{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T02:50:28Z","timestamp":1772679028000,"version":"3.50.1"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2022,10,18]],"date-time":"2022-10-18T00:00:00Z","timestamp":1666051200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,10,18]],"date-time":"2022-10-18T00:00:00Z","timestamp":1666051200000},"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":["Sci. China Inf. Sci."],"published-print":{"date-parts":[[2022,11]]},"DOI":"10.1007\/s11432-021-3364-5","type":"journal-article","created":{"date-parts":[[2022,10,22]],"date-time":"2022-10-22T07:02:42Z","timestamp":1666422162000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Repeatable adaptive keypoint detection via self-supervised learning"],"prefix":"10.1007","volume":"65","author":[{"given":"Pei","family":"Yan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yihua","family":"Tan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuan","family":"Tai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,10,18]]},"reference":[{"key":"3364_CR1","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","volume":"60","author":"D G Lowe","year":"2004","unstructured":"Lowe D G. Distinctive image features from scale-invariant keypoints. Int J Comput Vision, 2004, 60: 91\u2013110","journal-title":"Int J Comput Vision"},{"key":"3364_CR2","doi-asserted-by":"publisher","first-page":"112204","DOI":"10.1007\/s11432-019-2690-0","volume":"64","author":"C H Zhao","year":"2021","unstructured":"Zhao C H, Fan B, Hu J W, et al. Homography-based camera pose estimation with known gravity direction for UAV navigation. Sci China Inf Sci, 2021, 64: 112204","journal-title":"Sci China Inf Sci"},{"key":"3364_CR3","doi-asserted-by":"publisher","first-page":"192105","DOI":"10.1007\/s11432-020-2973-7","volume":"64","author":"M T Chen","year":"2021","unstructured":"Chen M T, Wang X G, Luo H, et al. Learning to focus: cascaded feature matching network for few-shot image recognition. Sci China Inf Sci, 2021, 64: 192105","journal-title":"Sci China Inf Sci"},{"key":"3364_CR4","doi-asserted-by":"publisher","first-page":"023101","DOI":"10.1007\/s11432-017-9234-7","volume":"61","author":"Q L Dong","year":"2018","unstructured":"Dong Q L, Shu M, Cui H N, et al. Learning stratified 3D reconstruction. Sci China Inf Sci, 2018, 61: 023101","journal-title":"Sci China Inf Sci"},{"key":"3364_CR5","doi-asserted-by":"crossref","unstructured":"Rosten E, Drummond T. Machine learning for high-speed corner detection. In: Proceedings of European Conference on Computer Vision, 2006. 430\u2013443","DOI":"10.1007\/11744023_34"},{"key":"3364_CR6","doi-asserted-by":"crossref","unstructured":"Strecha C, Lindner A, Ali K, et al. Training for task specific keypoint detection. In: Proceedings of Joint Pattern Recognition Symposium, 2009. 151\u2013160","DOI":"10.1007\/978-3-642-03798-6_16"},{"key":"3364_CR7","doi-asserted-by":"crossref","unstructured":"Verdie Y, Yi K, Fua P, et al. TILDE: a temporally invariant learned detector. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015. 5279\u20135288","DOI":"10.1109\/CVPR.2015.7299165"},{"key":"3364_CR8","doi-asserted-by":"crossref","unstructured":"Yi K M, Trulls E, Lepetit V, et al. LIFT: learned invariant feature transform. In: Proceedings of European Conference on Computer Vision, 2016. 467\u2013483","DOI":"10.1007\/978-3-319-46466-4_28"},{"key":"3364_CR9","doi-asserted-by":"crossref","unstructured":"DeTone D, Malisiewicz T, Rabinovich A. SuperPoint: self-supervised interest point detection and description. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2018. 224\u2013236","DOI":"10.1109\/CVPRW.2018.00060"},{"key":"3364_CR10","doi-asserted-by":"crossref","unstructured":"Laguna A B, Riba E, Ponsa D, et al. Key.Net: keypoint detection by handcrafted and learned CNN filters. In: Proceedings of International Conference on Computer Vision. 2019. 5835\u20135843","DOI":"10.1109\/ICCV.2019.00593"},{"key":"3364_CR11","unstructured":"Ono Y, Trulls E, Fua P, et al. LF-Net: learning local features from images. In: Proceedings of Advances in Neural Information Processing Systems, 2018. 