{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T11:06:24Z","timestamp":1789124784302,"version":"build-2803163510"},"publisher-location":"Cham","reference-count":71,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032372604","type":"print"},{"value":"9783032372611","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\/978-3-032-37261-1_9","type":"book-chapter","created":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T10:14:23Z","timestamp":1789121663000},"page":"134-153","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["NoiseTilt: Noise-Tilted Reverse Kernels for\u00a0Diffusion Reward Alignment"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-6514-5575","authenticated-orcid":false,"given":"Jisung","family":"Hwang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-0657-5733","authenticated-orcid":false,"given":"Yunhong","family":"Min","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-4555-0459","authenticated-orcid":false,"given":"Jaihoon","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4201-3793","authenticated-orcid":false,"given":"I-Chao","family":"Shen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7428-9570","authenticated-orcid":false,"given":"Minhyuk","family":"Sung","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,9,12]]},"reference":[{"key":"9_CR1","doi-asserted-by":"crossref","unstructured":"Bansal, A., et al.: Universal guidance for diffusion models. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (2023)","DOI":"10.1109\/CVPRW59228.2023.00091"},{"key":"9_CR2","unstructured":"Ben-Hamu, H., Puny, O., Gat, I., Karrer, B., Singer, U., Lipman, Y.: D-Flow: differentiating through flows for controlled generation. In: International Conference on Machine Learning, pp. 3462\u20133483 (2024)"},{"key":"9_CR3","unstructured":"Black, K., Janner, M., Du, Y., Kostrikov, I., Levine, S.: Training diffusion models with reinforcement learning. In: International Conference on Learning Representations (2024)"},{"key":"9_CR4","unstructured":"Cai, H., et al.: Z-image: an efficient image generation foundation model with single-stream diffusion transformer. arXiv preprint arXiv:2511.22699 (2025)"},{"key":"9_CR5","unstructured":"Cardoso, G., Idrissi, Y.J.E., Corff, S.L., Moulines, E.: Monte Carlo guided diffusion for Bayesian linear inverse problems. In: International Conference on Learning Representations (2024)"},{"key":"9_CR6","doi-asserted-by":"publisher","unstructured":"Chefer, H., Alaluf, Y., Vinker, Y., Wolf, L., Cohen-Or, D.: Attend-and-excite: attention-based semantic guidance for text-to-image diffusion models. ACM Trans. Graph. 42(4), 148:1\u2013148:12 (2023). https:\/\/doi.org\/10.1145\/3592116","DOI":"10.1145\/3592116"},{"key":"9_CR7","unstructured":"Chung, H., Kim, J., Mccann, M.T., Klasky, M.L., Ye, J.C.: Diffusion posterior sampling for general noisy inverse problems. In: International Conference on Learning Representations (2023)"},{"key":"9_CR8","doi-asserted-by":"crossref","unstructured":"Chung, H., Sim, B., Ryu, D., Ye, J.C.: Improving diffusion models for inverse problems using manifold constraints. In: Advances in Neural Information Processing Systems, vol. 35, pp. 25683\u201325696 (2022)","DOI":"10.52202\/068431-1862"},{"key":"9_CR9","unstructured":"Clark, K., Vicol, P., Swersky, K., Fleet, D.: Directly fine-tuning diffusion models on differentiable rewards. In: International Conference on Learning Representations (2024)"},{"key":"9_CR10","doi-asserted-by":"crossref","unstructured":"Daras, G., et al.: Warped diffusion: solving video inverse problems with image diffusion models. In: Advances in Neural Information Processing Systems, vol. 37, pp. 101116\u2013101143 (2024)","DOI":"10.52202\/079017-3207"},{"key":"9_CR11","unstructured":"Dou, Z., Song, Y.: Diffusion posterior sampling for linear inverse problem solving: a filtering