{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T22:07:53Z","timestamp":1743113273032,"version":"3.40.3"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031262357"},{"type":"electronic","value":"9783031262364"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-26236-4_4","type":"book-chapter","created":{"date-parts":[[2023,2,10]],"date-time":"2023-02-10T15:58:23Z","timestamp":1676044703000},"page":"38-47","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Evaluating Zero-Cost Active Learning for\u00a0Object Detection"],"prefix":"10.1007","author":[{"given":"Dominik","family":"Probst","sequence":"first","affiliation":[]},{"given":"Hasnain","family":"Raza","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3711-1498","authenticated-orcid":false,"given":"Erik","family":"Rodner","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,2,11]]},"reference":[{"key":"4_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1007\/978-3-030-83906-2_19","volume-title":"Computer Safety, Reliability, and Security. SAFECOMP 2021 Workshops","author":"B Adhikari","year":"2021","unstructured":"Adhikari, B., Peltom\u00e4ki, J., Germi, S.B., Rahtu, E., Huttunen, H.: Effect of label noise on robustness of deep neural network object detectors. In: Habli, I., Sujan, M., Gerasimou, S., Schoitsch, E., Bitsch, F. (eds.) SAFECOMP 2021. LNCS, vol. 12853, pp. 239\u2013250. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-83906-2_19"},{"key":"4_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1007\/978-3-030-58517-4_9","volume-title":"Computer Vision \u2013 ECCV 2020","author":"S Agarwal","year":"2020","unstructured":"Agarwal, S., Arora, H., Anand, S., Arora, C.: Contextual diversity for active learning. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12361, pp. 137\u2013153. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58517-4_9"},{"key":"4_CR3","doi-asserted-by":"publisher","unstructured":"Alex Kendall, V.B., Cipolla, R.: Bayesian segnet: model uncertainty in deep convolutional encoder-decoder architectures for scene understanding. In: Proceedings of the British Machine Vision Conference (BMVC), pp. 57.1\u201357.12. BMVA Press (2017). https:\/\/doi.org\/10.5244\/C.31.57","DOI":"10.5244\/C.31.57"},{"key":"4_CR4","doi-asserted-by":"publisher","unstructured":"Brust, C.A., K\u00e4ding, C., Denzler, J.: Active learning for deep object detection. In: Computer Vision Theory and Applications (VISAPP), pp. 181\u2013190 (2019). https:\/\/doi.org\/10.5220\/0007248601810190","DOI":"10.5220\/0007248601810190"},{"key":"4_CR5","doi-asserted-by":"crossref","unstructured":"Choi, J., Elezi, I., Lee, H.J., Farabet, C., Alvarez, J.M.: Active learning for deep object detection via probabilistic modeling. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 10264\u201310273 (2021)","DOI":"10.1109\/ICCV48922.2021.01010"},{"key":"4_CR6","first-page":"11933","volume":"34","author":"G Citovsky","year":"2021","unstructured":"Citovsky, G., et al.: Batch active learning at scale. Adv. Neural. Inf. Process. Syst. 34, 11933\u201311944 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"issue":"1","key":"4_CR7","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1007\/s11263-014-0733-5","volume":"111","author":"M Everingham","year":"2015","unstructured":"Everingham, M., Eslami, S., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The pascal visual object classes challenge: a retrospective. Int. J. Comput. Vision 111(1), 98\u2013136 (2015)","journal-title":"Int. J. Comput. Vision"},{"key":"4_CR8","unstructured":"Feng, Z., et al.: ALBench: a framework for evaluating active learning in object detection. arXiv preprint arXiv:2207.13339 (2022)"},{"key":"4_CR9","volume-title":"Elementary Applied Statistics: For Students in Behavioral Science","author":"LC Freeman","year":"1965","unstructured":"Freeman, L.C.: Elementary Applied Statistics: For Students in Behavioral Science. Wiley, New York (1965)"},{"key":"4_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"562","DOI":"10.1007\/978-3-319-10593-2_37","volume-title":"Computer Vision \u2013 ECCV 2014","author":"A Freytag","year":"2014","unstructured":"Freytag, A., Rodner, E., Denzler, J.: Selecting influential examples: active learning with expected model output changes. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8692, pp. 562\u2013577. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10593-2_37"},{"key":"4_CR11","unstructured":"Gal, Y.: Uncertainty in deep learning. Ph.D. thesis, University of Cambridge (2016)"},{"key":"4_CR12","doi-asserted-by":"crossref","unstructured":"Joshi, A.J., Porikli, F., Papanikolopoulos, N.: Multi-class active learning for image classification. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 2372\u20132379. IEEE (2009)","DOI":"10.1109\/CVPR.2009.5206627"},{"issue":"10","key":"4_CR13","first-page":"1995","volume":"3361","author":"Y LeCun","year":"1995","unstructured":"LeCun, Y., Bengio, Y., et al.: Convolutional networks for images, speech, and time series. Handb. Brain Theory Neural Netw. 3361(10), 1995 (1995)","journal-title":"Handb. Brain Theory Neural Netw."