{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T07:04:39Z","timestamp":1790233479287,"version":"4.1.0"},"publisher-location":"Cham","reference-count":76,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032374608","type":"print"},{"value":"9783032374615","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\/978-3-032-37461-5_1","type":"book-chapter","created":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T06:41:25Z","timestamp":1790232085000},"page":"1-20","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":[": Accelerating Agentic Multimodal LLMs via\u00a0Speculative Perception and\u00a0Planning"],"prefix":"10.1007","author":[{"given":"Haoyu","family":"Huang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinfa","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhongwei","family":"Wan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiawu","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rongrong","family":"Ji","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiebo","family":"Luo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,9,25]]},"reference":[{"key":"1_CR1","doi-asserted-by":"publisher","first-page":"23716","DOI":"10.52202\/068431-1723","volume":"35","author":"JB Alayrac","year":"2022","unstructured":"Alayrac, J.B., et al.: Flamingo: a visual language model for few-shot learning. Adv. Neural. Inf. Process. Syst. 35, 23716\u201323736 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"1_CR2","unstructured":"Bai, J., et\u00a0al.: Qwen technical report. arXiv preprint arXiv:2309.16609 (2023)"},{"key":"1_CR3","unstructured":"Bolya, D., Fu, C.Y., Dai, X., Zhang, P., Feichtenhofer, C., Hoffman, J.: Token merging: Your vit but faster. arXiv preprint arXiv:2210.09461 (2022)"},{"key":"1_CR4","unstructured":"Cai, T., et al.: Medusa: simple llm inference acceleration framework with multiple decoding heads. arXiv preprint arXiv:2401.10774 (2024)"},{"key":"1_CR5","unstructured":"Chen, C., Borgeaud, S., Irving, G., Lespiau, J.B., Sifre, L., Jumper, J.: Accelerating large language model decoding with speculative sampling. arXiv preprint arXiv:2302.01318 (2023)"},{"key":"1_CR6","unstructured":"Chen, W., et al.: Wavelet-based frame selection by detecting semantic boundary for long video understanding (2026). https:\/\/arxiv.org\/abs\/2603.00512"},{"key":"1_CR7","unstructured":"Chen, Y., Pan, X., Li, Y., Ding, B., Zhou, J.: Ee-llm: Large-scale training and inference of early-exit large language models with 3d parallelism. arXiv preprint arXiv:2312.04916 (2023)"},{"key":"1_CR8","unstructured":"Chng, Y.X., et al.: Sensenova-mars: empowering multimodal agentic reasoning and search via reinforcement learning. arXiv preprint arXiv:2512.24330 (2025)"},{"key":"1_CR9","doi-asserted-by":"publisher","first-page":"49250","DOI":"10.52202\/075280-2142","volume":"36","author":"W Dai","year":"2023","unstructured":"Dai, W., et al.: Instructblip: towards general-purpose vision-language models with instruction tuning. Adv. Neural. Inf. Process. Syst. 36, 49250\u201349267 (2023)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"1_CR10","unstructured":"Doshi, R.: Introducing Agentic Vision in Gemini 3 Flash (2026). https:\/\/blog.google\/innovation-and-ai\/technology\/developers-tools\/agentic-vision-gemini-3-flash\/. Accessed 24 Feb 2026"},{"key":"1_CR11","doi-asserted-by":"crossref","unstructured":"Endo, M., Wang, X., Yeung-Levy, S.: Feather the throttle: revisiting visual token pruning for vision-language model acceleration. In: ICCV (2025)","DOI":"10.1109\/ICCV51701.2025.02119"},{"key":"1_CR12","doi-asserted-by":"crossref","unstructured":"Fan, S., et al.: Not all layers of llms are necessary during inference. arXiv preprint arXiv:2403.02181 (2024)","DOI":"10.24963\/ijcai.2025\/566"},{"key":"1_CR13","doi-asserted-by":"crossref","unstructured":"Fu, T., et al.: Framefusion: combining similarity and importance for video token reduction on large vision language models. