{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T10:24:48Z","timestamp":1789122288732,"version":"build-2803163510"},"publisher-location":"Cham","reference-count":66,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032374349","type":"print"},{"value":"9783032374356","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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":[[2026]]},"DOI":"10.1007\/978-3-032-37435-6_19","type":"book-chapter","created":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T09:21:59Z","timestamp":1789118519000},"page":"345-363","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Moebius: 0.2B Lightweight Image Inpainting Framework with\u00a010B-Level Performance"],"prefix":"10.1007","author":[{"given":"Kangsheng","family":"Duan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-7682-6995","authenticated-orcid":false,"given":"Ziyang","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4582-7488","authenticated-orcid":false,"given":"Wenyu","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-4975-2103","authenticated-orcid":false,"given":"Xiaohu","family":"Ruan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-5908-847X","authenticated-orcid":false,"given":"Xiaoxin","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6732-7823","authenticated-orcid":false,"given":"Xinggang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,9,12]]},"reference":[{"key":"19_CR1","unstructured":"Bello, I.: Lambdanetworks: modeling long-range interactions without attention. arXiv preprint arXiv:2102.08602 (2021)"},{"key":"19_CR2","doi-asserted-by":"publisher","unstructured":"Bertalmio, M., Sapiro, G., Caselles, V., Ballester, C.: Image inpainting. In: Proceedings of the 27th annual conference on Computer graphics and interactive techniques, pp. 417\u2013424. SIGGRAPH \u201900, ACM Press\/Addison-Wesley Publishing Co., USA (2000). https:\/\/doi.org\/10.1145\/344779.344972","DOI":"10.1145\/344779.344972"},{"key":"19_CR3","doi-asserted-by":"crossref","unstructured":"Cai, H., Li, J., Hu, M., Gan, C., Han, S.: Efficientvit: lightweight multi-scale attention for high-resolution dense prediction. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 17302\u201317313 (2023)","DOI":"10.1109\/ICCV51070.2023.01587"},{"key":"19_CR4","doi-asserted-by":"crossref","unstructured":"Chen, H., Zhao, Y.: Don\u2019t look into the dark: latent codes for pluralistic image inpainting. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7591\u20137600 (June 2024)","DOI":"10.1109\/CVPR52733.2024.00725"},{"key":"19_CR5","doi-asserted-by":"crossref","unstructured":"Chen, P., Liu, S., Zhao, H., Jia, J.: Distilling knowledge via knowledge review (2021). https:\/\/arxiv.org\/abs\/2104.09044","DOI":"10.1109\/CVPR46437.2021.00497"},{"key":"19_CR6","unstructured":"Dao, T.: FlashAttention-2: faster attention with better parallelism and work partitioning. In: International Conference on Learning Representations (ICLR) (2024)"},{"key":"19_CR7","doi-asserted-by":"publisher","first-page":"16344","DOI":"10.52202\/068431-1189","volume":"35","author":"T Dao","year":"2022","unstructured":"Dao, T., Fu, D., Ermon, S., Rudra, A., R\u00e9, C.: Flashattention: fast and memory-efficient exact attention with io-awareness. Adv. Neural. Inf. Process. Syst. 35, 16344\u201316359 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"19_CR8","unstructured":"Esser, P., et al.: Scaling rectified flow transformers for high-resolution image synthesis. In: Forty-first International Conference on Machine Learning (2024)"},{"key":"19_CR9","doi-asserted-by":"crossref","unstructured":"Esser, P., Rombach, R., Ommer, B.: Taming transformers for high-resolution image synthesis (2021). https:\/\/arxiv.org\/abs\/2012.09841","DOI":"10.1109\/CVPR46437.2021.01268"},{"key":"19_CR10","unstructured":"Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: GANs trained by a two time-scale update rule converge to a local nash equilibrium. