{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T17:40:18Z","timestamp":1777657218431,"version":"3.51.4"},"publisher-location":"Cham","reference-count":75,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031729324","type":"print"},{"value":"9783031729331","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,10,3]],"date-time":"2024-10-03T00:00:00Z","timestamp":1727913600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,10,3]],"date-time":"2024-10-03T00:00:00Z","timestamp":1727913600000},"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\/978-3-031-72933-1_6","type":"book-chapter","created":{"date-parts":[[2024,10,2]],"date-time":"2024-10-02T12:02:53Z","timestamp":1727870573000},"page":"91-109","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["DGInStyle: Domain-Generalizable Semantic Segmentation with\u00a0Image Diffusion Models and\u00a0Stylized Semantic Control"],"prefix":"10.1007","author":[{"given":"Yuru","family":"Jia","sequence":"first","affiliation":[]},{"given":"Lukas","family":"Hoyer","sequence":"additional","affiliation":[]},{"given":"Shengyu","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Tianfu","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Luc","family":"Van Gool","sequence":"additional","affiliation":[]},{"given":"Konrad","family":"Schindler","sequence":"additional","affiliation":[]},{"given":"Anton","family":"Obukhov","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,10,3]]},"reference":[{"key":"6_CR1","unstructured":"Azizi, S., Kornblith, S., Saharia, C., Norouzi, M., Fleet, D.J.: Synthetic data from diffusion models improves ImageNet classification. arXiv:2304.08466 (2023)"},{"key":"6_CR2","doi-asserted-by":"crossref","unstructured":"Bansal, A., et al.: Universal guidance for diffusion models. arXiv:2302.07121 (2023)","DOI":"10.1109\/CVPRW59228.2023.00091"},{"key":"6_CR3","unstructured":"Bar-Tal, O., Yariv, L., Lipman, Y., Dekel, T.: MultiDiffusion: fusing diffusion paths for controlled image generation. arXiv:2302.08113 (2023)"},{"key":"6_CR4","unstructured":"Brock, A., Donahue, J., Simonyan, K.: Large scale GAN training for high fidelity natural image synthesis. arXiv:1809.11096 (2019)"},{"key":"6_CR5","doi-asserted-by":"crossref","unstructured":"Cai, S., et al.: DiffDreamer: towards consistent unsupervised single-view scene extrapolation with conditional diffusion models. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (2023)","DOI":"10.1109\/ICCV51070.2023.00204"},{"key":"6_CR6","doi-asserted-by":"crossref","unstructured":"Cai, S., Obukhov, A., Dai, D., Van\u00a0Gool, L.: Pix2NeRF: unsupervised conditional P-GAN for single image to neural radiance fields translation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (2022)","DOI":"10.1109\/CVPR52688.2022.00395"},{"key":"6_CR7","unstructured":"Chen, K., et al.: GeoDiffusion: Text-prompted geometric control for object detection data generation. arXiv:2306.04607 (2023)"},{"key":"6_CR8","doi-asserted-by":"crossref","unstructured":"Chen, M., Laina, I., Vedaldi, A.: Training-free layout control with cross-attention guidance. arXiv:2304.03373 (2023)","DOI":"10.1109\/WACV57701.2024.00526"},{"key":"6_CR9","doi-asserted-by":"crossref","unstructured":"Choi, S., Jung, S., Yun, H., Kim, J.T., Kim, S., Choo, J.: RobustNet: improving domain generalization in urban-scene segmentation via instance selective whitening. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (2021)","DOI":"10.1109\/CVPR46437.2021.01141"},{"key":"6_CR10","doi-asserted-by":"crossref","unstructured":"Cordts, M., et al.: The cityscapes dataset for semantic urban scene understanding. In: IEEE Conference on Computer Vision and Pattern Recognition (2016)","DOI":"10.1109\/CVPR.2016.350"},{"key":"6_CR11","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: IEEE Conference on Computer Vision and Pattern Recognition. IEEE (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"6_CR12","unstructured":"Dhariwal, P., Nichol, A.: Diffusion models beat GANs on image synthesis. arXiv:2105.05233 (2021)"},{"key":"6_CR13","doi-asserted-by":"crossref","unstructured":"Ding, J., Xue, N., Xia, G.S., Schiele, B., Dai, D.: HGFormer: hierarchical grouping transformer for domain generalized semantic segmentation. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (2023)","DOI":"10.1109\/CVPR52729.2023.01479"},{"key":"6_CR14","unstructured":"Dunlap, L., Umino, A., Zhang, H., Yang, J., Gonzalez, J.E., Darrell, T.: Diversify your vision datasets with automatic diffusion-based augmentation. arXiv:2305.16289 (2023)"},{"key":"6_CR15","unstructured":"Ganin, Y., Lempitsky, V.: Unsupervised domain adaptation by backpropagation. In: International Conference on Machine Learning (2015)"},{"key":"6_CR16","unstructured":"Goel, V., et al.: PAIR-diffusion: object-level image editing with structure-and-appearance paired diffusion models. arXiv:2303.17546 (2023)"},{"key":"6_CR17","unstructured":"Gong, R., Danelljan, M., Sun, H., Mangas, J.D., Gool, L.V.: Prompting diffusion representations for cross-domain semantic segmentation. arXiv:2307.02138 (2023)"},{"key":"6_CR18","unstructured":"Goodfellow, I.J., et al.: Generative adversarial networks. arXiv:1406.2661 (2014)"},{"key":"6_CR19","doi-asserted-by":"crossref","unstructured":"Ham, C., Hays, J., Lu, J., Singh, K.K., Zhang, Z., Hinz, T.: Modulating pretrained diffusion models for multimodal image synthesis. arXiv:2302.12764 (2023)","DOI":"10.1145\/3588432.3591549"},{"key":"6_CR20","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"6_CR21","unstructured":"He, R., et al.: Is synthetic data from generative models ready for image recognition? arXiv:2210.07574 (2023)"},{"key":"6_CR22","unstructured":"Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. In: Advances in Neural Information Processing Systems (2020)"},{"key":"6_CR23","unstructured":"Ho, J., Salimans, T.: Classifier-free diffusion guidance. arXiv:2207.12598 (2022)"},{"key":"6_CR24","doi-asserted-by":"crossref","unstructured":"Hoyer, L., Dai, D., Van Gool, L.: DAFormer: improving network architectures and training strategies for domain-adaptive semantic segmentation. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (2022)","DOI":"10.1109\/CVPR52688.2022.00969"},{"key":"6_CR25","doi-asserted-by":"crossref","unstructured":"Hoyer, L., Dai, D., Van Gool, L.: HRDA: context-aware high-resolution domain-adaptive semantic segmentation. arXiv:2204.13132 (2022)","DOI":"10.1007\/978-3-031-20056-4_22"},{"issue":"1","key":"6_CR26","doi-asserted-by":"publisher","first-page":"220","DOI":"10.1109\/TPAMI.2023.3320613","volume":"46","author":"L Hoyer","year":"2024","unstructured":"Hoyer, L., Dai, D., Van Gool, L.: Domain adaptive and generalizable network architectures and training strategies for semantic image segmentation. IEEE TPAMI 46(1), 220\u2013235 (2024)","journal-title":"IEEE TPAMI"},{"key":"6_CR27","doi-asserted-by":"crossref","unstructured":"Huang, J., Guan, D., Xiao, A., Lu, S.: FSDR: frequency space domain randomization for domain generalization. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (2021)","DOI":"10.1109\/CVPR46437.2021.00682"},{"key":"6_CR28","unstructured":"Huang, L., Chen, D., Liu, Y., Shen, Y., Zhao, D., Zhou, J.: Composer: creative and controllable image synthesis with composable conditions. arXiv:2302.09778 (2023)"},{"key":"6_CR29","doi-asserted-by":"crossref","unstructured":"Huang, W., et al.: Style projected clustering for domain generalized semantic segmentation. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (2023)","DOI":"10.1109\/CVPR52729.2023.00299"},{"key":"6_CR30","doi-asserted-by":"crossref","unstructured":"Ke, B., Obukhov, A., Huang, S., Metzger, N., Daudt, R.C., Schindler, K.: Repurposing diffusion-based image generators for monocular depth estimation (2023)","DOI":"10.1109\/CVPR52733.2024.00907"},{"key":"6_CR31","doi-asserted-by":"crossref","unstructured":"Kim, S., Kim, D.H., Kim, H.: Texture learning domain randomization for domain generalized segmentation. arXiv preprint arXiv:2303.11546 (2023)","DOI":"10.1109\/ICCV51070.2023.00069"},{"key":"6_CR32","unstructured":"Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv:1312.6114 (2022)"},{"key":"6_CR33","doi-asserted-by":"crossref","unstructured":"Kondapaneni, N., Marks, M., Knott, M., Guimar\u00e3es, R., Perona, P.: Text-image alignment for diffusion-based perception. arXiv:2310.00031 (2023)","DOI":"10.1109\/CVPR52733.2024.01317"},{"key":"6_CR34","unstructured":"Li, Z., Li, Y., Zhao, P., Song, R., Li, X., Yang, J.: Is synthetic data from diffusion models ready for knowledge distillation? arXiv:2305.12954 (2023)"},{"key":"6_CR35","unstructured":"Li, Z., Zhou, Q., Zhang, X., Zhang, Y., Wang, Y., Xie, W.: Guiding text-to-image diffusion model towards grounded generation. arXiv preprint arXiv:2301.05221 (2023)"},{"key":"6_CR36","doi-asserted-by":"crossref","unstructured":"Lugmayr, A., Danelljan, M., Romero, A., Yu, F., Timofte, R., Van Gool, L.: RePaint: inpainting using denoising diffusion probabilistic models. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (2022)","DOI":"10.1109\/CVPR52688.2022.01117"},{"key":"6_CR37","unstructured":"Meng, C., Song, Y., Song, J., Wu, J., Zhu, J.Y., Ermon, S.: SDEdit: image synthesis and editing with stochastic differential equations. arXiv preprint arXiv:2108.01073 (2021)"},{"key":"6_CR38","doi-asserted-by":"crossref","unstructured":"Mou, C., et al.: T2I-adapter: learning adapters to dig out more controllable ability for text-to-image diffusion models. arXiv:2302.08453 (2023)","DOI":"10.1609\/aaai.v38i5.28226"},{"key":"6_CR39","doi-asserted-by":"crossref","unstructured":"Neuhold, G., Ollmann, T., Rota\u00a0Bulo, S., Kontschieder, P.: The mapillary vistas dataset for semantic understanding of street scenes. In: IEEE International Conference on Computer Vision (2017)","DOI":"10.1109\/ICCV.2017.534"},{"key":"6_CR40","doi-asserted-by":"crossref","unstructured":"Pan, X., Luo, P., Shi, J., Tang, X.: Two at once: enhancing learning and generalization capacities via IBN-Net. In: European Conference on Computer Vision (2018)","DOI":"10.1007\/978-3-030-01225-0_29"},{"key":"6_CR41","doi-asserted-by":"crossref","unstructured":"Peng, D., Hu, P., Ke, Q., Liu, J.: Diffusion-based image translation with label guidance for domain adaptive semantic segmentation. In: IEEE\/CVF International Conference on Computer Vision (2023)","DOI":"10.1109\/ICCV51070.2023.00081"},{"key":"6_CR42","doi-asserted-by":"crossref","unstructured":"Peng, D., Lei, Y., Hayat, M., Guo, Y., Li, W.: Semantic-aware domain generalized segmentation. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (2022)","DOI":"10.1109\/CVPR52688.2022.00262"},{"key":"6_CR43","doi-asserted-by":"crossref","unstructured":"Peng, D., Lei, Y., Liu, L., Zhang, P., Liu, J.: Global and local texture randomization for synthetic-to-real semantic segmentation. IEEE Trans. Image Process. 30 (2021)","DOI":"10.1109\/TIP.2021.3096334"},{"key":"6_CR44","unstructured":"Radford, A., et al.: Learning transferable visual models from natural language supervision. arXiv:2103.00020 (2021)"},{"key":"6_CR45","unstructured":"Rezende, D.J., Mohamed, S.: Variational inference with normalizing flows. arXiv:1505.05770 (2016)"},{"key":"6_CR46","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1007\/978-3-319-46475-6_7","volume-title":"Computer Vision \u2013 ECCV 2016","author":"SR Richter","year":"2016","unstructured":"Richter, S.R., Vineet, V., Roth, S., Koltun, V.: Playing for data: ground truth from computer games. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906, pp. 102\u2013118. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46475-6_7"},{"key":"6_CR47","doi-asserted-by":"crossref","unstructured":"Roberts, M., et al.: Hypersim: a photorealistic synthetic dataset for holistic indoor scene understanding. In: International Conference on Computer Vision (ICCV) (2021)","DOI":"10.1109\/ICCV48922.2021.01073"},{"key":"6_CR48","doi-asserted-by":"crossref","unstructured":"Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (2022)","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"6_CR49","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. arXiv:1505.04597 (2015)","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"6_CR50","doi-asserted-by":"crossref","unstructured":"Ruiz, N., Li, Y., Jampani, V., Pritch, Y., Rubinstein, M., Aberman, K.: DreamBooth: fine tuning text-to-image diffusion models for subject-driven generation. arXiv:2208.12242 (2023)","DOI":"10.1109\/CVPR52729.2023.02155"},{"key":"6_CR51","doi-asserted-by":"crossref","unstructured":"Saha, S., Hoyer, L., Obukhov, A., Dai, D., Van\u00a0Gool, L.: EDAPS: enhanced domain-adaptive panoptic segmentation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV) (2023)","DOI":"10.1109\/ICCV51070.2023.01762"},{"key":"6_CR52","doi-asserted-by":"crossref","unstructured":"Sakaridis, C., Dai, D., Van Gool, L.: Guided curriculum model adaptation and uncertainty-aware evaluation for semantic nighttime image segmentation. In: IEEE International Conference on Computer Vision (2019)","DOI":"10.1109\/ICCV.2019.00747"},{"key":"6_CR53","doi-asserted-by":"crossref","unstructured":"Sakaridis, C., Dai, D., Van\u00a0Gool, L.: ACDC: the adverse conditions dataset with correspondences for semantic driving scene understanding. In: IEEE\/CVF International Conference on Computer Vision (2021)","DOI":"10.1109\/ICCV48922.2021.01059"},{"key":"6_CR54","doi-asserted-by":"crossref","unstructured":"Sariyildiz, M.B., Alahari, K., Larlus, D., Kalantidis, Y.: Fake it till you make it: learning transferable representations from synthetic imagenet clones. arXiv:2212.08420 (2023)","DOI":"10.1109\/CVPR52729.2023.00774"},{"key":"6_CR55","unstructured":"Schuhmann, C., et\u00a0al.: LAION-5B: an open large-scale dataset for training next generation image-text models. In: Advances in Neural Information Processing Systems (2022)"},{"key":"6_CR56","unstructured":"Song, J., Meng, C., Ermon, S.: Denoising diffusion implicit models. arXiv:2010.02502 (2022)"},{"key":"6_CR57","unstructured":"Song, Y., Sohl-Dickstein, J., Kingma, D.P., Kumar, A., Ermon, S., Poole, B.: Score-based generative modeling through stochastic differential equations. arXiv:2011.13456 (2021)"},{"key":"6_CR58","unstructured":"Trabucco, B., Doherty, K., Gurinas, M., Salakhutdinov, R.: Effective data augmentation with diffusion models. arXiv:2302.07944 (2023)"},{"key":"6_CR59","unstructured":"Wang, T., Kanakis, M., Schindler, K., Van Gool, L., Obukhov, A.: Breathing new life into 3D assets with generative repainting. In: British Machine Vision Conference (2023)"},{"key":"6_CR60","unstructured":"Wu, W., et al.: DatasetDM: synthesizing data with perception annotations using diffusion models. arXiv:2308.06160 (2023)"},{"key":"6_CR61","doi-asserted-by":"crossref","unstructured":"Wu, W., Zhao, Y., Shou, M.Z., Zhou, H., Shen, C.: DiffuMask: synthesizing images with pixel-level annotations for semantic segmentation using diffusion models. arXiv preprint arXiv:2303.11681 (2023)","DOI":"10.1109\/ICCV51070.2023.00117"},{"key":"6_CR62","doi-asserted-by":"crossref","unstructured":"Wu, Z., et al.: Synthetic data supervised salient object detection. In: ACM International Conference on Multimedia (2022)","DOI":"10.1145\/3503161.3547930"},{"key":"6_CR63","unstructured":"Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: SegFormer: simple and efficient design for semantic segmentation with transformers. In: Advances in Neural Information Processing Systems (2021)"},{"key":"6_CR64","doi-asserted-by":"crossref","unstructured":"Xu, J., Liu, S., Vahdat, A., Byeon, W., Wang, X., Mello, S.D.: Open-vocabulary panoptic segmentation with text-to-image diffusion models. arXiv:2303.04803 (2023)","DOI":"10.1109\/CVPR52729.2023.00289"},{"key":"6_CR65","doi-asserted-by":"crossref","unstructured":"Xue, H., Huang, Z., Sun, Q., Song, L., Zhang, W.: Freestyle layout-to-image synthesis. arXiv:2303.14412 (2023)","DOI":"10.1109\/CVPR52729.2023.01370"},{"key":"6_CR66","unstructured":"Yang, L., Xu, X., Kang, B., Shi, Y., Zhao, H.: FreeMask: synthetic images with dense annotations make stronger segmentation models. arXiv preprint arXiv:2310.15160 (2023)"},{"key":"6_CR67","doi-asserted-by":"crossref","unstructured":"Yu, F., et al.: BDD100K: a diverse driving dataset for heterogeneous multitask learning. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (2020)","DOI":"10.1109\/CVPR42600.2020.00271"},{"key":"6_CR68","doi-asserted-by":"crossref","unstructured":"Yu, J., Wang, Y., Zhao, C., Ghanem, B., Zhang, J.: FreeDoM: training-free energy-guided conditional diffusion model. arXiv:2303.09833 (2023)","DOI":"10.1109\/ICCV51070.2023.02118"},{"key":"6_CR69","doi-asserted-by":"crossref","unstructured":"Yue, X., Zhang, Y., Zhao, S., Sangiovanni-Vincentelli, A., Keutzer, K., Gong, B.: Domain randomization and pyramid consistency: simulation-to-real generalization without accessing target domain data. In: IEEE\/CVF International Conference on Computer Vision (2019)","DOI":"10.1109\/ICCV.2019.00219"},{"key":"6_CR70","doi-asserted-by":"crossref","unstructured":"Zhang, L., Agrawala, M.: Adding conditional control to text-to-image diffusion models. arXiv preprint arXiv:2302.05543 (2023)","DOI":"10.1109\/ICCV51070.2023.00355"},{"key":"6_CR71","doi-asserted-by":"crossref","unstructured":"Zhang, M., et al.: DiffusionEngine: diffusion model is scalable data engine for object detection. arXiv:2309.03893 (2023)","DOI":"10.2139\/ssrn.4866102"},{"key":"6_CR72","doi-asserted-by":"crossref","unstructured":"Zhang, Y., et al.: DatasetGAN: efficient labeled data factory with minimal human effort. arXiv:2104.06490 (2021)","DOI":"10.1109\/CVPR46437.2021.01001"},{"key":"6_CR73","unstructured":"Zhao, S., et al.: Uni-ControlNet: all-in-one control to text-to-image diffusion models. arXiv:2305.16322 (2023)"},{"key":"6_CR74","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"535","DOI":"10.1007\/978-3-031-19815-1_31","volume-title":"Computer Vision - ECCV 2022","author":"Y Zhao","year":"2022","unstructured":"Zhao, Y., Zhong, Z., Zhao, N., Sebe, N., Lee, G.H.: Style-hallucinated dual consistency learning for domain generalized semantic segmentation. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, vol. 13688, pp. 535\u2013552. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-19815-1_31"},{"key":"6_CR75","unstructured":"Zhong, Z., Zhao, Y., Lee, G.H., Sebe, N.: Adversarial style augmentation for domain generalized urban-scene segmentation. In: Advances in Neural Information Processing Systems (2022)"}],"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\/978-3-031-72933-1_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,2]],"date-time":"2024-10-02T12:31:58Z","timestamp":1727872318000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-72933-1_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,3]]},"ISBN":["9783031729324","9783031729331"],"references-count":75,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-72933-1_6","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,3]]},"assertion":[{"value":"3 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"}}]}}