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 22654\u201322663 (2025)","DOI":"10.1109\/ICCV51701.2025.02103"},{"key":"1_CR14","unstructured":"Fu, Y., Wang, X., Tian, Y., Zhao, J.: Deep think with confidence. arXiv preprint arXiv:2508.15260 (2025)"},{"key":"1_CR15","unstructured":"Guan, Y., et al.: Dynamic speculative agent planning. arXiv preprint arXiv:2509.01920 (2025)"},{"key":"1_CR16","unstructured":"Guo, Z., Hong, M., Zhang, F., Jia, K., Jin, T.: Thinking with programming vision: towards a unified view for thinking with images. arXiv preprint arXiv:2512.03746 (2025)"},{"key":"1_CR17","unstructured":"He, Y., et al.: Zipvl: efficient large vision-language models with dynamic token sparsification (2024). https:\/\/arxiv.org\/abs\/2410.08584"},{"key":"1_CR18","unstructured":"Hong, J., Zhao, C., Zhu, C., Lu, W., Xu, G., Yu, X.: Deepeyesv2: toward agentic multimodal model. arXiv preprint arXiv:2511.05271 (2025)"},{"key":"1_CR19","unstructured":"Hou, X., et al.: Codev: code with images for faithful visual reasoning via tool-aware policy optimization (2026). https:\/\/arxiv.org\/abs\/2511.19661"},{"key":"1_CR20","unstructured":"Hu, P., et al.: Thinking with drafts: speculative temporal reasoning for efficient long video understanding. ArXiv arxiv:2512.00805 (2025). https:\/\/api.semanticscholar.org\/CorpusID:283449335"},{"key":"1_CR21","unstructured":"Huang, C., Zheng, T., Huang, L., Li, J., Liu, H., Huang, J.: Relayllm: efficient reasoning via collaborative decoding. ArXiv arxiv:2601.05167 (2026). https:\/\/api.semanticscholar.org\/CorpusID:284544142"},{"key":"1_CR22","doi-asserted-by":"crossref","unstructured":"Kim, M., Gao, S., Hsu, Y.C., Shen, Y., Jin, H.: Token fusion: bridging the gap between token pruning and token merging. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 1383\u20131392 (2024)","DOI":"10.1109\/WACV57701.2024.00141"},{"key":"1_CR23","unstructured":"Kumar, A., Nag, S., Clemons, J., John, L., Das, P.: Helios: adaptive model and early-exit selection for efficient llm inference serving. arXiv preprint arXiv:2504.10724 (2025)"},{"key":"1_CR24","doi-asserted-by":"crossref","unstructured":"Kwon, W., et al.: Efficient memory management for large language model serving with pagedattention. In: Proceedings of the 29th Symposium on Operating Systems Principles, pp. 611\u2013626 (2023)","DOI":"10.1145\/3600006.3613165"},{"key":"1_CR25","unstructured":"Lai, X., Li, J., Li, W., Liu, T., Li, T., Zhao, H.: Mini-o3: scaling up reasoning patterns and interaction turns for visual search. ArXiv arxiv:2509.07969 (2025). https:\/\/api.semanticscholar.org\/CorpusID:281217747"},{"key":"1_CR26","unstructured":"Leviathan, Y., Kalman, M., Matias, Y.: Fast inference from transformers via speculative decoding. In: International Conference on Machine Learning, pp. 19274\u201319286. PMLR (2023)"},{"key":"1_CR27","unstructured":"Li, J., Li, D., Savarese, S., Hoi, S.: Blip-2: bootstrapping language-image pre-training with frozen image encoders and large language models. In: International Conference on Machine Learning, pp. 19730\u201319742. PMLR (2023)"},{"key":"1_CR28","doi-asserted-by":"crossref","unstructured":"Li, X., et al.: Hero: rethinking visual token early dropping in high-resolution large vision-language models (2025). https:\/\/arxiv.org\/abs\/2509.13067","DOI":"10.1109\/TMM.2026.3724758"},{"key":"1_CR29","doi-asserted-by":"publisher","unstructured":"Li, Y., Du, Y., Zhou, K., Wang, J., Zhao, X., Wen, J.R.: Evaluating object hallucination in large vision-language models. In: Bouamor, H., Pino, J., Bali, K. (eds.) Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pp. 292\u2013305. Association for Computational Linguistics, Singapore (2023). https:\/\/doi.org\/10.18653\/v1\/2023.emnlp-main.20. https:\/\/aclanthology.org\/2023.emnlp-main.20\/","DOI":"10.18653\/v1\/2023.emnlp-main.20"},{"key":"1_CR30","doi-asserted-by":"crossref","unstructured":"Li, Y., Wei, F., Zhang, C., Zhang, H.: Eagle-2: faster inference of language models with dynamic draft trees. In: Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pp. 7421\u20137432 (2024)","DOI":"10.18653\/v1\/2024.emnlp-main.422"},{"key":"1_CR31","unstructured":"Li, Y., Wei, F., Zhang, C., Zhang, H.: Eagle: speculative sampling requires rethinking feature uncertainty. arXiv preprint arXiv:2401.15077 (2024)"},{"key":"1_CR32","doi-asserted-by":"crossref","unstructured":"Li, Y., Wei, F., Zhang, C., Zhang, H.: Eagle-3: scaling up inference acceleration of large language models via training-time test. arXiv preprint arXiv:2503.01840 (2025)","DOI":"10.52202\/085713-4562"},{"key":"1_CR33","unstructured":"Lian, S., et al.: Ui-agile: advancing gui agents with effective reinforcement learning and precise inference-time grounding. arXiv preprint arXiv:2507.22025 (2025)"},{"key":"1_CR34","doi-asserted-by":"crossref","unstructured":"Lin, B., et al.: Moe-llava: mixture of experts for large vision-language models. IEEE Trans. Multimedia (2026)","DOI":"10.1109\/TMM.2026.3654458"},{"key":"1_CR35","unstructured":"Lin, L., Lin, Z., Zeng, Z., Ji, R.: Speculative decoding reimagined for multimodal large language models. arXiv preprint arXiv:2505.14260 (2025)"},{"key":"1_CR36","unstructured":"Lin, Z., Sun, Z., Chen, S., Chen, X., Zhao, R.: Accelerating multi-modal llm gaming performance via input prediction and mishit correction. arXiv preprint arXiv:2512.17250 (2025)"},{"key":"1_CR37","doi-asserted-by":"publisher","unstructured":"Liu, Z., et al.: Efficient inference of vision instruction-following models with elastic cache. In: European Conference on Computer Vision, pp. 54\u201369. Springer, Heidelberg (2024). https:\/\/doi.org\/10.1007\/978-3-031-72643-9_4","DOI":"10.1007\/978-3-031-72643-9_4"},{"key":"1_CR38","first-page":"168008","volume":"38","author":"Y Luo","year":"2026","unstructured":"Luo, Y., et al.: Video-rag: visually-aligned retrieval-augmented long video comprehension. Adv. Neural. Inf. Process. Syst. 38, 168008\u2013168033 (2026)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"1_CR39","unstructured":"Ma, Q., Ma, Y., Xie, Y., Xie, T., Zheng, X., Ji, R.: A2rbench: an automatic paradigm for formally verifiable abstract reasoning benchmark generation (2026). https:\/\/arxiv.org\/abs\/2605.17278"},{"key":"1_CR40","unstructured":"Ma, Q., Wu, Y., Zheng, X., Ji, R.: Benchmarking abstract and reasoning abilities through a theoretical perspective (2025). https:\/\/arxiv.org\/abs\/2505.23833"},{"key":"1_CR41","unstructured":"OpenAI: Introducing OpenAI o3 and o4-mini (2025). https:\/\/openai.com\/index\/introducing-o3-and-o4-mini\/, Accessed: 2026-02-24"},{"key":"1_CR42","doi-asserted-by":"crossref","unstructured":"Pan, R., Dai, Y., Zhang, Z., Oliaro, G., Jia, Z., Netravali, R.: Specreason: fast and accurate inference-time compute via speculative reasoning. arXiv preprint arXiv:2504.07891 (2025)","DOI":"10.52202\/085713-0428"},{"key":"1_CR43","unstructured":"Peng, Y., et\u00a0al.: Skywork r1v: pioneering multimodal reasoning with chain-of-thought. arXiv preprint arXiv:2504.05599 (2025)"},{"key":"1_CR44","doi-asserted-by":"publisher","first-page":"68539","DOI":"10.52202\/075280-2997","volume":"36","author":"T Schick","year":"2023","unstructured":"Schick, T., et al.: Toolformer: language models can teach themselves to use tools. Adv. Neural. Inf. Process. Syst. 36, 68539\u201368551 (2023)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"1_CR45","doi-asserted-by":"crossref","unstructured":"Shao, Z., et al.: Growing a twig to accelerate large vision-language models. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 20064\u201320074 (2025)","DOI":"10.1109\/ICCV51701.2025.01866"},{"key":"1_CR46","doi-asserted-by":"publisher","first-page":"38154","DOI":"10.52202\/075280-1657","volume":"36","author":"Y Shen","year":"2023","unstructured":"Shen, Y., Song, K., Tan, X., Li, D., Lu, W., Zhuang, Y.: Hugginggpt: solving ai tasks with chatgpt and its friends in hugging face. Adv. Neural. Inf. Process. Syst. 36, 38154\u201338180 (2023)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"1_CR47","unstructured":"Song, Q., et al.: Codedance: a dynamic tool-integrated mllm for executable visual reasoning. ArXiv arxiv:2512.17312 (2025). https:\/\/api.semanticscholar.org\/CorpusID:284058227"},{"key":"1_CR48","unstructured":"Team, G., et al.: Gemini: a family of highly capable multimodal models. arXiv preprint arXiv:2312.11805 (2023)"},{"key":"1_CR49","unstructured":"Team, K., et\u00a0al.: Kimi k2. 5: visual agentic intelligence. arXiv preprint arXiv:2602.02276 (2026)"},{"key":"1_CR50","unstructured":"Team, Q.: Qwen3 technical report (2025). https:\/\/arxiv.org\/abs\/2505.09388"},{"key":"1_CR51","doi-asserted-by":"crossref","unstructured":"Teerapittayanon, S., McDanel, B., Kung, H.T.: Branchynet: fast inference via early exiting from deep neural networks. In: 2016 23rd International Conference on Pattern Recognition (ICPR), pp. 2464\u20132469. IEEE (2016)","DOI":"10.1109\/ICPR.2016.7900006"},{"key":"1_CR52","doi-asserted-by":"crossref","unstructured":"Wan, Z., Shen, H., Wang, X., Liu, C., Mai, Z., Zhang, M.: Meda: dynamic kv cache allocation for efficient multimodal long-context inference. In: Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies, vol. 1: Long Papers, pp. 2485\u20132497 (2025)","DOI":"10.18653\/v1\/2025.naacl-long.125"},{"key":"1_CR53","doi-asserted-by":"crossref","unstructured":"Wan, Z., et al.: Look-m: Look-once optimization in kv cache for efficient multimodal long-context inference. In: Findings of the Association for Computational Linguistics: EMNLP 2024, pp. 4065\u20134078 (2024)","DOI":"10.18653\/v1\/2024.findings-emnlp.235"},{"key":"1_CR54","unstructured":"Wang, H., et al.: Fourier-vlm: compressing vision tokens in the frequency domain for large vision-language models (2025). https:\/\/arxiv.org\/abs\/2508.06038"},{"key":"1_CR55","doi-asserted-by":"crossref","unstructured":"Wang, W., et al.: Divide, conquer and combine: a training-free framework for high-resolution image perception in multimodal large language models. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 39, pp. 7907\u20137915 (2025)","DOI":"10.1609\/aaai.v39i8.32852"},{"key":"1_CR56","doi-asserted-by":"crossref","unstructured":"Wang, Y., Yang, Y.: Efficient visual transformer by learnable token merging. IEEE Trans. Pattern Anal. Mach. Intell. (2025)","DOI":"10.1109\/TPAMI.2025.3588186"},{"key":"1_CR57","doi-asserted-by":"crossref","unstructured":"Wu, P., Xie, S.: V*: guided visual search as a core mechanism in multimodal llms. arXiv preprint arXiv:2312.14135 (2023)","DOI":"10.1109\/CVPR52733.2024.01243"},{"key":"1_CR58","doi-asserted-by":"publisher","unstructured":"Xia, H., Ge, T., Wang, P., Chen, S.Q., Wei, F., Sui, Z.: Speculative decoding: exploiting speculative execution for accelerating seq2seq generation. In: Bouamor, H., Pino, J., Bali, K. (eds.) Findings of the Association for Computational Linguistics: EMNLP 2023, pp. 3909\u20133925. Association for Computational Linguistics, Singapore (2023). https:\/\/doi.org\/10.18653\/v1\/2023.findings-emnlp.257. https:\/\/aclanthology.org\/2023.findings-emnlp.257\/","DOI":"10.18653\/v1\/2023.findings-emnlp.257"},{"key":"1_CR59","unstructured":"Xia, H., Li, Y., Zhang, J., Du, C., Li, W.: Swift: on-the-fly self-speculative decoding for llm inference acceleration. arXiv preprint arXiv:2410.06916 (2024)"},{"key":"1_CR60","unstructured":"Xie, T., et