perspective. In: International Conference on Learning Representations (2024)"},{"key":"9_CR12","doi-asserted-by":"crossref","unstructured":"Doucet, A., De Freitas, N., Gordon, N.J.: Sequential Monte Carlo Methods in Practice. Springer (2001)","DOI":"10.1007\/978-1-4757-3437-9"},{"key":"9_CR13","doi-asserted-by":"crossref","unstructured":"Eyring, L., Karthik, S., Roth, K., Dosovitskiy, A., Akata, Z.: ReNO: enhancing one-step text-to-image models through reward-based noise optimization. In: Advances in Neural Information Processing Systems, vol. 37, pp. 125487\u2013125519 (2024)","DOI":"10.52202\/079017-3987"},{"key":"9_CR14","doi-asserted-by":"crossref","unstructured":"Fan, Y., et al.: DPOK: reinforcement learning for fine-tuning text-to-image diffusion models. In: Advances in Neural Information Processing Systems, vol.\u00a036, pp. 79858\u201379885 (2023)","DOI":"10.52202\/075280-3497"},{"key":"9_CR15","unstructured":"Gao, L., Schulman, J., Hilton, J.: Scaling laws for reward model overoptimization. In: International Conference on Machine Learning, pp. 10835\u201310866 (2023)"},{"key":"9_CR16","unstructured":"He, Y., et al.: Manifold preserving guided diffusion. In: International Conference on Learning Representations (2024)"},{"key":"9_CR17","unstructured":"Hertz, A., Mokady, R., Tenenbaum, J., Aberman, K., Pritch, Y., Cohen-Or, D.: Prompt-to-prompt image editing with cross attention control. In: International Conference on Learning Representations (2023)"},{"key":"9_CR18","doi-asserted-by":"crossref","unstructured":"Hertz, A., Voynov, A., Fruchter, S., Cohen-Or, D.: Style aligned image generation via shared attention. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4775\u20134785 (2024)","DOI":"10.1109\/CVPR52733.2024.00457"},{"key":"9_CR19","unstructured":"Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. In: Advances in Neural Information Processing Systems, vol. 33, pp. 6840\u20136851 (2020)"},{"issue":"5","key":"9_CR20","doi-asserted-by":"publisher","first-page":"3563","DOI":"10.1109\/TPAMI.2025.3531907","volume":"47","author":"K Huang","year":"2025","unstructured":"Huang, K., Duan, C., Sun, K., Xie, E., Li, Z., Liu, X.: T2I-CompBench++: an enhanced and comprehensive benchmark for compositional text-to-image generation. IEEE Trans. Pattern Anal. Mach. Intell. 47(5), 3563\u20133579 (2025). https:\/\/doi.org\/10.1109\/TPAMI.2025.3531907","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"9_CR21","doi-asserted-by":"crossref","unstructured":"Huang, Z., et al.: VBench: comprehensive benchmark suite for video generative models. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 21807\u201321818 (2024)","DOI":"10.1109\/CVPR52733.2024.02060"},{"key":"9_CR22","doi-asserted-by":"crossref","unstructured":"Hwang, J., Kim, J., Sung, M.: Moment- and power-spectrum-based Gaussianity regularization for text-to-image models. In: Advances in Neural Information Processing Systems, vol. 38, pp. 18235\u201318264 (2025)","DOI":"10.52202\/085713-0614"},{"key":"9_CR23","unstructured":"Hwang, J., Sung, M.: Gradient preconditioning for efficient and reliable reward-guided generation. arXiv preprint arXiv:2602.08646 (2026)"},{"key":"9_CR24","doi-asserted-by":"crossref","unstructured":"Hyv\u00e4rinen, A., Karhunen, J., Oja, E.: Independent Component Analysis. Wiley (2001)","DOI":"10.1002\/0471221317"},{"key":"9_CR25","unstructured":"Jang, S., et al.: Frame guidance: training-free guidance for frame-level control in video diffusion models. In: International Conference on Learning Representations (2026)"},{"issue":"4","key":"9_CR26","doi-asserted-by":"publisher","first-page":"309","DOI":"10.1080\/00031305.2016.1277159","volume":"72","author":"A Kessy","year":"2018","unstructured":"Kessy, A., Lewin, A., Strimmer, K.: Optimal whitening and decorrelation. Am. Stat. 72(4), 309\u2013314 (2018). https:\/\/doi.org\/10.1080\/00031305.2016.1277159","journal-title":"Am. Stat."