{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,26]],"date-time":"2025-05-26T09:10:10Z","timestamp":1748250610529,"version":"3.41.0"},"publisher-location":"Cham","reference-count":67,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031915840","type":"print"},{"value":"9783031915857","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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\/978-3-031-91585-7_21","type":"book-chapter","created":{"date-parts":[[2025,5,26]],"date-time":"2025-05-26T08:30:43Z","timestamp":1748248243000},"page":"345-362","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Bottom-Up Approach to\u00a0Class-Agnostic Image Segmentation"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0390-2803","authenticated-orcid":false,"given":"Sebastian","family":"Dille","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0001-9807-7385","authenticated-orcid":false,"given":"Ari","family":"Blondal","sequence":"additional","affiliation":[]},{"given":"Sylvain","family":"Paris","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1495-0491","authenticated-orcid":false,"given":"Ya\u011f\u0131z","family":"Aksoy","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,5,12]]},"reference":[{"key":"21_CR1","doi-asserted-by":"crossref","unstructured":"Atigh, M.G., Schoep, J., Acar, E., Van\u00a0Noord, N., Mettes, P.: Hyperbolic image segmentation. In: Proc. CVPR (2022)","DOI":"10.1109\/CVPR52688.2022.00441"},{"key":"21_CR2","doi-asserted-by":"crossref","unstructured":"Bai, M., Urtasun, R.: Deep watershed transform for instance segmentation. In: Proc. CVPR (2017)","DOI":"10.1109\/CVPR.2017.305"},{"key":"21_CR3","unstructured":"Banerjee, A., Dhillon, I.S., Ghosh, J., Sra, S., Ridgeway, G.: Clustering on the unit hypersphere using von mises-fisher distributions. J. Mach. Learn. Res. 6(9) (2005)"},{"issue":"2","key":"21_CR4","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1037\/0033-295X.94.2.115","volume":"94","author":"I Biederman","year":"1987","unstructured":"Biederman, I.: Recognition-by-components: a theory of human image understanding. Psychol. Rev. 94(2), 115\u2013147 (1987)","journal-title":"Psychol. Rev."},{"key":"21_CR5","doi-asserted-by":"crossref","unstructured":"Caesar, H., Uijlings, J., Ferrari, V.: Coco-stuff: thing and stuff classes in context. In: Proc. CVPR (2018)","DOI":"10.1109\/CVPR.2018.00132"},{"issue":"5","key":"21_CR6","doi-asserted-by":"publisher","first-page":"1483","DOI":"10.1109\/TPAMI.2019.2956516","volume":"43","author":"Z Cai","year":"2021","unstructured":"Cai, Z., Vasconcelos, N.: Cascade R-CNN: high quality object detection and instance segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 43(5), 1483\u20131498 (2021)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"21_CR7","volume-title":"Intrinsic Image Decomposition Via Ordinal Shading","author":"C Careaga","year":"2023","unstructured":"Careaga, C., Aksoy, Y.: Intrinsic Image Decomposition Via Ordinal Shading. ACM Trans, Graph (2023)"},{"key":"21_CR8","unstructured":"Cetin, E., Chamberlain, B.P., Bronstein, M.M., Hunt, J.J.: Hyperbolic deep reinforcement learning. In: Proc. ICLR (2023)"},{"issue":"4","key":"21_CR9","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","volume":"40","author":"LC Chen","year":"2017","unstructured":"Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: DeepLab: semantic image segmentation with deep convolutional nets, Atrous convolution, and fully connected CRFs. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 834\u2013848 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"21_CR10","unstructured":"Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: Proc. ICML (2020)"},{"key":"21_CR11","doi-asserted-by":"crossref","unstructured":"Cheng, B., et al.: Panoptic-DeepLab: a simple, strong, and fast baseline for bottom-up panoptic segmentation. In: Proc. CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.01249"},{"key":"21_CR12","doi-asserted-by":"crossref","unstructured":"Cheng, B., Girshick, R., Doll\u00e1r, P., Berg, A.C., Kirillov, A.: Boundary IoU: improving object-centric image segmentation evaluation. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.01508"},{"key":"21_CR13","doi-asserted-by":"crossref","unstructured":"Cheng, B., Misra, I., Schwing, A.G., Kirillov, A., Girdhar, R.: Masked-attention mask transformer for universal image segmentation. In: Proc. CVPR (2022)","DOI":"10.1109\/CVPR52688.2022.00135"},{"key":"21_CR14","unstructured":"Cheng, B., Schwing, A.G., Kirillov, A.: Per-pixel classification is not all you need for semantic segmentation. In: Proc. NeurIPS (2021)"},{"issue":"12","key":"21_CR15","first-page":"2071","volume":"9","author":"HD Cheng","year":"2000","unstructured":"Cheng, H.D., Sun, Y.: A hierarchical approach to color image segmentation using homogeneity. IEEE Trans. Pattern Anal. Mach. Intell. 9(12), 2071\u20132082 (2000)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"21_CR16","doi-asserted-by":"crossref","unstructured":"De\u00a0Brabandere, B., Neven, D., Van\u00a0Gool, L.: Semantic instance segmentation for autonomous driving. In: Proc. CVPR Workshops (2017)","DOI":"10.1109\/CVPRW.2017.66"},{"key":"21_CR17","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: Proc. CVPR (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"21_CR18","unstructured":"Fathi, A., et al.: Semantic instance segmentation via deep metric learning. arXiv preprint arXiv:1703.10277 [cs] (2017)"},{"key":"21_CR19","doi-asserted-by":"crossref","unstructured":"Gao, N., et al.: SSAP: single-shot instance segmentation with affinity pyramid. In: Proc. ICCV (2019)","DOI":"10.1109\/ICCV.2019.00073"},{"key":"21_CR20","doi-asserted-by":"crossref","unstructured":"Ge, S., Mishra, S., Kornblith, S., Li, C.L., Jacobs, D.: Hyperbolic contrastive learning for visual representations beyond objects. In: Proc. CVPR (2023)","DOI":"10.1109\/CVPR52729.2023.00661"},{"key":"21_CR21","doi-asserted-by":"crossref","unstructured":"Gong, K., Liang, X., Li, Y., Chen, Y., Yang, M., Lin, L.: Instance-level human parsing via part grouping network. In: Proc. ECCV (2018)","DOI":"10.1007\/978-3-030-01225-0_47"},{"key":"21_CR22","unstructured":"Gopal, S., Yang, Y.: Von Mises-fisher clustering models. In: International Conference on Machine Learning, pp. 154\u2013162. PMLR (2014)"},{"key":"21_CR23","doi-asserted-by":"crossref","unstructured":"He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.: Momentum contrast for unsupervised visual representation learning. In: Proc. CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"21_CR24","doi-asserted-by":"publisher","first-page":"386","DOI":"10.1109\/TPAMI.2018.2844175","volume":"42","author":"K He","year":"2020","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., Girshick, R.B.: Mask R-CNN. IEEE Trans. Pattern Anal. Mach. Intell. 42, 386\u2013397 (2020)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"21_CR25","doi-asserted-by":"crossref","unstructured":"Hwang, J., Oh, S.W., Lee, J.Y., Han, B.: Exemplar-based open-set panoptic segmentation network. In: Proc. CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.00123"},{"key":"21_CR26","doi-asserted-by":"crossref","unstructured":"Hwang, J.J., et al.: SegSort: segmentation by discriminative sorting of segments. In: Proc. ICCV (2019)","DOI":"10.1109\/ICCV.2019.00743"},{"key":"21_CR27","doi-asserted-by":"crossref","unstructured":"Jain, J., Li, J., Chiu, M., Hassani, A., Orlov, N., Shi, H.: OneFormer: one transformer to rule universal image segmentation. In: Proc. CVPR (2023)","DOI":"10.1109\/CVPR52729.2023.00292"},{"key":"21_CR28","unstructured":"Ji, S., Park, H.W.: Image segmentation of color image based on region coherency. In: Proc. ICIP (1998)"},{"key":"21_CR29","doi-asserted-by":"crossref","unstructured":"Khalid, N., et al.: DeepCens: an end-to-end pipeline for cell and nucleus segmentation in microscopic images. In: Proc. IJCNN. IEEE (2021)","DOI":"10.1109\/IJCNN52387.2021.9533624"},{"key":"21_CR30","doi-asserted-by":"crossref","unstructured":"Khrulkov, V., Mirvakhabova, L., Ustinova, E., Oseledets, I., Lempitsky, V.: Hyperbolic image embeddings. In: Proc. CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.00645"},{"key":"21_CR31","doi-asserted-by":"crossref","unstructured":"Kirillov, A., Girshick, R., He, K., Doll\u00e1r, P.: Panoptic feature pyramid networks. In: Proc. CVPR (2019)","DOI":"10.1109\/CVPR.2019.00656"},{"key":"21_CR32","doi-asserted-by":"crossref","unstructured":"Kirillov, A., He, K., Girshick, R., Rother, C., Doll\u00e1r, P.: Panoptic segmentation. In: Proc. CVPR (2019)","DOI":"10.1109\/CVPR.2019.00963"},{"key":"21_CR33","doi-asserted-by":"crossref","unstructured":"Kirillov, A., et al.: Segment anything. In: Proc. ICCV (2023)","DOI":"10.1109\/ICCV51070.2023.00371"},{"key":"21_CR34","doi-asserted-by":"crossref","unstructured":"Kong, S., Fowlkes, C.C.: Recurrent pixel embedding for instance grouping. In: Proc. CVPR (2018)","DOI":"10.1109\/CVPR.2018.00940"},{"key":"21_CR35","doi-asserted-by":"crossref","unstructured":"Li, F., et al.: Mask Dino: towards a unified transformer-based framework for object detection and segmentation. In: Proc. CVPR (2023)","DOI":"10.1109\/CVPR52729.2023.00297"},{"key":"21_CR36","doi-asserted-by":"crossref","unstructured":"Li, Z., Snavely, N.: MegaDepth: learning single-view depth prediction from internet photos. In: Proc. CVPR (2018)","DOI":"10.1109\/CVPR.2018.00218"},{"key":"21_CR37","doi-asserted-by":"crossref","unstructured":"Li, Z., et al.: Panoptic SegFormer: delving deeper into panoptic segmentation with transformers. In: Proc. CVPR (2022)","DOI":"10.1109\/CVPR52688.2022.00134"},{"key":"21_CR38","doi-asserted-by":"crossref","unstructured":"Lin, F., Bai, B., Guo, Y., Chen, H., Ren, Y., Xu, Z.: MHCN: a hyperbolic neural network model for multi-view hierarchical clustering. In: Proc. ICCV (2023)","DOI":"10.1109\/ICCV51070.2023.01515"},{"key":"21_CR39","doi-asserted-by":"crossref","unstructured":"Lin, G., Liu, F., Milan, A., Shen, C., Reid, I.: RefineNet: multi-path refinement networks for dense prediction. IEEE Trans. Pattern Anal. Mach, Intell (2019)","DOI":"10.1109\/TPAMI.2019.2893630"},{"key":"21_CR40","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., et al.: Microsoft CoCo: common objects in context. In: Proc. ECCV (2014)","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"21_CR41","unstructured":"Liu, S., Zhi, S., Johns, E., Davison, A.: Bootstrapping semantic segmentation with regional contrast. In: Proc. ICLR (2022)"},{"key":"21_CR42","doi-asserted-by":"crossref","unstructured":"Liu, Z., Mao, H., Wu, C.Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proc. CVPR (2022)","DOI":"10.1109\/CVPR52688.2022.01167"},{"key":"21_CR43","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proc. CVPR (2015)","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"21_CR44","unstructured":"Luo, J., Gray, R.T., Lee, H.C.: Incorporation of derivative priors in adaptive Bayesian color image segmentation. In: Proc. ICIP (1998)"},{"issue":"4","key":"21_CR45","doi-asserted-by":"publisher","first-page":"1193","DOI":"10.2514\/1.28949","volume":"30","author":"FL Markley","year":"2007","unstructured":"Markley, F.L., Cheng, Y., Crassidis, J.L., Oshman, Y.: Averaging quaternions. J. Guid. Control. Dyn. 30(4), 1193\u20131197 (2007)","journal-title":"J. Guid. Control. Dyn."},{"key":"21_CR46","doi-asserted-by":"crossref","unstructured":"Miangoleh, S.M.H., Dille, S., Mai, L., Paris, S., Aksoy, Y.: Boosting monocular depth estimation models to high-resolution via content-adaptive multi-resolution merging. In: Proc. CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.00956"},{"issue":"7","key":"21_CR47","first-page":"3523","volume":"44","author":"S Minaee","year":"2021","unstructured":"Minaee, S., Boykov, Y., Porikli, F., Plaza, A., Kehtarnavaz, N., Terzopoulos, D.: Image segmentation using deep learning: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 44(7), 3523\u20133542 (2021)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"21_CR48","unstructured":"Nickel, M., Kiela, D.: Poincar\u00e9 embeddings for learning hierarchical representations. NeurIPS 30 (2017)"},{"key":"21_CR49","doi-asserted-by":"crossref","unstructured":"Nock, R., Nielsen, F.: Statistical region merging. IEEE Trans. Pattern Anal. Mach, Intell (2004)","DOI":"10.1109\/TPAMI.2004.110"},{"key":"21_CR50","doi-asserted-by":"crossref","unstructured":"Qi, L., et al.: Ca-SSL: class-agnostic semi-supervised learning for detection and segmentation. In: Proc. ECCV (2022)","DOI":"10.1007\/978-3-031-19821-2_4"},{"key":"21_CR51","doi-asserted-by":"crossref","unstructured":"Qi, L., et al.: High quality entity segmentation. In: Proc. ICCV (2023)","DOI":"10.1109\/ICCV51070.2023.00374"},{"key":"21_CR52","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3227513","volume-title":"Open world entity segmentation","author":"L Qi","year":"2022","unstructured":"Qi, L., et al.: Open world entity segmentation. IEEE Trans. Pattern Anal. Mach, Intell (2022)"},{"issue":"3","key":"21_CR53","doi-asserted-by":"publisher","first-page":"1623","DOI":"10.1109\/TPAMI.2020.3019967","volume":"44","author":"R Ranftl","year":"2020","unstructured":"Ranftl, R., Lasinger, K., Hafner, D., Schindler, K., Koltun, V.: Towards robust monocular depth estimation: Mixing datasets for zero-shot cross-dataset transfer. IEEE Trans. Pattern Anal. Mach. Intell. 