al.: Socialomni: benchmarking audio-visual social interactivity in omni models. arXiv preprint arXiv:2603.16859 (2026)"},{"key":"1_CR61","unstructured":"Xie, T., et al.: Training-free multimodal large language model orchestration. arXiv preprint arXiv:2508.10016 (2025)"},{"key":"1_CR62","unstructured":"Xing, L., et al.: Pyramiddrop: accelerating your large vision-language models via pyramid visual redundancy reduction. arXiv preprint arXiv:2410.17247 (2024)"},{"key":"1_CR63","unstructured":"Xiong, J., et al.: Atts: asynchronous test-time scaling via conformal prediction (2026). https:\/\/arxiv.org\/abs\/2509.15148"},{"key":"1_CR64","doi-asserted-by":"crossref","unstructured":"Xu, J., et al.: Specee: accelerating large language model inference with speculative early exiting. In: Proceedings of the 52nd Annual International Symposium on Computer Architecture, pp. 467\u2013481 (2025)","DOI":"10.1145\/3695053.3730996"},{"key":"1_CR65","unstructured":"Yang, P., et al.: Longspec: long-context speculative decoding with efficient drafting and verification. arXiv e-prints pp. arXiv\u20132502 (2025)"},{"key":"1_CR66","doi-asserted-by":"crossref","unstructured":"Yang, S., et al.: Visionzip: longer is better but not necessary in vision language models. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 19792\u201319802 (2025)","DOI":"10.1109\/CVPR52734.2025.01843"},{"key":"1_CR67","unstructured":"Yang, W., et al.: Deep but reliable: advancing multi-turn reasoning for thinking with images (2026). https:\/\/arxiv.org\/abs\/2512.17306"},{"key":"1_CR68","unstructured":"Yao, S., et al.: React: synergizing reasoning and acting in language models. In: The Eleventh International Conference on Learning Representations (2022)"},{"key":"1_CR69","unstructured":"Yu, Z., et al.: Recode: unify plan and action for universal granularity control. arXiv preprint arXiv:2510.23564 (2025)"},{"key":"1_CR70","doi-asserted-by":"crossref","unstructured":"Zhang, J., et al.: Draft& verify: lossless large language model acceleration via self-speculative decoding. In: Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics, vol. 1: Long Papers, pp. 11263\u201311282 (2024)","DOI":"10.18653\/v1\/2024.acl-long.607"},{"key":"1_CR71","unstructured":"Zhang, Y.F., et\u00a0al.: Thyme: think beyond images. arXiv preprint arXiv:2508.11630 (2025)"},{"key":"1_CR72","unstructured":"Zhang, Y., et al.: Skywork-r1v4: toward agentic multimodal intelligence through interleaved thinking with images and deepresearch. arXiv preprint arXiv:2512.02395 (2025)"},{"key":"1_CR73","unstructured":"Zhao, S., et al.: Pyvision-rl: forging open agentic vision models via rl. arXiv preprint arXiv:2602.20739 (2026)"},{"key":"1_CR74","doi-asserted-by":"crossref","unstructured":"Zhao, W., et al.: A stitch in time saves nine: small vlm is a precise guidance for accelerating large vlms. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 19814\u201319824 (2025)","DOI":"10.1109\/CVPR52734.2025.01845"},{"key":"1_CR75","unstructured":"Zheng, Z., et al.: Deepeyes: incentivizing \u201cthinking with images\" via reinforcement learning. arXiv preprint arXiv:2505.14362 (2025)"},{"key":"1_CR76","unstructured":"Zhu, Y., Yang, X., Wu, Y., Zhang, W.: Hierarchical skip decoding for efficient autoregressive text generation. arXiv preprint arXiv:2403.14919 (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\/978-3-032-37461-5_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T06:41:44Z","timestamp":1790232104000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-37461-5_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032374608","9783032374615"],"references-count":76,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-37461-5_1","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":"25 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"}}]}}