},{"key":"9_CR27","doi-asserted-by":"crossref","unstructured":"Kim, J., Yoon, T., Hwang, J., Sung, M.: Inference-time scaling for flow models via stochastic generation and rollover budget forcing. In: Advances in Neural Information Processing Systems, vol. 38, pp. 30830\u201330864 (2025)","DOI":"10.52202\/085713-1035"},{"key":"9_CR28","unstructured":"Kim, S., Kim, M., Park, D.: Test-time alignment of diffusion models without reward over-optimization. In: International Conference on Learning Representations (2025)"},{"key":"9_CR29","doi-asserted-by":"crossref","unstructured":"Kirstain, Y., Polyak, A., Singer, U., Matiana, S., Penna, J., Levy, O.: Pick-a-pic: an open dataset of user preferences for text-to-image generation. In: Advances in Neural Information Processing Systems, vol. 36, pp. 36652\u201336663 (2023)","DOI":"10.52202\/075280-1594"},{"key":"9_CR30","doi-asserted-by":"crossref","unstructured":"Korbak, T., Elsahar, H., Kruszewski, G., Dymetmant, M.: On reinforcement learning and distribution matching for fine-tuning language models with no catastrophic forgetting. In: Advances in Neural Information Processing Systems, vol. 35, pp. 16203\u201316220 (2022)","DOI":"10.52202\/068431-1179"},{"key":"9_CR31","doi-asserted-by":"crossref","unstructured":"Kumari, N., Zhang, B., Zhang, R., Shechtman, E., Zhu, J.Y.: Multi-concept customization of text-to-image diffusion. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1931\u20131941 (2023)","DOI":"10.1109\/CVPR52729.2023.00192"},{"key":"9_CR32","unstructured":"Kwon, T., Ye, J.C.: Solving video inverse problems using image diffusion models. In: International Conference on Learning Representations (2025)"},{"key":"9_CR33","unstructured":"Labs, B.F.: FLUX (2024). https:\/\/github.com\/black-forest-labs\/flux. Accessed 26 June 2026 (AoE)"},{"key":"9_CR34","doi-asserted-by":"crossref","unstructured":"Lee, Y., Kim, K., Kim, H., Sung, M.: SyncDiffusion: coherent montage via synchronized joint diffusions. In: Advances in Neural Information Processing Systems, vol.\u00a036, pp. 50648\u201350660 (2023)","DOI":"10.52202\/075280-2203"},{"key":"9_CR35","unstructured":"Li, J., et al.: MixGRPO: unlocking flow-based GRPO efficiency with mixed ODE-SDE. arXiv preprint arXiv:2507.21802 (2025)"},{"key":"9_CR36","doi-asserted-by":"crossref","unstructured":"Li, X., et al.: Derivative-free guidance in continuous and discrete diffusion models with soft value-based decoding. In: Advances in Neural Information Processing Systems, vol. 38, pp. 95507\u201395545 (2025)","DOI":"10.52202\/085713-3194"},{"key":"9_CR37","doi-asserted-by":"crossref","unstructured":"Lin, Z., et al.: Evaluating text-to-visual generation with image-to-text generation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 366\u2013384 (2024)","DOI":"10.1007\/978-3-031-72673-6_20"},{"key":"9_CR38","doi-asserted-by":"crossref","unstructured":"Liu, J., et al.: Flow-GRPO: training flow matching models via online RL. In: Advances in Neural Information Processing Systems, vol.\u00a038, pp. 40783\u201340818 (2025)","DOI":"10.52202\/085713-1362"},{"key":"9_CR39","doi-asserted-by":"crossref","unstructured":"Liu, J., et\u00a0al.: Improving video generation with human feedback. In: Advances in Neural Information Processing Systems, vol.