object segmentation by motion grouping. In: ICCV (2021)","DOI":"10.1109\/ICCV48922.2021.00709"},{"key":"16_CR68","doi-asserted-by":"crossref","unstructured":"Yang, C., Xie, W., Zisserman, A.: It\u2019s about time: analog clock reading in the wild. In: CVPR (2022)","DOI":"10.1109\/CVPR52688.2022.00254"},{"key":"16_CR69","unstructured":"Zarrabi, N., Avidan, S., Moses, Y.: Crowdcam: dynamic region segmentation. arXiv preprint arXiv:1811.11455 (2018)"},{"key":"16_CR70","doi-asserted-by":"crossref","unstructured":"Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.: Learning deep features for discriminative localization. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.319"},{"key":"16_CR71","doi-asserted-by":"crossref","unstructured":"Zhou, T., Brown, M., Snavely, N., Lowe, D.G.: Unsupervised learning of depth and ego-motion from video. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.700"},{"key":"16_CR72","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"470","DOI":"10.1007\/978-3-030-58526-6_28","volume-title":"Computer Vision \u2013 ECCV 2020","author":"D Zhukov","year":"2020","unstructured":"Zhukov, D., Alayrac, J.-B., Laptev, I., Sivic, J.: Learning actionness via long-range temporal order verification. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12374, pp. 470\u2013487. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58526-6_28"}],"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\/978-3-031-72904-1_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,15]],"date-time":"2025-03-15T19:52:06Z","timestamp":1742068326000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-72904-1_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,21]]},"ISBN":["9783031729034","9783031729041"],"references-count":72,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-72904-1_16","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,21]]},"assertion":[{"value":"21 November 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"}}]}}