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems. vol.\u00a030. Curran Associates, Inc. (2017). https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2017\/file\/8a1d694707eb0fefe65871369074926d-Paper.pdf"},{"key":"19_CR11","unstructured":"Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network (2015). https:\/\/arxiv.org\/abs\/1503.02531"},{"key":"19_CR12","unstructured":"Inc, M.P.: Papers with code (2025). https:\/\/web.archive.org\/web\/20250621114958\/https:\/\/paperswithcode.com\/"},{"key":"19_CR13","unstructured":"Jordan, K., et al.: Muon: an optimizer for hidden layers in neural networks (2024). https:\/\/kellerjordan.github.io\/posts\/muon\/"},{"key":"19_CR14","doi-asserted-by":"crossref","unstructured":"Ju, X., Liu, X., Wang, X., Bian, Y., Shan, Y., Xu, Q.: Brushnet: a plug-and-play image inpainting model with decomposed dual-branch diffusion (2024)","DOI":"10.1007\/978-3-031-72661-3_9"},{"key":"19_CR15","doi-asserted-by":"crossref","unstructured":"Kang, M., et al.: Distilling Diffusion Models into Conditional GANs. In: European Conference on Computer Vision (ECCV) (2024)","DOI":"10.1007\/978-3-031-73390-1_25"},{"issue":"12","key":"19_CR16","doi-asserted-by":"publisher","first-page":"4217","DOI":"10.1109\/TPAMI.2020.2970919","volume":"43","author":"T Karras","year":"2021","unstructured":"Karras, T., Laine, S., Aila, T.: A style-based generator architecture for generative adversarial networks. IEEE Trans. Pattern Anal. Mach. Intell. 43(12), 4217\u20134228 (2021). https:\/\/doi.org\/10.1109\/TPAMI.2020.2970919","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"19_CR17","unstructured":"Labs, B.F.: Flux (2024). https:\/\/github.com\/black-forest-labs\/flux"},{"key":"19_CR18","doi-asserted-by":"publisher","unstructured":"Li, J., Zhao, R., Huang, J.T., Gong, Y.: Learning small-size DNN with output-distribution-based criteria. In: Interspeech 2014, pp. 1910\u20131914 (2014). https:\/\/doi.org\/10.21437\/Interspeech.2014-432","DOI":"10.21437\/Interspeech.2014-432"},{"key":"19_CR19","doi-asserted-by":"crossref","unstructured":"Li, R., Yang, T., Guo, S., Zhang, L.: Rorem: training a robust object remover with human-in-the-loop. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 14024\u201314035 (2025)","DOI":"10.1109\/CVPR52734.2025.01309"},{"key":"19_CR20","doi-asserted-by":"crossref","unstructured":"Li, W., Lin, Z., Zhou, K., Qi, L., Wang, Y., Jia, J.: Mat: mask-aware transformer for large hole image inpainting. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (2022)","DOI":"10.1109\/CVPR52688.2022.01049"},{"key":"19_CR21","doi-asserted-by":"crossref","unstructured":"Li, Y., et al.: Rethinking vision transformers for mobilenet size and speed. In: Proceedings of the IEEE International Conference on Computer Vision (2023)","DOI":"10.1109\/ICCV51070.2023.01549"},{"key":"19_CR22","unstructured":"Lu, C., Song, Y.: Simplifying, stabilizing and scaling continuous-time consistency models (2025). https:\/\/arxiv.org\/abs\/2410.11081"},{"key":"19_CR23","unstructured":"Manukyan, H., Sargsyan, A., Atanyan, B., Wang, Z., Navasardyan, S., Shi, H.: Hd-painter: high-resolution and prompt-faithful text-guided image inpainting with diffusion models. In: The Thirteenth International Conference on Learning Representations (2023)"},{"key":"19_CR24","doi-asserted-by":"crossref","unstructured":"Nazeri, K., Ng, E., Joseph, T., Qureshi, F., Ebrahimi, M.: Edgeconnect: structure guided image inpainting using edge prediction. In: The IEEE International Conference on Computer Vision (ICCV) Workshops (2019)","DOI":"10.1109\/ICCVW.2019.00408"},{"key":"19_CR25","doi-asserted-by":"crossref","unstructured":"Peebles, W., Xie, S.: Scalable diffusion models with transformers. arXiv preprint arXiv:2212.09748 (2022)","DOI":"10.1109\/ICCV51070.2023.00387"},{"key":"19_CR26","unstructured":"Podell, D., et al.: Sdxl: improving latent diffusion models for high-resolution image synthesis (2023). https:\/\/arxiv.org\/abs\/2307.01952"},{"key":"19_CR27","unstructured":"Qin, D., et al.: Mobilenetv4-universal models for the mobile ecosystem. arXiv preprint arXiv:2404.10518 (2024)"},{"key":"19_CR28","doi-asserted-by":"crossref","unstructured":"Reda, F., Kontkanen, J., Tabellion, E., Sun, D., Pantofaru, C., Curless, B.: Film: Frame interpolation for large motion. In: European Conference on Computer Vision (ECCV) (2022)","DOI":"10.1007\/978-3-031-20071-7_15"},{"key":"19_CR29","doi-asserted-by":"crossref","unstructured":"Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 10684\u201310695 (2022)","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"19_CR30","unstructured":"Romero, A., Ballas, N., Kahou, S.E., Chassang, A., Gatta, C., Bengio, Y.: Fitnets: hints for thin deep nets (2015). https:\/\/arxiv.org\/abs\/1412.6550"},{"key":"19_CR31","unstructured":"Salimans, T., Ho, J.: Progressive distillation for fast sampling of diffusion models (2022). https:\/\/arxiv.org\/abs\/2202.00512"},{"key":"19_CR32","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A.G., Zhu, M., Zhmoginov, A., Chen, L.C.: Mobilenetv2: inverted residuals and linear bottlenecks. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4510\u20134520 (2018)","DOI":"10.1109\/CVPR.2018.00474"},{"key":"19_CR33","doi-asserted-by":"crossref","unstructured":"Sargsyan, A., Navasardyan, S., Xu, X., Shi, H.: Mi-GAN: a simple baseline for image inpainting on mobile devices. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 7335\u20137345 (October 2023)","DOI":"10.1109\/ICCV51070.2023.00674"},{"key":"19_CR34","unstructured":"Shazeer, N.: Glu variants improve transformer (2020). https:\/\/arxiv.org\/abs\/2002.05202"},{"key":"19_CR35","unstructured":"Simonyan, K., Zisserman, A.: very deep convolutional networks for large-scale image recognition (2015). https:\/\/arxiv.org\/abs\/1409.1556"},{"key":"19_CR36","unstructured":"Song, Y., Dhariwal, P., Chen, M., Sutskever, I.: Consistency models (2023). https:\/\/arxiv.org\/abs\/2303.01469"},{"key":"19_CR37","doi-asserted-by":"crossref","unstructured":"Suvorov, R., et al.: Resolution-robust large mask inpainting with Fourier convolutions. arXiv preprint arXiv:2109.07161 (2021)","DOI":"10.1109\/WACV51458.2022.00323"},{"key":"19_CR38","unstructured":"Tan, M., Le, Q.: Efficientnet: Rethinking model scaling for convolutional neural networks. In: International Conference on Machine Learning, pp. 6105\u20136114. PMLR (2019)"},{"key":"19_CR39","unstructured":"Team, K., et al.: Kimi k2: open agentic intelligence (2025). https:\/\/arxiv.org\/abs\/2507.20534"},{"key":"19_CR40","unstructured":"Tero\u00a0Karras, Timo\u00a0Aila, S.L., Lehtinen, J.: Progressive growing of GANs for improved quality, stability and variation. In: International Conference on Learning Representations (ICLR) (2018)"},{"key":"19_CR41","unstructured":"Tian, Y., Krishnan, D., Isola, P.: Contrastive representation distillation (2022). https:\/\/arxiv.org\/abs\/1910.10699"},{"key":"19_CR42","unstructured":"Vaswani, A., et al.: Attention is all you need. Adv. Neural