{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T02:22:48Z","timestamp":1777429368639,"version":"3.51.4"},"publisher-location":"Cham","reference-count":57,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031250712","type":"print"},{"value":"9783031250729","type":"electronic"}],"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\/978-3-031-25072-9_15","type":"book-chapter","created":{"date-parts":[[2023,2,17]],"date-time":"2023-02-17T08:40:04Z","timestamp":1676623204000},"page":"228-244","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":128,"title":["YOLO5Face: Why Reinventing a\u00a0Face Detector"],"prefix":"10.1007","author":[{"given":"Delong","family":"Qi","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2344-0773","authenticated-orcid":false,"given":"Weijun","family":"Tan","sequence":"additional","affiliation":[]},{"given":"Qi","family":"Yao","sequence":"additional","affiliation":[]},{"given":"Jingfeng","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,2,18]]},"reference":[{"key":"15_CR1","unstructured":"Bochkovskiy, A., Wang, C.Y., Liao, H.Y.M.: YOLOv4: optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934 (2020)"},{"key":"15_CR2","doi-asserted-by":"crossref","unstructured":"Cai, Z., Vasconcelos, N.: Cascade R-CNN: delving into high quality object detection. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00644"},{"key":"15_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1007\/978-3-030-58452-8_13","volume-title":"Computer Vision \u2013 ECCV 2020","author":"N Carion","year":"2020","unstructured":"Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12346, pp. 213\u2013229. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58452-8_13"},{"key":"15_CR4","unstructured":"Chen, K., et al.: MMDetection: open MMLab detection toolbox and benchmark. In: ECCV (2020)"},{"key":"15_CR5","unstructured":"Chi, C., Zhang, S., Xing, J., Lei, Z., Li, S.Z.: SRN - selective refinement network for high performance face detection. arXiv preprint arXiv:1809.02693 (2018)"},{"key":"15_CR6","doi-asserted-by":"crossref","unstructured":"Deng, J., Guo, J., Xue, N., Zafeiriou, S.: ArcFace: additive angular margin loss for deep face recognition. In: CVPR, June 2019","DOI":"10.1109\/CVPR.2019.00482"},{"key":"15_CR7","doi-asserted-by":"crossref","unstructured":"Deng, J., Guo, J., Zhou, Y., Yu, J., Kotsia, I., Zafeiriou, S.: RetinaFace: single-stage dense face localisation in the wild. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.00525"},{"key":"15_CR8","doi-asserted-by":"crossref","unstructured":"Duan, K., Bai, S., Xie, L., Qi, H., Huang, Q., Tian, Q.: CenterNet: keypoint triplets for object detection. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00667"},{"key":"15_CR9","doi-asserted-by":"crossref","unstructured":"Elfwinga, S., Uchibea, E., Doyab, K.: Sigmoid-weighted linear units for neural network function approximation in reinforcement learning. arXiv preprint arXiv:1702.03118 (2017)","DOI":"10.1016\/j.neunet.2017.12.012"},{"key":"15_CR10","doi-asserted-by":"crossref","unstructured":"Feng, Z., Kittler, J., Awais, M., Huber, P., Wu, X.: Wing loss for robust facial landmark localisation with convolutional neural networks. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00238"},{"key":"15_CR11","doi-asserted-by":"crossref","unstructured":"Girshick, R.: Fast R-CNN. In: Proceedings of the International Conference on Computer Vision (ICCV) (2015)","DOI":"10.1109\/ICCV.2015.169"},{"key":"15_CR12","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2014)","DOI":"10.1109\/CVPR.2014.81"},{"key":"15_CR13","unstructured":"Guo, J., Deng, J., Lattas, A., Zafeiriou, S.: Sample and computation redistribution for efficient face detection. arXiv preprint arXiv:2105.04714 (2021)"},{"key":"15_CR14","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"15_CR15","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., Girshick, R.: Mask R-CNN. In: Proceedings of the International Conference on Computer Vision (ICCV) (2017)","DOI":"10.1109\/ICCV.2017.322"},{"key":"15_CR16","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Sigmoid-weighted linear units for neural network function approximation in reinforcement learning. TPAMI (2015)"},{"key":"15_CR17","doi-asserted-by":"crossref","unstructured":"He, T., Zhang, Z., Zhang, H., Zhang, Z., Xie, J., Li, M.: Bag of tricks for image classification with convolutional neural networks. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00065"},{"key":"15_CR18","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Maaten, L., Weinberger, K.: Densely connected convolutional networks. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.243"},{"key":"15_CR19","doi-asserted-by":"crossref","unstructured":"Li, J., et al.: DSFD: dual shot face detector. arXiv preprint arXiv:1810.10220 (2018)","DOI":"10.1109\/CVPR.2019.00520"},{"key":"15_CR20","unstructured":"Li, Z., Tang, X., Han, J., Liu, J., He, Z.: PyramidBox++: high performance detector for finding tiny face. arXiv preprint arXiv:1904.00386 (2019)"},{"key":"15_CR21","doi-asserted-by":"crossref","unstructured":"Lin, T., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.106"},{"key":"15_CR22","doi-asserted-by":"crossref","unstructured":"Liu, S., Qi, L., Qin, H., Shi, J., Jia, J.: Path aggregation network for instance segmentation. arXiv preprint arXiv:1803.01534 (2018)","DOI":"10.1109\/CVPR.2018.00913"},{"key":"15_CR23","unstructured":"Liu, W., et al.: YOLOv3: an incremental improvement. In: ECCV (2016)"},{"key":"15_CR24","unstructured":"Liu, Y., Wang, F., Sun, B., Li, H.: MogFace: rethinking scale augmentation on the face detector. arXiv preprint arXiv:2103.11139 (2021)"},{"key":"15_CR25","doi-asserted-by":"crossref","unstructured":"Liu, Y., Tang, X., Wu, X., Han, J., Liu, J., Ding, E.: HAMBox: delving into online high-quality anchors mining for detecting outer faces. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.01306"},{"key":"15_CR26","doi-asserted-by":"crossref","unstructured":"Ma, M., Zhang, X., Zheng, H., Sun, J.: ShuffleNet V2: practical guidelines for efficient CNN architecture design. arXiv preprint ArXiv:1807.11164 (2018)","DOI":"10.1007\/978-3-030-01264-9_8"},{"key":"15_CR27","doi-asserted-by":"crossref","unstructured":"Qi, D., Hu, K., Tan, W., Yao, Q., Liu, J.: Balanced masked and standard face recognition. In: ICCV Workshops (2021)","DOI":"10.1109\/ICCVW54120.2021.00174"},{"key":"15_CR28","unstructured":"Redmon, J., Farhadi, A.: YOLOv3: an incremental improvement. arXiv preprint arXiv:1804.02767 (2015)"},{"key":"15_CR29","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.91"},{"key":"15_CR30","doi-asserted-by":"crossref","unstructured":"Redmon, J., Farhadi, A.: YOLO9000: better, faster, stronger. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.690"},{"key":"15_CR31","doi-asserted-by":"crossref","unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. (2016)","DOI":"10.1109\/TPAMI.2016.2577031"},{"key":"15_CR32","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, W., Zhmoginov, A., Chen, L.: MobileNetV2: inverted residuals and linear bottlenecks. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00474"},{"key":"15_CR33","doi-asserted-by":"crossref","unstructured":"Tan, M., Pang, R., Le, Q.: EfficientDet: scalable and efficient object detection. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"15_CR34","doi-asserted-by":"crossref","unstructured":"Tang, X., Du, D.K., He, Z., Liu, J.: PyramidBox: a context-assisted single shot face detector. arXiv preprint ArXiv:1803.07737 (2018)","DOI":"10.1007\/978-3-030-01240-3_49"},{"key":"15_CR35","unstructured":"Tian, W., Wang, Z., Shen, H., Deng, W., Chen, B., Zhang, X.: Learning better features for face detection with feature fusion and segmentation supervision. arXiv preprint arXiv:1811.08557 (2018)"},{"key":"15_CR36","unstructured":"Jain, V., Learned-Miller, E.: FDDB: a benchmark for face detection in unconstrained settings. University of Massachusetts Report (UM-CS-2010-009) (2010)"},{"key":"15_CR37","unstructured":"Wang, R.J., Li, X., Ling, C.X.: Pelee: a real-time object detection system on mobile devices. In: NeurIPS (2018)"},{"issue":"1","key":"15_CR38","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1109\/34.982883","volume":"24","author":"M Yang","year":"2002","unstructured":"Yang, M., Kriegman, D., Ahuja, N.: Detecting faces in images: a survey. TPAMI 24(1), 34\u201358 (2002)","journal-title":"TPAMI"},{"key":"15_CR39","unstructured":"Yang, S., Luo, P., Loy, C.C., Tang, X.: Wider face: a face detection benchmark. shuoyang1213.me\/WIDERFACE\/index.html"},{"key":"15_CR40","doi-asserted-by":"crossref","unstructured":"Yang, S., Luo, P., Loy, C.C., Tang, X.: Wider face: a face detection benchmark. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.596"},{"key":"15_CR41","unstructured":"Yashunin, D., Baydasov, T., Vlasov, R.: MaskFace: multi-task face and landmark detector. arXiv preprint arXiv:2005.09412 (2020)"},{"key":"15_CR42","unstructured":"YOLOv5. github.com\/ultralytics\/yolov5"},{"key":"15_CR43","unstructured":"Zhang, B., et al.: Automatic and scalable face detector. arXiv preprint arXiv:2003.11228 (2020)"},{"issue":"10","key":"15_CR44","doi-asserted-by":"publisher","first-page":"1499","DOI":"10.1109\/LSP.2016.2603342","volume":"23","author":"K Zhang","year":"2016","unstructured":"Zhang, K., Zhang, Z., Li, Z., Qiao, Y.: Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Sig. Process. Lett. 23(10), 1499\u20131503 (2016)","journal-title":"IEEE Sig. Process. Lett."},{"key":"15_CR45","unstructured":"Zhang, S., Chi, C., Lei, Z., Li, S.: RefineFace: refinement neural network for high performance face detection. arXiv preprint arXiv:1909.04376 (2019)"},{"key":"15_CR46","unstructured":"Zhang, S., et al.: ISRN - improved selective refinement network for face detection. arXiv preprint arXiv:1901.06651 (2019)"},{"key":"15_CR47","doi-asserted-by":"crossref","unstructured":"Zhang, S., Zhu, X., Lei, Z., Shi, H., Wang, X., Li, S.Z.: S$$^3$$FD: single shot scale-invariant face detector. In: ICCV (2017)","DOI":"10.1109\/ICCV.2017.30"},{"key":"15_CR48","doi-asserted-by":"crossref","unstructured":"Zhang, S., Zhu, X., Lei, Z., Shi, H., Wang, X., Li, S.Z.: FaceBoxes: a CPU real-time face detector with high accuracy. In: IJCB (2017)","DOI":"10.1109\/BTAS.2017.8272675"},{"key":"15_CR49","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zhou, X., Lin, M., Sun, J.: ShuffleNet: an extremely efficient convolutional neural network for mobile devices. arXiv preprint arXiv:1707.01083 (2017)","DOI":"10.1109\/CVPR.2018.00716"},{"key":"15_CR50","unstructured":"Zhang, Y., Xu, X., Liu, X.: Robust and high performance face detector. arXiv preprint arXiv:1901.02350 (2019)"},{"key":"15_CR51","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1023\/B:VISI.0000013087.49260.fb","volume":"57","author":"C Zhang","year":"2004","unstructured":"Zhang, C., Zhang, Z.: Robust real-time face detection. IJCV 57, 137\u2013154 (2004). https:\/\/doi.org\/10.1023\/B:VISI.0000013087.49260.fb","journal-title":"IJCV"},{"key":"15_CR52","unstructured":"Zhang, C., Zhang, Z.: A survey of recent advances in face detection. Technical report, Microsoft Research (2010)"},{"key":"15_CR53","unstructured":"Zhao, R., Liu, T., Xiao, J., Lun, D.P.K., Lam, K.M.: Deep multi-task learning for facial expression recognition and synthesis based on selective feature sharing. In: ICPR (2020)"},{"key":"15_CR54","unstructured":"Zhou, X., Wang, D., Philipp, K.: Objects as points. arXiv preprint arXiv:1904.07850 (2019)"},{"key":"15_CR55","unstructured":"Zhu, Y., Cai, H., Zhang, S., Wang, C., Xiong, W.: TinaFace: strong but simple baseline for face detection. arXiv preprint arXiv:2011.13183 (2020)"},{"key":"15_CR56","doi-asserted-by":"crossref","unstructured":"Zhu, Z., et al.: WebFace260M: a benchmark unveiling the power of million-scale deep face recognition. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.01035"},{"key":"15_CR57","unstructured":"Zhu, Z., et al.: Masked face recognition challenge: the WebFace260M track report. In: ICCV Workshops (2021)"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2022 Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-25072-9_15","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T15:37:25Z","timestamp":1710257845000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-25072-9_15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031250712","9783031250729"],"references-count":57,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-25072-9_15","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"18 February 2023","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)"}},{"value":"From the workshops, 367 reviewed full papers have been selected for publication","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}