\u00a038, pp. 82155\u201382192 (2025)","DOI":"10.52202\/085713-2750"},{"key":"9_CR40","doi-asserted-by":"crossref","unstructured":"Lu, Y., Liang, Y., Zhu, L., Yang, Y.: FreeLong: training-free long video generation with spectralblend temporal attention. In: Advances in Neural Information Processing Systems, vol. 37, pp. 131434\u2013131455 (2024)","DOI":"10.52202\/079017-4177"},{"key":"9_CR41","unstructured":"Ma, N., et al.: Inference-time scaling for diffusion models beyond scaling denoising steps. arXiv preprint arXiv:2501.09732 (2025)"},{"key":"9_CR42","unstructured":"Ma, X., Wang, Y., Chen, X., Wong, T.T., Chen, C.: Training-free stylized text-to-image generation with fast inference. arXiv preprint arXiv:2505.19063 (2025)"},{"key":"9_CR43","unstructured":"Prabhudesai, M., Mendonca, R., Qin, Z., Fragkiadaki, K., Pathak, D.: Video diffusion alignment via reward gradients. In: International Conference on Learning Representations (2025)"},{"key":"9_CR44","unstructured":"Qiu, H., Chen, Z., Wang, Z., He, Y., Xia, M., Liu, Z.: FreeTraj: tuning-free trajectory control in video diffusion models. arXiv preprint arXiv:2406.16863 (2024)"},{"key":"9_CR45","doi-asserted-by":"crossref","unstructured":"Rafailov, R., Sharma, A., Mitchell, E., Manning, C.D., Ermon, S., Finn, C.: Direct preference optimization: your language model is secretly a reward model. In: Advances in Neural Information Processing Systems, vol. 36, pp. 53728\u201353741 (2023)","DOI":"10.52202\/075280-2338"},{"key":"9_CR46","doi-asserted-by":"crossref","unstructured":"Ramesh, V., Mardani, M.: Test-time scaling of diffusion models via noise trajectory search. arXiv preprint arXiv:2506.03164 (2025)","DOI":"10.52202\/085713-2919"},{"key":"9_CR47","doi-asserted-by":"crossref","unstructured":"Robbins, H.E.: An Empirical Bayes Approach to Statistics. Springer (1992)","DOI":"10.1007\/978-1-4612-0919-5_26"},{"key":"9_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: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 10674\u201310685 (2022)","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"9_CR49","doi-asserted-by":"crossref","unstructured":"Rout, L., Chen, Y., Kumar, A., Caramanis, C., Shakkottai, S., Chu, W.S.: Beyond first-order tweedie: Solving inverse problems using latent diffusion. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9472\u20139481 (2024)","DOI":"10.1109\/CVPR52733.2024.00905"},{"key":"9_CR50","doi-asserted-by":"crossref","unstructured":"Rout, L., et al.: Solving linear inverse problems provably via posterior sampling with latent diffusion models. In: Advances in Neural Information Processing Systems, vol. 36, pp. 49960\u201349990 (2023)","DOI":"10.52202\/075280-2174"},{"key":"9_CR51","doi-asserted-by":"crossref","unstructured":"Rozet, F., Andry, G., Lanusse, F., Louppe, G.: Learning diffusion priors from observations by expectation maximization. In: Advances in Neural Information Processing Systems, vol. 37, pp. 87647\u201387682 (2024)","DOI":"10.52202\/079017-2783"},{"key":"9_CR52","doi-asserted-by":"crossref","unstructured":"Samuel, D., Ben-Ari, R., Darshan, N., Maron, H., Chechik, G.: Norm-guided latent space exploration for text-to-image generation. In: Advances in Neural Information Processing Systems, vol. 36, pp. 57863\u201357875 (2023)","DOI":"10.52202\/075280-2522"},{"key":"9_CR53","unstructured":"Schuhmann, C.: LAION aesthetics (2022). https:\/\/laion.ai\/blog\/laion-aesthetics. Accessed 26 June 2026 (AoE)"},{"key":"9_CR54","unstructured":"Seo, J., et al.: StressDream: steering video world models for robust policy evaluation and improvement. arXiv preprint arXiv:2606.00267 (2026)"},{"key":"9_CR55","unstructured":"Singhal, R., et al.: A general framework for inference-time scaling and steering of diffusion models. arXiv preprint arXiv:2501.06848 (2025)"},{"key":"9_CR56","unstructured":"Song, J., Vahdat, A., Mardani, M., Kautz, J.: Pseudoinverse-guided diffusion models for inverse problems. In: International Conference on Learning Representations (2023)"},{"key":"9_CR57","unstructured":"Song, J., et al.: Loss-guided diffusion models for plug-and-play controllable generation. In: International Conference on Machine Learning, pp. 32483\u201332498 (2023)"},{"key":"9_CR58","unstructured":"Song, Y., Sohl-Dickstein, J., Kingma, D.P., Kumar, A., Ermon, S., Poole, B.: Score-based generative modeling through