Inf. Process. Syst. 30 (2017)"},{"key":"19_CR43","doi-asserted-by":"publisher","unstructured":"Wan, Z., Zhang, J., Chen, D., Liao, J.: High-fidelity pluralistic image completion with transformers. In: 2021 IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 4672\u20134681 (2021). https:\/\/doi.org\/10.1109\/ICCV48922.2021.00465","DOI":"10.1109\/ICCV48922.2021.00465"},{"issue":"6","key":"19_CR44","doi-asserted-by":"publisher","first-page":"6305","DOI":"10.1109\/TKDE.2022.3171571","volume":"35","author":"C Wang","year":"2023","unstructured":"Wang, C., Chen, D., Mei, J.P., Zhang, Y., Feng, Y., Chen, C.: Semckd: semantic calibration for cross-layer knowledge distillation. IEEE Trans. Knowl. Data Eng. 35(6), 6305\u20136319 (2023). https:\/\/doi.org\/10.1109\/TKDE.2022.3171571","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"19_CR45","unstructured":"Wang, Y., Yu, J., Zhang, J.: Zero-shot image restoration using denoising diffusion null-space model. arXiv preprint arXiv:2212.00490 (2022)"},{"key":"19_CR46","unstructured":"Xie, E., et al.: Sana: efficient high-resolution image synthesis with linear diffusion transformer (2024). https:\/\/arxiv.org\/abs\/2410.10629"},{"key":"19_CR47","unstructured":"Xie, E., et\u00a0al.: Sana 1.5: efficient scaling of training-time and inference-time compute in linear diffusion transformer (2025). https:\/\/arxiv.org\/abs\/2501.18427"},{"key":"19_CR48","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: Neural Information Processing Systems (NeurIPS) (2021)"},{"key":"19_CR49","unstructured":"Xu, Z., et al.: Pixelhacker: image inpainting with structural and semantic consistency (2025). https:\/\/arxiv.org\/abs\/2504.20438"},{"key":"19_CR50","doi-asserted-by":"crossref","unstructured":"Xu, Z., Zhao, H., Cui, Z., Liu, W., Zheng, C., Wang, X.: Most-DSA: modeling motion and structural interactions for direct multi-frame interpolation in dsa images. In: European Conference on Artificial Intelligence, pp. 537\u2013544 (2024). https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA240531","DOI":"10.3233\/FAIA240531"},{"key":"19_CR51","doi-asserted-by":"crossref","unstructured":"Xu, Z., Zhao, H., Liu, W., Wang, X.: Garamost: Parallel multi-granularity motion and structural modeling for efficient multi-frame interpolation in DSA images. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 39, pp. 28530\u201328538 (2025)","DOI":"10.1609\/aaai.v39i27.35076"},{"key":"19_CR52","unstructured":"Xue, F., et al.: Openmoe: an early effort on open mixture-of-experts language models. arXiv preprint arXiv:2402.01739 (2024)"},{"key":"19_CR53","unstructured":"Yang, S., Wang, B., Shen, Y., Panda, R., Kim, Y.: Gated linear attention transformers with hardware-efficient training. In: Proceedings of ICML (2024)"},{"key":"19_CR54","unstructured":"Yang, S., Zhang, Y.: Fla: A triton-based library for hardware-efficient implementations of linear attention mechanism (2024). https:\/\/github.com\/fla-org\/flash-linear-attention"},{"key":"19_CR55","doi-asserted-by":"crossref","unstructured":"Yao, J., Yang, B., Wang, X.: Reconstruction vs. generation: Taming optimization dilemma in latent diffusion models. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (2025)","DOI":"10.1109\/CVPR52734.2025.01464"},{"key":"19_CR56","doi-asserted-by":"crossref","unstructured":"Yi, Z., Tang, Q., Azizi, S., Jang, D., Xu, Z.: Contextual residual aggregation for ultra high-resolution image inpainting. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7508\u20137517 (2020)","DOI":"10.1109\/CVPR42600.2020.00753"},{"key":"19_CR57","doi-asserted-by":"publisher","unstructured":"Yim, J., Joo, D., Bae, J., Kim, J.: A gift from knowledge distillation: fast optimization, network minimization and transfer learning. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7130\u20137138 (2017). https:\/\/doi.org\/10.1109\/CVPR.2017.754","DOI":"10.1109\/CVPR.2017.754"},{"key":"19_CR58","doi-asserted-by":"publisher","unstructured":"Yu, J., Lin, Z., Yang, J., Shen, X., Lu, X., Huang, T.: Free-form image inpainting with gated convolution. In: 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 4470\u20134479 (2019).https:\/\/doi.org\/10.1109\/ICCV.2019.00457","DOI":"10.1109\/ICCV.2019.00457"},{"issue":"7","key":"19_CR59","doi-asserted-by":"publisher","first-page":"3266","DOI":"10.1109\/TVCG.2022.3156949","volume":"29","author":"Y Zeng","year":"2023","unstructured":"Zeng, Y., Fu, J., Chao, H., Guo, B.: Aggregated contextual transformations for high-resolution image inpainting. IEEE Trans. Visual Comput. Graphics 29(7), 3266\u20133280 (2023). https:\/\/doi.org\/10.1109\/TVCG.2022.3156949","journal-title":"IEEE Trans. Visual Comput. Graphics"},{"key":"19_CR60","doi-asserted-by":"crossref","unstructured":"Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00068"},{"key":"19_CR61","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zhou, X., Lin, M., Sun, J.: Shufflenet: an extremely efficient convolutional neural network for mobile devices. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6848\u20136856 (2018)","DOI":"10.1109\/CVPR.2018.00716"},{"key":"19_CR62","unstructured":"Zhao, S., et al.: Large scale image completion via co-modulated generative adversarial networks. In: International Conference on Learning Representations (ICLR) (2021)"},{"key":"19_CR63","doi-asserted-by":"crossref","unstructured":"Zhou, B., Lapedriza, A., Khosla, A., Oliva, A., Torralba, A.: Places: a 10 million image database for scene recognition. IEEE Trans. Pattern Anal. Mach. Intell. (2017)","DOI":"10.1167\/17.10.296"},{"key":"19_CR64","doi-asserted-by":"crossref","unstructured":"Zhu, L., et al.: Dig: scalable and efficient diffusion models with gated linear attention. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7664\u20137674 (2025)","DOI":"10.1109\/CVPR52734.2025.00718"},{"key":"19_CR65","doi-asserted-by":"publisher","first-page":"4855","DOI":"10.1109\/TIP.2021.3076310","volume":"30","author":"M Zhu","year":"2021","unstructured":"Zhu, M., et al.: Image inpainting by end-to-end cascaded refinement with mask awareness. IEEE Trans. Image Process. 30, 4855\u20134866 (2021). https:\/\/doi.org\/10.1109\/TIP.2021.3076310","journal-title":"IEEE Trans. Image Process."},{"key":"19_CR66","doi-asserted-by":"crossref","unstructured":"Zhuang, J., Zeng, Y., Liu, W., Yuan, C., Chen, K.: A task is worth one word: learning with task prompts for high-quality versatile image inpainting (2023)","DOI":"10.1007\/978-3-031-73636-0_12"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2026"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-37435-6_19","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T09:22:20Z","timestamp":1789118540000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-37435-6_19"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032374349","9783032374356"],"references-count":66,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-37435-6_19","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"12 September 2026","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":"Malm\u00f6","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Sweden","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 September 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 September 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}