6237\u20136247"},{"key":"3364_CR12","unstructured":"Revaud J, de Souza C R, Humenberger M, et al. R2D2: reliable and repeatable detector and descriptor. In: Proceedings of Advances in Neural Information Processing Systems, 2019. 12405\u201312415"},{"key":"3364_CR13","doi-asserted-by":"crossref","unstructured":"Sch\u00f6nberger J L, Frahm J M. Structure-from-motion revisited. In: Proceedings of Conference on Computer Vision and Pattern Recognition (CVPR), 2016","DOI":"10.1109\/CVPR.2016.445"},{"key":"3364_CR14","doi-asserted-by":"publisher","first-page":"40850","DOI":"10.1109\/ACCESS.2021.3065022","volume":"9","author":"S Kim","year":"2021","unstructured":"Kim S, Jeong M, Ko B C. Self-supervised keypoint detection based on multi-layer random forest regressor. IEEE Access, 2021, 9: 40850\u201340859","journal-title":"IEEE Access"},{"key":"3364_CR15","doi-asserted-by":"publisher","first-page":"107808","DOI":"10.1016\/j.patcog.2020.107808","volume":"112","author":"P Yan","year":"2021","unstructured":"Yan P, Tan Y, Tai Y, et al. Unsupervised learning framework for interest point detection and description via properties optimization. Pattern Recogn, 2021, 112: 107808","journal-title":"Pattern Recogn"},{"key":"3364_CR16","doi-asserted-by":"crossref","unstructured":"Bay H, Tuytelaars T, van Gool L. SURF: speeded up robust features. In: Proceedings of European Conference on Computer Vision, 2006. 404\u2013417","DOI":"10.1007\/11744023_32"},{"key":"3364_CR17","doi-asserted-by":"crossref","unstructured":"Alcantarilla P F, Bartoli A, Davison A J. KAZE features. In: Proceedings of European Conference on Computer Vision, 2012. 214\u2013227","DOI":"10.1007\/978-3-642-33783-3_16"},{"key":"3364_CR18","doi-asserted-by":"crossref","unstructured":"Noh H, Araujo A, Sim J, et al. Large-scale image retrieval with attentive deep local features. In: Proceedings of the IEEE International Conference on Computer Vision, 2017. 3456\u20133465","DOI":"10.1109\/ICCV.2017.374"},{"key":"3364_CR19","doi-asserted-by":"crossref","unstructured":"Dusmanu M, Rocco I, Pajdla T, et al. D2-Net: a trainable CNN for joint description and detection of local features. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019. 8092\u20138101","DOI":"10.1109\/CVPR.2019.00828"},{"key":"3364_CR20","doi-asserted-by":"crossref","unstructured":"Savinov N, Seki A, Ladicky L, et al. Quad-networks: unsupervised learning to rank for interest point detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017. 1822\u20131830","DOI":"10.1109\/CVPR.2017.418"},{"key":"3364_CR21","doi-asserted-by":"crossref","unstructured":"Cieslewski T, Derpanis K G, Scaramuzza D. SIPs: succinct interest points from unsupervised inlierness probability learning. In: Proceedings of International Conference on 3D Vision, 2019. 604\u2013613","DOI":"10.1109\/3DV.2019.00072"},{"key":"3364_CR22","doi-asserted-by":"crossref","unstructured":"Mishkin D, Radenovi\u0107 F, Matas J. Repeatability is not enough: learning affine regions via discriminability. In: Proceedings of European Conference on Computer Vision, 2018","DOI":"10.1007\/978-3-030-01240-3_18"},{"key":"3364_CR23","doi-asserted-by":"publisher","first-page":"4037","DOI":"10.1109\/TPAMI.2020.2992393","volume":"43","author":"L Jing","year":"2021","unstructured":"Jing L, Tian Y. Self-supervised visual feature learning with deep neural networks: a survey. IEEE Trans Pattern Anal Mach Intell, 2021, 43: 4037\u20134058","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"3364_CR24","doi-asserted-by":"crossref","unstructured":"Zhang R, Isola P, Efros A A. Colorful image colorization. In: Proceedings of European Conference on Computer Vision, 2016. 649\u2013666","DOI":"10.1007\/978-3-319-46487-9_40"},{"key":"3364_CR25","doi-asserted-by":"crossref","unstructured":"Ledig C, Theis L, Huszar F, et al. Photo-realistic single image super-resolution using a generative adversarial network. In: Proceedings of Computer Vision and Pattern Recognition, 2017. 