{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,23]],"date-time":"2024-09-23T04:29:54Z","timestamp":1727065794995},"publisher-location":"Cham","reference-count":52,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031200557"},{"type":"electronic","value":"9783031200564"}],"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"},{"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\/978-3-031-20056-4_9","type":"book-chapter","created":{"date-parts":[[2022,11,2]],"date-time":"2022-11-02T19:31:54Z","timestamp":1667417514000},"page":"143-159","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Open-Set Semi-Supervised Object Detection"],"prefix":"10.1007","author":[{"given":"Yen-Cheng","family":"Liu","sequence":"first","affiliation":[]},{"given":"Chih-Yao","family":"Ma","sequence":"additional","affiliation":[]},{"given":"Xiaoliang","family":"Dai","sequence":"additional","affiliation":[]},{"given":"Junjiao","family":"Tian","sequence":"additional","affiliation":[]},{"given":"Peter","family":"Vajda","sequence":"additional","affiliation":[]},{"given":"Zijian","family":"He","sequence":"additional","affiliation":[]},{"given":"Zsolt","family":"Kira","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,11,3]]},"reference":[{"key":"9_CR1","unstructured":"Berthelot, D., Carlini, N., Goodfellow, I., Papernot, N., Oliver, A., Raffel, C.A.: Mixmatch: a holistic approach to semi-supervised learning. In: Advances in Neural Information Processing Systems (NeurIPS), pp. 5049\u20135059 (2019)"},{"key":"9_CR2","doi-asserted-by":"crossref","unstructured":"Caron, M., et al.: Emerging properties in self-supervised vision transformers. arXiv preprint arXiv:2104.14294 (2021)","DOI":"10.1109\/ICCV48922.2021.00951"},{"key":"9_CR3","unstructured":"Chen, K., et al.: MMDetection: open MMLab detection toolbox and benchmark. arXiv preprint arXiv:1906.07155 (2019)"},{"key":"9_CR4","doi-asserted-by":"crossref","unstructured":"Dhamija, A., Gunther, M., Ventura, J., Boult, T.: The overlooked elephant of object detection: open set. In: Proceedings of the IEEE Winter Conference on Applications of Computer Vision (WACV) (2020)","DOI":"10.1109\/WACV45572.2020.9093355"},{"key":"9_CR5","unstructured":"Dosovitskiy, A., et al.: An image is worth 16x16 words: transformers for image recognition at scale. In: Proceedings of the International Conference on Learning Representations (ICLR) (2021)"},{"key":"9_CR6","unstructured":"Du, X., Wang, Z., Cai, M., Li, Y.: Vos: learning what you don\u2019t know by virtual outlier synthesis. arXiv preprint arXiv:2202.01197 (2022)"},{"key":"9_CR7","unstructured":"Fort, S., Ren, J., Lakshminarayanan, B.: Exploring the limits of out-of-distribution detection. In: Advances in Neural Information Processing Systems (NeurIPS) (2021)"},{"key":"9_CR8","doi-asserted-by":"crossref","unstructured":"Girish, S., Suri, S., Rambhatla, S.S., Shrivastava, A.: Towards discovery and attribution of open-world GAN generated images. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 14094\u201314103 (2021)","DOI":"10.1109\/ICCV48922.2021.01383"},{"key":"9_CR9","unstructured":"Gu, X., Lin, T.Y., Kuo, W., Cui, Y.: Open-vocabulary object detection via vision and language knowledge distillation. In: Proceedings of the International Conference on Learning Representations (ICLR) (2022)"},{"key":"9_CR10","doi-asserted-by":"crossref","unstructured":"Guo, H., Mao, Y., Zhang, R.: MixUP as locally linear out-of-manifold regularization. In: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), vol. 33, pp. 3714\u20133722 (2019)","DOI":"10.1609\/aaai.v33i01.33013714"},{"key":"9_CR11","unstructured":"Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: Proceedings of the International Conference on Learning Representations (ICLR) (2017)"},{"key":"9_CR12","unstructured":"Hendrycks, D., Mazeika, M., Dietterich, T.: Deep anomaly detection with outlier exposure. In: Proceedings of the International Conference on Learning Representations (ICLR) (2019)"},{"key":"9_CR13","unstructured":"Hendrycks, D., Mu, N., Cubuk, E.D., Zoph, B., Gilmer, J., Lakshminarayanan, B.: AugMix: a simple data processing method to improve robustness and uncertainty. In: Proceedings of the International Conference on Learning Representations (ICLR) (2020)"},{"key":"9_CR14","doi-asserted-by":"crossref","unstructured":"Hsu, Y.C., Shen, Y., Jin, H., Kira, Z.: Generalized ODIN: detecting out-of-distribution image without learning from out-of-distribution data. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2020)","DOI":"10.1109\/CVPR42600.2020.01096"},{"key":"9_CR15","doi-asserted-by":"crossref","unstructured":"Huang, J., et al.: Trash to treasure: harvesting OOD data with cross-modal matching for open-set semi-supervised learning. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 8310\u20138319 (2021)","DOI":"10.1109\/ICCV48922.2021.00820"},{"key":"9_CR16","doi-asserted-by":"crossref","unstructured":"Huynh, D., Kuen, J., Lin, Z., Gu, J., Elhamifar, E.: Open-vocabulary instance segmentation via robust cross-modal pseudo-labeling. arXiv preprint arXiv:2111.12698 (2021)","DOI":"10.1109\/CVPR52688.2022.00689"},{"key":"9_CR17","doi-asserted-by":"crossref","unstructured":"Joseph, K., Khan, S., Khan, F.S., Balasubramanian, V.N.: Towards open world object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2021)","DOI":"10.1109\/CVPR46437.2021.00577"},{"issue":"2","key":"9_CR18","doi-asserted-by":"publisher","first-page":"5453","DOI":"10.1109\/LRA.2022.3146922","volume":"7","author":"D Kim","year":"2022","unstructured":"Kim, D., Lin, T.Y., Angelova, A., Kweon, I.S., Kuo, W.: Learning open-world object proposals without learning to classify. IEEE Robot. Autom. Lett. 7(2), 5453\u20135460 (2022)","journal-title":"IEEE Robot. Autom. Lett."},{"key":"9_CR19","unstructured":"Krasin, I., et al.: Openimages: a public dataset for large-scale multi-label and multi-class image classification (2017). https:\/\/storage.googleapis.com\/openimages\/web\/index.html"},{"key":"9_CR20","unstructured":"Laine, S., Aila, T.: Temporal ensembling for semi-supervised learning. In: Proceedings of the International Conference on Learning Representations (ICLR) (2017)"},{"key":"9_CR21","unstructured":"Lee, K., Lee, K., Lee, H., Shin, J.: A simple unified framework for detecting out-of-distribution samples and adversarial attacks. In: Advances in Neural Information Processing Systems (NeurIPS) (2018)"},{"key":"9_CR22","unstructured":"Liang, S., Li, Y., Srikant, R.: Enhancing the reliability of out-of-distribution image detection in neural networks. In: Proceedings of the International Conference on Learning Representations (ICLR) (2018)"},{"key":"9_CR23","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., et al.: Microsoft coco: common objects in context. In: Proceedings of the European Conference on Computer Vision (ECCV) (2014)","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"9_CR24","unstructured":"Liu, W., Wang, X., Owens, J.D., Li, Y.: Energy-based out-of-distribution detection. In: Advances in Neural Information Processing Systems (NeurIPS) (2020)"},{"key":"9_CR25","unstructured":"Liu, Y.C., et al.: Unbiased teacher for semi-supervised object detection. In: Proceedings of the International Conference on Learning Representations (ICLR) (2021)"},{"key":"9_CR26","unstructured":"Luo, H., et al.: On the consistency training for open-set semi-supervised learning. arXiv preprint arXiv:2101.08237 (2021)"},{"key":"9_CR27","doi-asserted-by":"crossref","unstructured":"Miller, D., S\u00fcnderhauf, N., Milford, M., Dayoub, F.: Uncertainty for identifying open-set errors in visual object detection. arXiv preprint arXiv:2104.01328 (2021)","DOI":"10.1109\/LRA.2021.3123374"},{"key":"9_CR28","doi-asserted-by":"crossref","unstructured":"Mohseni, S., Pitale, M., Yadawa, J., Wang, Z.: Self-supervised learning for generalizable out-of-distribution detection. In: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI) (2020)","DOI":"10.1609\/aaai.v34i04.5966"},{"key":"9_CR29","unstructured":"Nalisnick, E., Matsukawa, A., Teh, Y.W., Gorur, D., Lakshminarayanan, B.: Do deep generative models know what they don\u2019t know? In: Proceedings of the International Conference on Learning Representations (ICLR) (2019)"},{"key":"9_CR30","unstructured":"Pidhorskyi, S., Almohsen, R., Adjeroh, D.A., Doretto, G.: Generative probabilistic novelty detection with adversarial autoencoders. In: Advances in Neural Information Processing Systems (NeurIPS) (2018)"},{"key":"9_CR31","unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in neural information processing systems (NeurIPS), pp. 91\u201399 (2015)"},{"key":"9_CR32","doi-asserted-by":"crossref","unstructured":"Sabokrou, M., Khalooei, M., Fathy, M., Adeli, E.: Adversarially learned one-class classifier for novelty detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)","DOI":"10.1109\/CVPR.2018.00356"},{"key":"9_CR33","doi-asserted-by":"crossref","unstructured":"Saito, K., Hu, P., Darrell, T., Saenko, K.: Learning to detect every thing in an open world. arXiv preprint arXiv:2112.01698 (2021)","DOI":"10.1007\/978-3-031-20053-3_16"},{"key":"9_CR34","unstructured":"Saito, K., Kim, D., Saenko, K.: OpenMatch: open-set consistency regularization for semi-supervised learning with outliers. In: Advances in Neural Information Processing Systems (NeurIPS) (2021)"},{"key":"9_CR35","unstructured":"Sajjadi, M., Javanmardi, M., Tasdizen, T.: Regularization with stochastic transformations and perturbations for deep semi-supervised learning. In: Advances in Neural Information Processing Systems (NeurIPS), pp. 1163\u20131171 (2016)"},{"key":"9_CR36","unstructured":"Sohn, K., et al.: FixMatch: simplifying semi-supervised learning with consistency and confidence. In: Advances in Neural Information Processing Systems (NeurIPS) (2020)"},{"key":"9_CR37","unstructured":"Sohn, K., Zhang, Z., Li, C.L., Zhang, H., Lee, C.Y., Pfister, T.: A simple semi-supervised learning framework for object detection. arXiv preprint arXiv:2005.04757 (2020)"},{"key":"9_CR38","doi-asserted-by":"crossref","unstructured":"Tang, Y., Chen, W., Luo, Y., Zhang, Y.: Humble teachers teach better students for semi-supervised object detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3132\u20133141 (2021)","DOI":"10.1109\/CVPR46437.2021.00315"},{"key":"9_CR39","unstructured":"Tarvainen, A., Valpola, H.: Mean teachers are better role models: weight-averaged consistency targets improve semi-supervised deep learning results. In: Advances in Neural Information Processing Systems (NeurIPS), pp. 1195\u20131204 (2017)"},{"key":"9_CR40","doi-asserted-by":"crossref","unstructured":"Thulasidasan, S., Thapa, S., Dhaubhadel, S., Chennupati, G., Bhattacharya, T., Bilmes, J.: An effective baseline for robustness to distributional shift. arXiv preprint arXiv:2105.07107 (2021)","DOI":"10.1109\/ICMLA52953.2021.00050"},{"key":"9_CR41","unstructured":"Tian, J., Yung, D., Hsu, Y.C., Kira, Z.: A geometric perspective towards neural calibration via sensitivity decomposition. In: Advances in Neural Information Processing Systems (NeurIPS) (2021)"},{"key":"9_CR42","unstructured":"Wu, Y., Kirillov, A., Massa, F., Lo, W.Y., Girshick, R.: Detectron2 (2019). https:\/\/github.com\/facebookresearch\/detectron2"},{"key":"9_CR43","doi-asserted-by":"crossref","unstructured":"Xu, M., et