44(3), 1623\u20131637 (2020)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"21_CR54","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Proc. MICCAI (2015)","DOI":"10.1007\/978-3-319-24574-4_28"},{"issue":"12","key":"21_CR55","doi-asserted-by":"publisher","first-page":"3863","DOI":"10.1093\/bioinformatics\/btaa225","volume":"36","author":"M Schwendy","year":"2020","unstructured":"Schwendy, M., Unger, R.E., Parekh, S.H.: EVICAN-a balanced dataset for algorithm development in cell and nucleus segmentation. Bioinformatics 36(12), 3863\u20133870 (2020)","journal-title":"Bioinformatics"},{"key":"21_CR56","doi-asserted-by":"crossref","unstructured":"Tian, Z., Shen, C., Chen, H.: Conditional convolutions for instance segmentation. In: Proc. ECCV (2020)","DOI":"10.1007\/978-3-030-58452-8_17"},{"issue":"7","key":"21_CR57","doi-asserted-by":"publisher","first-page":"1191","DOI":"10.1016\/S0031-3203(96)00147-1","volume":"30","author":"A Tremeau","year":"1997","unstructured":"Tremeau, A., Borel, N.: A region growing and merging algorithm to color segmentation. Pattern Recogn. 30(7), 1191\u20131203 (1997)","journal-title":"Pattern Recogn."},{"key":"21_CR58","doi-asserted-by":"crossref","unstructured":"Uhrig, J., Rehder, E., Fr\u00f6hlich, B., Franke, U., Brox, T.: Box2Pix: single-shot instance segmentation by assigning pixels to object boxes. In: 2018 IEEE Intelligent Vehicles Symposium (IV). IEEE (2018)","DOI":"10.1109\/IVS.2018.8500621"},{"key":"21_CR59","unstructured":"Wang, X., Zhang, R., Kong, T., Li, L., Shen, C.: SOLOV2: dynamic and fast instance segmentation. In: Proc. NeurIPS (2020)"},{"issue":"11","key":"21_CR60","first-page":"8587","volume":"44","author":"X Wang","year":"2021","unstructured":"Wang, X., Zhang, R., Shen, C., Kong, T., Li, L.: SOLO: a simple framework for instance segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 44(11), 8587\u20138601 (2021)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"21_CR61","doi-asserted-by":"crossref","unstructured":"Weng, Z., Ogut, M.G., Limonchik, S., Yeung, S.: Unsupervised discovery of the long-tail in instance segmentation using hierarchical self-supervision. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 2603\u20132612 (2021)","DOI":"10.1109\/CVPR46437.2021.00263"},{"key":"21_CR62","doi-asserted-by":"crossref","unstructured":"Xian, K., et al.: Monocular relative depth perception with web stereo data supervision. In: Proc. CVPR (2018)","DOI":"10.1109\/CVPR.2018.00040"},{"key":"21_CR63","unstructured":"Xu, H.M., Chen, H., Liu, L., Yin, Y.: Dual decision improves open-set panoptic segmentation. In: Proc. BMVC (2022)"},{"key":"21_CR64","unstructured":"Yi-de, M., Qing, L., Zhi-Bai, Q.: Automated image segmentation using improved PCNN model based on cross-entropy. In: Proceedings of 2004 International Symposium on Intelligent Multimedia, Video and Speech Processing, 2004. IEEE (2004)"},{"key":"21_CR65","doi-asserted-by":"crossref","unstructured":"Yu, Q., et al.: K-means mask transformer. In: Proc. ECCV (2022)","DOI":"10.1007\/978-3-031-19818-2_17"},{"key":"21_CR66","doi-asserted-by":"crossref","unstructured":"Zhou, B., Zhao, H., Puig, X., Fidler, S., Barriuso, A., Torralba, A.: Scene parsing through ADE20K dataset. In: Proc. CVPR (2017)","DOI":"10.1109\/CVPR.2017.544"},{"key":"21_CR67","doi-asserted-by":"crossref","unstructured":"Zhou, T., Wang, W., Konukoglu, E., Van\u00a0Gool, L.: Rethinking semantic segmentation: a prototype view. In: Proc. CVPR (2022)","DOI":"10.1109\/CVPR52688.2022.00261"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2024 Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-91585-7_21","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,26]],"date-time":"2025-05-26T08:31:11Z","timestamp":1748248271000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-91585-7_21"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031915840","9783031915857"],"references-count":67,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-91585-7_21","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"12 May 2025","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"}}]}}