105\u2013114","DOI":"10.1109\/CVPR.2017.19"},{"key":"3364_CR26","doi-asserted-by":"crossref","unstructured":"Pathak D, Kr\u00e4henb\u00fchl P, Donahue J, et al. Context encoders: feature learning by inpainting. In: Proceedings of Conference on Computer Vision and Pattern Recognition, 2016. 2536\u20132544","DOI":"10.1109\/CVPR.2016.278"},{"key":"3364_CR27","unstructured":"Chen T, Kornblith S, Norouzi M, et al. A simple framework for contrastive learning of visual representations. In: Proceedings of the 37th International Conference on Machine Learning, 2020. 119: 1597\u20131607"},{"key":"3364_CR28","doi-asserted-by":"crossref","unstructured":"Lin T Y, Maire M, Belongie S, et al. Microsoft COCO: common objects in context. In: Proceedings of European Conference on Computer Vision, 2014. 740\u2013755","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"3364_CR29","doi-asserted-by":"crossref","unstructured":"Balntas V, Lenc K, Vedaldi A, et al. Hpatches: a benchmark and evaluation of handcrafted and learned local descriptors. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017. 5173\u20135182","DOI":"10.1109\/CVPR.2017.410"},{"key":"3364_CR30","doi-asserted-by":"crossref","unstructured":"Menze M, Geiger A. Object scene flow for autonomous vehicles. In: Proceedings of Conference on Computer Vision and Pattern Recognition (CVPR), 2015","DOI":"10.1109\/CVPR.2015.7298925"},{"key":"3364_CR31","doi-asserted-by":"crossref","unstructured":"Geiger A, Lenz P, Urtasun R. Are we ready for autonomous driving? The KITTI vision benchmark suite. In: Proceedings of Conference on Computer Vision and Pattern Recognition (CVPR), 2012","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"3364_CR32","doi-asserted-by":"crossref","unstructured":"Rublee E, Rabaud V, Konolige K, et al. ORB: an efficient alternative to SIFT or SURF. In: Proceedings of International Conference on Computer Vision, 2011. 2564\u20132571","DOI":"10.1109\/ICCV.2011.6126544"},{"key":"3364_CR33","doi-asserted-by":"publisher","first-page":"381","DOI":"10.1145\/358669.358692","volume":"24","author":"M A Fischler","year":"1981","unstructured":"Fischler M A, Bolles R C. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun ACM, 1981, 24: 381\u2013395","journal-title":"Commun ACM"},{"key":"3364_CR34","doi-asserted-by":"crossref","unstructured":"Chum O, Matas J. Matching with PROSAC\u2014progressive sample consensus. In: Proceedings of Conference on Computer Vision and Pattern Recognition, 2005. 220\u2013226","DOI":"10.1109\/CVPR.2005.221"},{"key":"3364_CR35","doi-asserted-by":"crossref","unstructured":"He K, Sun J. Convolutional neural networks at constrained time cost. In: Proceedings of Conference on Computer Vision and Pattern Recognition CVPR, 2015. 5353\u20135360","DOI":"10.1109\/CVPR.2015.7299173"},{"key":"3364_CR36","doi-asserted-by":"crossref","unstructured":"Sch\u00fcnberger J L, Hardmeier H, Sattler T, et al. Comparative evaluation of hand-crafted and learned local features. In: Proceedings of Conference on Computer Vision and Pattern Recognition, 2017. 6959\u20136968","DOI":"10.1109\/CVPR.2017.736"}],"container-title":["Science China Information Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11432-021-3364-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11432-021-3364-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11432-021-3364-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,6]],"date-time":"2024-10-06T09:58:27Z","timestamp":1728208707000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11432-021-3364-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,18]]},"references-count":36,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2022,11]]}},"alternative-id":["3364"],"URL":"https:\/\/doi.org\/10.1007\/s11432-021-3364-5","relation":{},"ISSN":["1674-733X","1869-1919"],"issn-type":[{"value":"1674-733X","type":"print"},{"value":"1869-1919","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,18]]},"assertion":[{"value":"22 April 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 August 2021","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 September 2021","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 October 2022","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"212103"}}