al.: End-to-end semi-supervised object detection with soft teacher. arXiv preprint arXiv:2106.09018 (2021)","DOI":"10.1109\/ICCV48922.2021.00305"},{"key":"9_CR44","doi-asserted-by":"crossref","unstructured":"Yang, Q., Wei, X., Wang, B., Hua, X.S., Zhang, L.: Interactive self-training with mean teachers for semi-supervised object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2021)","DOI":"10.1109\/CVPR46437.2021.00588"},{"key":"9_CR45","doi-asserted-by":"crossref","unstructured":"Yu, Q., Ikami, D., Irie, G., Aizawa, K.: Multi-task curriculum framework for open-set semi-supervised learning. In: Proceedings of the European Conference on Computer Vision (ECCV) (2020)","DOI":"10.1007\/978-3-030-58610-2_26"},{"key":"9_CR46","doi-asserted-by":"crossref","unstructured":"Yun, S., Han, D., Oh, S.J., Chun, S., Choe, J., Yoo, Y.: CutMix: regularization strategy to train strong classifiers with localizable features. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 6023\u20136032 (2019)","DOI":"10.1109\/ICCV.2019.00612"},{"key":"9_CR47","doi-asserted-by":"crossref","unstructured":"Zareian, A., Rosa, K.D., Hu, D.H., Chang, S.F.: Open-vocabulary object detection using captions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2021)","DOI":"10.1109\/CVPR46437.2021.01416"},{"key":"9_CR48","unstructured":"Zhang, H., Cisse, M., Dauphin, Y.N., Lopez-Paz, D.: mixup: beyond empirical risk minimization. In: Proceedings of the International Conference on Learning Representations (ICLR) (2018)"},{"key":"9_CR49","doi-asserted-by":"crossref","unstructured":"Zhou, Q., Yu, C., Wang, Z., Qian, Q., Li, H.: Instant-teaching: an end-to-end semi-supervised object detection framework. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2021)","DOI":"10.1109\/CVPR46437.2021.00407"},{"key":"9_CR50","doi-asserted-by":"crossref","unstructured":"Zhou, X., Girdhar, R., Joulin, A., Kr\u00e4henb\u00fchl, P., Misra, I.: Detecting twenty-thousand classes using image-level supervision. arXiv preprint arXiv:2201.02605 (2022)","DOI":"10.1007\/978-3-031-20077-9_21"},{"key":"9_CR51","doi-asserted-by":"crossref","unstructured":"Zhu, C., Chen, F., Shen, Z., Savvides, M.: Soft anchor-point object detection. In: Proceedings of the European Conference on Computer Vision (ECCV) (2020)","DOI":"10.1007\/978-3-030-58545-7_6"},{"key":"9_CR52","unstructured":"Zong, B., et al.: Deep autoencoding gaussian mixture model for unsupervised anomaly detection. In: Proceedings of the International Conference on Learning Representations (ICLR) (2018)"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-20056-4_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,11]],"date-time":"2023-03-11T00:38:57Z","timestamp":1678495137000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-20056-4_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031200557","9783031200564"],"references-count":52,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-20056-4_9","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":"3 November 2022","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":"Tel Aviv","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Israel","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":"23 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 October 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2022.ecva.net\/","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":"5804","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":"1645","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":"28% - 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":"3.21","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":"3.91","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)"}}]}}