{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T00:55:24Z","timestamp":1767315324462,"version":"3.48.0"},"publisher-location":"Cham","reference-count":47,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032101846","type":"print"},{"value":"9783032101853","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\/978-3-032-10185-3_2","type":"book-chapter","created":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T00:52:30Z","timestamp":1767315150000},"page":"17-28","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Zero-Shot Neural Architecture Search for\u00a0Efficient Deep Stereo Matching"],"prefix":"10.1007","author":[{"given":"Alessio","family":"Mingozzi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stefano","family":"Mattoccia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Matteo","family":"Poggi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fatma","family":"G\u00fcney","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,1,2]]},"reference":[{"key":"2_CR1","unstructured":"Abdelfattah, M.S., Mehrotra, A., Dudziak, \u0141., Lane, N.D.: Zero-cost proxies for lightweight NAS (2021). https:\/\/arxiv.org\/abs\/2101.08134"},{"key":"2_CR2","doi-asserted-by":"crossref","unstructured":"Aleotti, F., et al.: Neural disparity refinement for arbitrary resolution stereo. In: 3DV (2021)","DOI":"10.1109\/3DV53792.2021.00031"},{"key":"2_CR3","doi-asserted-by":"crossref","unstructured":"Bartolomei, L., Poggi, M., Tosi, F., Conti, A., Mattoccia, S.: Active stereo without pattern projector. In: ICCV (2023)","DOI":"10.1109\/ICCV51070.2023.01693"},{"key":"2_CR4","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"611","DOI":"10.1007\/978-3-642-33783-3_44","volume-title":"Computer Vision \u2013 ECCV 2012","author":"DJ Butler","year":"2012","unstructured":"Butler, D.J., Wulff, J., Stanley, G.B., Black, M.J.: A naturalistic open source movie for optical flow evaluation. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7577, pp. 611\u2013625. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-33783-3_44"},{"key":"2_CR5","doi-asserted-by":"crossref","unstructured":"Cai, C., Poggi, M., Mattoccia, S., Mordohai, P.: Matching-space stereo networks for cross-domain generalization. In: 3DV (2020)","DOI":"10.1109\/3DV50981.2020.00046"},{"key":"2_CR6","unstructured":"Cheng, X., et al.: Hierarchical neural architecture search for deep stereo matching. In: NeurIPS (2020)"},{"key":"2_CR7","doi-asserted-by":"crossref","unstructured":"Costanzino, A., Ramirez, P.Z., Poggi, M., Tosi, F., Mattoccia, S., Di\u00a0Stefano, L.: Learning depth estimation for transparent and mirror surfaces. In: CVPR (2023)","DOI":"10.1109\/ICCV51070.2023.00848"},{"key":"2_CR8","doi-asserted-by":"crossref","unstructured":"Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? The Kitti vision benchmark suite. In: CVPR (2012)","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"2_CR9","doi-asserted-by":"publisher","unstructured":"Han, K., Wang, Y., Tian, Q., Guo, J., Xu, C., Xu, C.: GhostNet: more features from cheap operations (2020). https:\/\/doi.org\/10.48550\/arXiv.1911.11907","DOI":"10.48550\/arXiv.1911.11907"},{"key":"2_CR10","doi-asserted-by":"publisher","unstructured":"Han, K., et al.: GhostNets on heterogeneous devices via cheap operations (2022). https:\/\/doi.org\/10.48550\/arXiv.2201.03297","DOI":"10.48550\/arXiv.2201.03297"},{"key":"2_CR11","doi-asserted-by":"publisher","unstructured":"Hutter, F., Kotthoff, L., Vanschoren, J. (eds.): Automated Machine Learning. TSSCML, Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-05318-5","DOI":"10.1007\/978-3-030-05318-5"},{"key":"2_CR12","doi-asserted-by":"crossref","unstructured":"Kendall, A., et al.: End-to-end learning of geometry and context for deep stereo regression. In: ICCV (2017)","DOI":"10.1109\/ICCV.2017.17"},{"key":"2_CR13","doi-asserted-by":"crossref","unstructured":"Lee, J., Ham, B.: AZ-NAS: assembling zero-cost proxies for network architecture search. In: CVPR (2024)","DOI":"10.1109\/CVPR52733.2024.00563"},{"key":"2_CR14","unstructured":"Li, G., Hoang, D., Bhardwaj, K., Lin, M., Wang, Z., Marculescu, R.: Zero-shot neural architecture search: challenges, solutions, and opportunities. TPAMI 1\u201319 (2024)"},{"key":"2_CR15","unstructured":"Li, G., Yang, Y., Bhardwaj, K., M\u0103rculescu, R.: ZiCo: zero-shot NAS via inverse coefficient of variation on gradients. ICLR (2023)"},{"key":"2_CR16","doi-asserted-by":"crossref","unstructured":"Li, J., et al.: Practical stereo matching via cascaded recurrent network with adaptive correlation. In: CVPR (2022)","DOI":"10.1109\/CVPR52688.2022.01578"},{"key":"2_CR17","doi-asserted-by":"crossref","unstructured":"Lin, M., et al.: Zen-NAS: a zero-shot NAS for high-performance image recognition. In: ICCV (2021)","DOI":"10.1109\/ICCV48922.2021.00040"},{"key":"2_CR18","doi-asserted-by":"crossref","unstructured":"Lipson, L., Teed, Z., Deng, J.: Raft-stereo: multilevel recurrent field transforms for stereo matching. In: 3DV (2021)","DOI":"10.1109\/3DV53792.2021.00032"},{"key":"2_CR19","doi-asserted-by":"publisher","unstructured":"Lipson, L., Teed, Z., Deng, J.: RAFT-stereo: multilevel recurrent field transforms for stereo matching (2021). https:\/\/doi.org\/10.48550\/arXiv.2109.07547","DOI":"10.48550\/arXiv.2109.07547"},{"key":"2_CR20","doi-asserted-by":"publisher","unstructured":"Liu, H., Simonyan, K., Yang, Y.: DARTS: Differentiable Architecture Search (2019). https:\/\/doi.org\/10.48550\/arXiv.1806.09055","DOI":"10.48550\/arXiv.1806.09055"},{"key":"2_CR21","doi-asserted-by":"crossref","unstructured":"Lopes, V., Alirezazadeh, S., Alexandre, L.A.: EPE-NAS: efficient performance estimation without training for neural architecture search. In: ICANN (2021)","DOI":"10.1007\/978-3-030-86383-8_44"},{"key":"2_CR22","doi-asserted-by":"crossref","unstructured":"Mayer, N., et al.: A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.438"},{"key":"2_CR23","doi-asserted-by":"crossref","unstructured":"Menze, M., Geiger, A.: Object scene flow for autonomous vehicles. In: CVPR (2015)","DOI":"10.1109\/CVPR.2015.7298925"},{"key":"2_CR24","doi-asserted-by":"crossref","unstructured":"Poggi, M., Pallotti, D., Tosi, F., Mattoccia, S.: Guided stereo matching. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00107"},{"issue":"9","key":"2_CR25","doi-asserted-by":"publisher","first-page":"4713","DOI":"10.1109\/TPAMI.2021.3075815","volume":"44","author":"M Poggi","year":"2021","unstructured":"Poggi, M., Tonioni, A., Tosi, F., Mattoccia, S., Di Stefano, L.: Continual adaptation for deep stereo. TPAMI 44(9), 4713\u20134729 (2021)","journal-title":"TPAMI"},{"key":"2_CR26","doi-asserted-by":"crossref","unstructured":"Poggi, M., Tosi, F.: Federated online adaptation for deep stereo. In: CVPR (2024)","DOI":"10.1109\/CVPR52733.2024.01906"},{"issue":"1","key":"2_CR27","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1109\/TPAMI.2023.3323858","volume":"46","author":"PZ Ramirez","year":"2024","unstructured":"Ramirez, P.Z., et al.: Booster: a benchmark for depth from images of specular and transparent surfaces. TPAMI 46(1), 85\u2013102 (2024)","journal-title":"TPAMI"},{"key":"2_CR28","doi-asserted-by":"publisher","unstructured":"Real, E., Aggarwal, A., Huang, Y., Le, Q.V.: Regularized evolution for image classifier architecture search (2019). https:\/\/doi.org\/10.48550\/arXiv.1802.01548","DOI":"10.48550\/arXiv.1802.01548"},{"key":"2_CR29","doi-asserted-by":"crossref","unstructured":"Saikia, T., Marrakchi, Y., Zela, A., Hutter, F., Brox, T.: Autodispnet: improving disparity estimation with autoML. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00190"},{"key":"2_CR30","doi-asserted-by":"crossref","unstructured":"Scharstein, D., et al.: High-resolution stereo datasets with subpixel-accurate ground truth. In: GCPR (2014)","DOI":"10.1007\/978-3-319-11752-2_3"},{"key":"2_CR31","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1023\/A:1014573219977","volume":"47","author":"D Scharstein","year":"2002","unstructured":"Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. IJCV 47, 7\u201342 (2002)","journal-title":"IJCV"},{"key":"2_CR32","doi-asserted-by":"crossref","unstructured":"Schops, T., et al.: A multi-view stereo benchmark with high-resolution images and multi-camera videos. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.272"},{"key":"2_CR33","doi-asserted-by":"publisher","unstructured":"Tan, M., Pang, R., Le, Q.V.: EfficientDet: scalable and efficient object detection (2020). https:\/\/doi.org\/10.48550\/arXiv.1911.09070","DOI":"10.48550\/arXiv.1911.09070"},{"key":"2_CR34","unstructured":"Tanaka, H., Kunin, D., Yamins, D.L., Ganguli, S.: Pruning neural networks without any data by iteratively conserving synaptic flow. In: NeurIPS (2020)"},{"key":"2_CR35","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"402","DOI":"10.1007\/978-3-030-58536-5_24","volume-title":"Computer Vision \u2013 ECCV 2020","author":"Z Teed","year":"2020","unstructured":"Teed, Z., Deng, J.: RAFT: recurrent all-pairs field transforms for optical flow. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12347, pp. 402\u2013419. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58536-5_24"},{"key":"2_CR36","doi-asserted-by":"crossref","unstructured":"Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00028"},{"key":"2_CR37","doi-asserted-by":"crossref","unstructured":"Tosi, F., et al.: Neural disparity refinement. TPAMI (2024)","DOI":"10.1109\/TPAMI.2024.3411292"},{"key":"2_CR38","unstructured":"Tosi, F., Bartolomei, L., Poggi, M.: A survey on deep stereo matching in the twenties. arXiv preprint arXiv:2407.07816 (2024). https:\/\/arxiv.org\/abs\/2407.07816, extended version of CVPR 2024 Tutorial \u201cDeep Stereo Matching in the Twenties\u201d (https:\/\/sites.google.com\/view\/stereo-twenties)"},{"key":"2_CR39","doi-asserted-by":"crossref","unstructured":"Tosi, F., Liao, Y., Schmitt, C., Geiger, A.: SMD-nets: stereo mixture density networks. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.00883"},{"key":"2_CR40","doi-asserted-by":"crossref","unstructured":"Tosi, F., Tonioni, A., De\u00a0Gregorio, D., Poggi, M.: NeRF-supervised deep stereo. In: CVPR (2023)","DOI":"10.1109\/CVPR52729.2023.00089"},{"key":"2_CR41","doi-asserted-by":"crossref","unstructured":"Tremblay, J., To, T., Birchfield, S.: Falling things: a synthetic dataset for 3D object detection and pose estimation. In: CVPR Workshops (2018)","DOI":"10.1109\/CVPRW.2018.00275"},{"key":"2_CR42","unstructured":"Wang, C., Zhang, G., Grosse, R.: Picking winning tickets before training by preserving gradient flow (2020). https:\/\/arxiv.org\/abs\/2002.07376"},{"key":"2_CR43","doi-asserted-by":"publisher","unstructured":"Watanabe, S.: Tree-structured parzen estimator: understanding its algorithm components and their roles for better empirical performance 2023). https:\/\/doi.org\/10.48550\/arXiv.2304.11127","DOI":"10.48550\/arXiv.2304.11127"},{"key":"2_CR44","doi-asserted-by":"publisher","unstructured":"Wu, B., et al.: FBNet: hardware-aware efficient ConvNet design via differentiable neural architecture search (2019). https:\/\/doi.org\/10.48550\/arXiv.1812.03443","DOI":"10.48550\/arXiv.1812.03443"},{"key":"2_CR45","doi-asserted-by":"crossref","unstructured":"Yin, Z., Darrell, T., Yu, F.: Hierarchical discrete distribution decomposition for match density estimation. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00620"},{"key":"2_CR46","doi-asserted-by":"crossref","unstructured":"Zama\u00a0Ramirez, P., Tosi, F., Poggi, M., Salti, S., Di\u00a0Stefano, L., Mattoccia, S.: Open challenges in deep stereo: the booster dataset. In: CVPR (2022)","DOI":"10.1109\/CVPR52688.2022.02049"},{"key":"2_CR47","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Poggi, M., Mattoccia, S.: TemporalStereo: efficient spatial-temporal stereo matching network. In: IROS (2023)","DOI":"10.1109\/IROS55552.2023.10341598"}],"container-title":["Lecture Notes in Computer Science","Image Analysis and Processing \u2013 ICIAP 2025"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-10185-3_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T00:52:34Z","timestamp":1767315154000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-10185-3_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032101846","9783032101853"],"references-count":47,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-10185-3_2","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":"2 January 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIAP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Image Analysis and Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Rome","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":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iciap2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.iciap.org\/home","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}