{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T18:45:17Z","timestamp":1761677117059,"version":"3.40.3"},"publisher-location":"Cham","reference-count":34,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031167874"},{"type":"electronic","value":"9783031167881"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-16788-1_36","type":"book-chapter","created":{"date-parts":[[2022,9,22]],"date-time":"2022-09-22T20:35:56Z","timestamp":1663878956000},"page":"594-606","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Improving Traffic Sign Recognition by\u00a0Active Search"],"prefix":"10.1007","author":[{"given":"Sami","family":"Jaghouar","sequence":"first","affiliation":[]},{"given":"Hannes","family":"Gustafsson","sequence":"additional","affiliation":[]},{"given":"Bernhard","family":"Mehlig","sequence":"additional","affiliation":[]},{"given":"Erik","family":"Werner","sequence":"additional","affiliation":[]},{"given":"Niklas","family":"Gustafsson","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,9,20]]},"reference":[{"key":"36_CR1","unstructured":"https:\/\/en.wikipedia.org\/wiki\/Comparison_of_European_road_signs. Accessed 21 Sept 2021"},{"key":"36_CR2","unstructured":"Andrychowicz, M., et al.: Learning to learn by gradient descent by gradient descent. In: Proceedings of the 30th International Conference on Neural Information Processing Systems. NIPS 16, pp. 3988\u20133996. Curran Associates Inc., Red Hook (2016)"},{"key":"36_CR3","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1016\/j.neunet.2018.07.011","volume":"106","author":"M Buda","year":"2018","unstructured":"Buda, M., Maki, A., Mazurowski, M.A.: A systematic study of the class imbalance problem in convolutional neural networks. Neural Netw. 106, 249\u2013259 (2018). https:\/\/doi.org\/10.1016\/j.neunet.2018.07.011","journal-title":"Neural Netw."},{"key":"36_CR4","doi-asserted-by":"publisher","unstructured":"Budd, S., Robinson, E.C., Kainz, B.: A survey on active learning and human-in-the-loop deep learning for medical image analysis. Med. Image Anal. 71, 102062 (2021). https:\/\/doi.org\/10.1016\/j.media.2021.102062, arXiv:1910.02923","DOI":"10.1016\/j.media.2021.102062"},{"key":"36_CR5","unstructured":"Chen, W.Y., Liu, Y.C., Kira, Z., Wang, Y.C.F., Huang, J.B.: A closer look at few-shot classification. In: International Conference on Learning Representations (2019)"},{"key":"36_CR6","doi-asserted-by":"publisher","unstructured":"Chowdhury, A., Jiang, M., Chaudhuri, S., Jermaine, C.: Few-shot image classification: just use a library of pre-trained feature extractors and a simple classifier. In: 2021 IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 9425\u20139434 (2021). https:\/\/doi.org\/10.1109\/ICCV48922.2021.00931","DOI":"10.1109\/ICCV48922.2021.00931"},{"key":"36_CR7","doi-asserted-by":"crossref","unstructured":"Coleman, C., et al.: Similarity search for efficient active learning and search of rare concepts. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, pp. 6402\u20136410 (2022)","DOI":"10.1609\/aaai.v36i6.20591"},{"key":"36_CR8","doi-asserted-by":"publisher","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248\u2013255 (2009). https:\/\/doi.org\/10.1109\/CVPR.2009.5206848","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"36_CR9","doi-asserted-by":"publisher","unstructured":"Ertler, C., Mislej, J., Ollmann, T., Porzi, L., Neuhold, G., Kuang, Y.: The Mapillary traffic sign dataset for detection and classification on a global scale. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.M. (eds.) Computer Vision \u2013 ECCV 2020. LNCS, vol. pp. 68\u201384. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58592-1_5, arXiv:1909.04422","DOI":"10.1007\/978-3-030-58592-1_5"},{"key":"36_CR10","unstructured":"Finn, C., Abbeel, P., Levine, S.: Model-agnostic meta-learning for fast adaptation of deep networks. In: Precup, D., Teh, Y.W. (eds.) Proceedings of the 34th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 70, pp. 1126\u20131135. PMLR (2017). arXiv:1703.03400"},{"key":"36_CR11","unstructured":"Gustafsson, H.: Searching for rare traffic signs. Master\u2019s thesis, Chalmers University of Technology (2021)"},{"key":"36_CR12","doi-asserted-by":"publisher","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770\u2013778 (2016). https:\/\/doi.org\/10.1109\/CVPR.2016.90, arXiv:1512.03385","DOI":"10.1109\/CVPR.2016.90"},{"key":"36_CR13","doi-asserted-by":"publisher","unstructured":"Hu, Y., Gripon, V., Pateux, S.: Leveraging the feature distribution in transfer-based few-shot learning. In: Farka\u0161, I., Masulli, P., Otte, S., Wermter, S. (eds.) Artificial Neural Networks and Machine Learning - ICANN 2021. LNCS, vol. , pp. 487\u2013499. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-86340-1_39, arXiv:2006.03806","DOI":"10.1007\/978-3-030-86340-1_39"},{"key":"36_CR14","unstructured":"Jaghour, S.: Finding a needle in a haystack, using deep learning to enrich a dataset with important edge cases. Master\u2019s thesis, University of Technology of Compi\u00e8gne (UTC) (2021)"},{"key":"36_CR15","unstructured":"Jiang, S., Garnett, R., Moseley, B.: Cost effective active search. In: Wallach, H., Larochelle, H., Beygelzimer, A., d\u2019Alch\u00e9-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 32. Curran Associates, Inc. (2019)"},{"key":"36_CR16","unstructured":"Jiang, S., Malkomes, G., Converse, G., Shofner, A., Moseley, B., Garnett, R.: Efficient nonmyopic active search. In: Precup, D., Teh, Y.W. (eds.) Proceedings of the 34th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 70, pp. 1714\u20131723. PMLR (2017)"},{"issue":"1","key":"36_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-019-0192-5","volume":"6","author":"JM Johnson","year":"2019","unstructured":"Johnson, J.M., Khoshgoftaar, T.M.: Survey on deep learning with class imbalance. J. Big Data 6(1), 1\u201354 (2019). https:\/\/doi.org\/10.1186\/s40537-019-0192-5","journal-title":"J. Big Data"},{"key":"36_CR18","unstructured":"Kingma, D., Ba, J.: Adam: a method for stochastic optimization. In: International Conference on Learning Representations (2014). arXiv:1412.6980"},{"key":"36_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"491","DOI":"10.1007\/978-3-030-58558-7_29","volume-title":"Computer Vision \u2013 ECCV 2020","author":"A Kolesnikov","year":"2020","unstructured":"Kolesnikov, A., et al.: Big transfer (BiT): general visual representation learning. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12350, pp. 491\u2013507. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58558-7_29"},{"key":"36_CR20","doi-asserted-by":"publisher","unstructured":"Marcel, S., Rodriguez, Y.: Torchvision the machine-vision package of torch. In: Proceedings of the 18th ACM International Conference on Multimedia. MM 2010, pp. 1485\u20131488. Association for Computing Machinery, New York (2010). https:\/\/doi.org\/10.1145\/1873951.1874254","DOI":"10.1145\/1873951.1874254"},{"key":"36_CR21","doi-asserted-by":"publisher","unstructured":"Mehlig, B.: Machine Learning with Neural Networks: An Introduction for Scientists and Engineers. Cambridge University Press, Cambridge (2021). https:\/\/doi.org\/10.1017\/9781108860604","DOI":"10.1017\/9781108860604"},{"key":"36_CR22","doi-asserted-by":"publisher","unstructured":"Nienhuser, D., Z\u00f6llner, J.M.: Batch-mode active learning for traffic sign recognition. In: 2013 IEEE Intelligent Vehicles Symposium (IV), pp. 541\u2013546 (2013). https:\/\/doi.org\/10.1109\/IVS.2013.6629523","DOI":"10.1109\/IVS.2013.6629523"},{"key":"36_CR23","doi-asserted-by":"crossref","unstructured":"Qi, H., Brown, M., Lowe, D.G.: Low-shot learning with imprinted weights. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5822\u20135830 (2018)","DOI":"10.1109\/CVPR.2018.00610"},{"key":"36_CR24","unstructured":"Raghu, A., Raghu, M., Bengio, S., Vinyals, O.: Rapid learning or feature reuse? Towards understanding the effectiveness of MAML. In: International Conference on Learning Representations (2020). https:\/\/openreview.net\/forum?id=rkgMkCEtPB"},{"key":"36_CR25","unstructured":"Ren, M., et al.: Meta-learning for semi-supervised few-shot classification. In: Proceedings of 6th International Conference on Learning Representations ICLR (2018)"},{"key":"36_CR26","doi-asserted-by":"publisher","unstructured":"Ren, P., et al.: A survey of deep active learning. ACM Comput. Surv. 54(9) (2021). https:\/\/doi.org\/10.1145\/3472291, arXiv:2009.00236","DOI":"10.1145\/3472291"},{"key":"36_CR27","unstructured":"Snell, J., Swersky, K., Zemel, R.: Prototypical networks for few-shot learning. In: Guyon, I., et al. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc. (2017). arXiv:1703.05175"},{"key":"36_CR28","doi-asserted-by":"publisher","unstructured":"Sung, F., Yang, Y., Zhang, L., Xiang, T., Torr, P.H., Hospedales, T.M.: Learning to compare: relation network for few-shot learning. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1199\u20131208 (2018). https:\/\/doi.org\/10.1109\/CVPR.2018.00131, arXiv:1711.06025","DOI":"10.1109\/CVPR.2018.00131"},{"key":"36_CR29","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"266","DOI":"10.1007\/978-3-030-58568-6_16","volume-title":"Computer Vision \u2013 ECCV 2020","author":"Y Tian","year":"2020","unstructured":"Tian, Y., Wang, Y., Krishnan, D., Tenenbaum, J.B., Isola, P.: Rethinking few-shot image classification: a good embedding is all you need? In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12359, pp. 266\u2013282. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58568-6_16"},{"key":"36_CR30","unstructured":"Vinyals, O., Blundell, C., Lillicrap, T., Kavukcuoglu, K., Wierstra, D.: Matching networks for one shot learning. In: Lee, D., Sugiyama, M., Luxburg, U., Guyon, I., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 29. Curran Associates, Inc. (2016), arXiv:1606.04080"},{"key":"36_CR31","doi-asserted-by":"publisher","unstructured":"Wang, Y., Yao, Q., Kwok, J.T., Ni, L.M.: Generalizing from a few examples: a survey on few-shot learning. ACM Comput. Surv. 53(3) (2020). https:\/\/doi.org\/10.1145\/3386252, arXiv:1904.05046","DOI":"10.1145\/3386252"},{"key":"36_CR32","unstructured":"Yue, Z., Zhang, H., Sun, Q., Hua, X.S.: Interventional few-shot learning. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS 2020. Curran Associates Inc., Red Hook (2020). arXiv:2009.13000"},{"key":"36_CR33","doi-asserted-by":"publisher","unstructured":"Yurtsever, E., Lambert, J., Carballo, A., Takeda, K.: A survey of autonomous driving: common practices and emerging technologies. IEEE Access 8, 58443\u201358469 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.2983149, arXiv:1906.05113","DOI":"10.1109\/ACCESS.2020.2983149"},{"key":"36_CR34","doi-asserted-by":"crossref","unstructured":"Zhai, X., Kolesnikov, A., Houlsby, N., Beyer, L.: Scaling vision transformers. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12104\u201312113 (2022)","DOI":"10.1109\/CVPR52688.2022.01179"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-16788-1_36","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,20]],"date-time":"2023-02-20T03:52:26Z","timestamp":1676865146000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-16788-1_36"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031167874","9783031167881"],"references-count":34,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-16788-1_36","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"20 September 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DAGM GCPR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"DAGM German Conference on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Konstanz","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":"27 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":"44","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dagm2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/gcpr-vmv-2022.uni-konstanz.de\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"78","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":"37","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":"0","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":"47% - 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":"2.6","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}