stochastic differential equations. In: International Conference on Learning Representations (2021)"},{"key":"9_CR59","unstructured":"Stiennon, N., et al.: Learning to summarize from human feedback. In: Advances in Neural Information Processing Systems, vol. 33, pp. 3008\u20133021 (2020)"},{"key":"9_CR60","unstructured":"Tang, Z., Peng, J., Tang, J., Hong, M., Wang, F., Chang, T.H.: Inference-time alignment of diffusion models with direct noise optimization. In: International Conference on Machine Learning, pp. 58905\u201358930 (2025)"},{"key":"9_CR61","unstructured":"Uehara, M., et al.: Fine-tuning of continuous-time diffusion models as entropy-regularized control. In: International Conference on Learning Representations (2025)"},{"key":"9_CR62","doi-asserted-by":"crossref","unstructured":"Uehara, M., et al.: Bridging model-based optimization and generative modeling via conservative fine-tuning of diffusion models. In: Advances in Neural Information Processing Systems, vol. 37, pp. 127511\u2013127535 (2024)","DOI":"10.52202\/079017-4049"},{"key":"9_CR63","doi-asserted-by":"crossref","unstructured":"Wallace, B., et al.: Diffusion model alignment using direct preference optimization. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 8228\u20138238 (2024)","DOI":"10.1109\/CVPR52733.2024.00786"},{"key":"9_CR64","unstructured":"Team, W., A.G.: Wan: open and advanced large-scale video generative models. arXiv preprint arXiv:2503.20314 (2025)"},{"key":"9_CR65","doi-asserted-by":"crossref","unstructured":"Wu, L., Trippe, B., Naesseth, C., Blei, D., Cunningham, J.P.: Practical and asymptotically exact conditional sampling in diffusion models. In: Advances in Neural Information Processing Systems, vol. 36, pp. 31372\u201331403 (2023)","DOI":"10.52202\/075280-1363"},{"key":"9_CR66","unstructured":"Wu, X., et al.: Human preference score v2: a solid benchmark for evaluating human preferences of text-to-image synthesis. In: International Conference on Learning Representations (2024)"},{"key":"9_CR67","doi-asserted-by":"crossref","unstructured":"Xu, J., et al.: ImageReward: learning and evaluating human preferences for text-to-image generation. In: Advances in Neural Information Processing Systems, vol. 36, pp. 15903\u201315935 (2023)","DOI":"10.52202\/075280-0700"},{"key":"9_CR68","doi-asserted-by":"crossref","unstructured":"Ye, H., et al.: TFG: unified training-free guidance for diffusion models. In: Advances in Neural Information Processing Systems, vol. 37, pp. 22370\u201322417 (2024)","DOI":"10.52202\/079017-0704"},{"key":"9_CR69","doi-asserted-by":"crossref","unstructured":"Yoon, T., Min, Y., Yeo, K., Sung, M.: Psi-sampler: initial particle sampling for SMC-based inference-time reward alignment in score models. In: Advances in Neural Information Processing Systems, vol. 38, pp. 104745\u2013104781 (2025)","DOI":"10.52202\/085713-3498"},{"key":"9_CR70","doi-asserted-by":"crossref","unstructured":"Yu, J., Wang, Y., Zhao, C., Ghanem, B., Zhang, J.: FreeDoM: training-free energy-guided conditional diffusion model. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 23174\u201323184 (2023)","DOI":"10.1109\/ICCV51070.2023.02118"},{"key":"9_CR71","unstructured":"Zhang, Y., Wei, Y., Jiang, D., Zhang, X., Zuo, W., Tian, Q.: ControlVideo: training-free controllable text-to-video generation. In: International Conference on Learning Representations (2024)"}],"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\/978-3-032-37261-1_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T10:14:46Z","timestamp":1789121686000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-37261-1_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032372604","9783032372611"],"references-count":71,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-37261-1_9","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"}}]}}