},{"key":"4_CR14","doi-asserted-by":"crossref","unstructured":"Lewis, D.D., Catlett, J.: Heterogeneous uncertainty sampling for supervised learning. In: Machine Learning Proceedings 1994, pp. 148\u2013156. Elsevier (1994)","DOI":"10.1016\/B978-1-55860-335-6.50026-X"},{"key":"4_CR15","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-1-4471-2099-5_1","volume-title":"SIGIR 1994","author":"DD Lewis","year":"1994","unstructured":"Lewis, D.D., Gale, W.A.: A sequential algorithm for training text classifiers. In: Croft, B.W., van Rijsbergen, C.J. (eds.) SIGIR 1994, pp. 3\u201312. Springer, London (1994). https:\/\/doi.org\/10.1007\/978-1-4471-2099-5_1"},{"key":"4_CR16","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"740","DOI":"10.1007\/978-3-319-10602-1_48","volume-title":"Computer Vision \u2013 ECCV 2014","author":"T-Y Lin","year":"2014","unstructured":"Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740\u2013755. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10602-1_48"},{"key":"4_CR17","doi-asserted-by":"crossref","unstructured":"Rei\u00df, S., Seibold, C., Freytag, A., Rodner, E., Stiefelhagen, R.: Every annotation counts: multi-label deep supervision for medical image segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9532\u20139542 (2021)","DOI":"10.1109\/CVPR46437.2021.00941"},{"key":"4_CR18","unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. Adv. Neural Inf. Process. Syst. 28 (2015)"},{"key":"4_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"232","DOI":"10.1007\/978-3-642-15986-2_24","volume-title":"Pattern Recognition","author":"E Rodner","year":"2010","unstructured":"Rodner, E., Denzler, J.: One-shot learning of object categories using dependent Gaussian processes. In: Goesele, M., Roth, S., Kuijper, A., Schiele, B., Schindler, K. (eds.) DAGM 2010. LNCS, vol. 6376, pp. 232\u2013241. Springer, Heidelberg (2010). https:\/\/doi.org\/10.1007\/978-3-642-15986-2_24"},{"key":"4_CR20","unstructured":"Rodner, E., Hoffman, J., Donahue, J., Darrell, T., Saenko, K.: Towards adapting imagenet to reality: scalable domain adaptation with implicit low-rank transformations. arXiv preprint arXiv:1308.4200 (2013)"},{"key":"4_CR21","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"413","DOI":"10.1007\/11871842_40","volume-title":"Machine Learning: ECML 2006","author":"D Roth","year":"2006","unstructured":"Roth, D., Small, K.: Margin-based active learning for structured output spaces. In: F\u00fcrnkranz, J., Scheffer, T., Spiliopoulou, M. (eds.) ECML 2006. LNCS (LNAI), vol. 4212, pp. 413\u2013424. Springer, Heidelberg (2006). https:\/\/doi.org\/10.1007\/11871842_40"},{"key":"4_CR22","unstructured":"Roy, S., Unmesh, A., Namboodiri, V.P.: Deep active learning for object detection. In: Proceedings of the British Machine Vision Conference (BMVC), p. 91 (2018)"},{"key":"4_CR23","unstructured":"Sener, O., Savarese, S.: Active learning for convolutional neural networks: a core-set approach. In: International Conference on Learning Representations (ICLR) (2017)"},{"key":"4_CR24","unstructured":"Wu, Y., Kirillov, A., Massa, F., Lo, W.Y., Girshick, R.: Detectron2 (2019). https:\/\/github.com\/facebookresearch\/detectron2"},{"key":"4_CR25","doi-asserted-by":"crossref","unstructured":"Yu, W., Zhu, S., Yang, T., Chen, C.: Consistency-based active learning for object detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3951\u20133960 (2022)","DOI":"10.1109\/CVPRW56347.2022.00440"},{"key":"4_CR26","doi-asserted-by":"crossref","unstructured":"Yuan, T., et al.: Multiple instance active learning for object detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5330\u20135339 (2021)","DOI":"10.1109\/CVPR46437.2021.00529"},{"key":"4_CR27","unstructured":"Zhdanov, F.: Diverse mini-batch active learning. arXiv preprint arXiv:1901.05954 (2019)"},{"key":"4_CR28","doi-asserted-by":"crossref","unstructured":"Zheng, M., You, S., Huang, L., Wang, F., Qian, C., Xu, C.: SimMatch: semi-supervised learning with similarity matching. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 14471\u201314481 (2022)","DOI":"10.1109\/CVPR52688.2022.01407"}],"container-title":["Lecture Notes in Computer Science","Software Engineering and Formal Methods. SEFM 2022 Collocated Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-26236-4_4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,10]],"date-time":"2023-02-10T15:59:37Z","timestamp":1676044777000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-26236-4_4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031262357","9783031262364"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-26236-4_4","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"11 February 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"SEFM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Software Engineering and Formal Methods","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Berlin","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Germany","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"sefm2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/sefm-conference.github.io\/2022\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"39","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"19","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"9","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"49% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}