{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T18:01:59Z","timestamp":1793124119008,"version":"4.5.0"},"publisher-location":"Cham","reference-count":45,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031263125","type":"print"},{"value":"9783031263132","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\/978-3-031-26313-2_30","type":"book-chapter","created":{"date-parts":[[2023,3,1]],"date-time":"2023-03-01T08:02:32Z","timestamp":1677657752000},"page":"491-507","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":112,"title":["DENet: Detection-driven Enhancement Network for\u00a0Object Detection Under Adverse Weather Conditions"],"prefix":"10.1007","author":[{"given":"Qingpao","family":"Qin","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kan","family":"Chang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mengyuan","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guiqing","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,3,2]]},"reference":[{"key":"30_CR1","doi-asserted-by":"crossref","unstructured":"Girshick, R.B., 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), pp. 580\u2013587. Columbus, OH, USA (2014)","DOI":"10.1109\/CVPR.2014.81"},{"key":"30_CR2","doi-asserted-by":"crossref","unstructured":"Girshick, R.B.: Fast R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 1440\u20131448. Santiago, Chile (2015)","DOI":"10.1109\/ICCV.2015.169"},{"key":"30_CR3","unstructured":"Ren, S., He, K., Girshick, R.B., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in neural information processing systems (NIPS), pp. 91\u201399. Montreal, Quebec, Canada (2015)"},{"key":"30_CR4","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., Girshick, R.B.: Mask R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 2980\u20132988. Venice, Italy (2017)","DOI":"10.1109\/ICCV.2017.322"},{"key":"30_CR5","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S.K., Girshick, R.B., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 779\u2013788. Las Vegas, NV, USA (2016)","DOI":"10.1109\/CVPR.2016.91"},{"key":"30_CR6","doi-asserted-by":"crossref","unstructured":"Redmon, J., Farhadi, A.: YOLO9000: better, faster, stronger. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6517\u20136525. Honolulu, HI, USA (2017)","DOI":"10.1109\/CVPR.2017.690"},{"key":"30_CR7","unstructured":"Redmon, J., Farhadi, A.: YOLOv3: an incremental improvement. arXiv preprint arXiv:1804.02767 (2018)"},{"key":"30_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1007\/978-3-319-46448-0_2","volume-title":"Computer Vision \u2013 ECCV 2016","author":"W Liu","year":"2016","unstructured":"Liu, W., et al.: SSD: single shot multibox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21\u201337. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46448-0_2"},{"issue":"2","key":"30_CR9","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","volume":"88","author":"M Everingham","year":"2010","unstructured":"Everingham, M., Gool, L.V., Williams, C.K.I., Winn, J.M., Zisserman, A.: The pascal visual object classes (VOC) challenge. Int. J. Comput. Vis. 88(2), 303\u2013338 (2010)","journal-title":"Int. J. Comput. Vis."},{"key":"30_CR10","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":"30_CR11","doi-asserted-by":"crossref","unstructured":"Chen, Y., Li, W., Sakaridis, C., Dai, D., Gool, L.V.: Domain adaptive faster R-CNN for object detection in the wild. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3339\u20133348. Salt Lake City, UT, USA (2018)","DOI":"10.1109\/CVPR.2018.00352"},{"key":"30_CR12","doi-asserted-by":"crossref","unstructured":"Zhu, X., Pang, J., Yang, C., Shi, J., Lin, D.: Adapting object detectors via selective cross-domain alignment. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 687\u2013696. Long Beach, CA, USA (2019)","DOI":"10.1109\/CVPR.2019.00078"},{"key":"30_CR13","doi-asserted-by":"crossref","unstructured":"Wang, T., Zhang, X., Yuan, L., Feng, J.: Few-shot adaptive faster R-CNN. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7173\u20137182. Long Beach, CA, USA (2019)","DOI":"10.1109\/CVPR.2019.00734"},{"key":"30_CR14","doi-asserted-by":"crossref","unstructured":"Saito, K., Ushiku, Y., Harada, T., Saenko, K.: Strong-weak distribution alignment for adaptive object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6956\u20136965. Long Beach, CA, USA (2019)","DOI":"10.1109\/CVPR.2019.00712"},{"key":"30_CR15","doi-asserted-by":"crossref","unstructured":"He, Z., Zhang, L.: Multi-adversarial faster-rcnn for unrestricted object detection. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 6667\u20136676. Seoul, Korea (South) (2019)","DOI":"10.1109\/ICCV.2019.00677"},{"key":"30_CR16","doi-asserted-by":"crossref","unstructured":"Hnewa, M., Radha, H.: Multiscale domain adaptive yolo for cross-domain object detection. In: Proceedings of the IEEE International Conference on Image Processing (ICIP), pp. 3323\u20133327. Anchorage, AK, USA (2021)","DOI":"10.1109\/ICIP42928.2021.9506039"},{"key":"30_CR17","unstructured":"Zhang, S., Tuo, H., Hu, J., Jing, Z.: Domain adaptive YOLO for one-stage cross-domain detection. In: Proceedings of the Asian Conference on Machine Learning (ACML), pp. 785\u2013797. Virtual Event (2021)"},{"key":"30_CR18","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"763","DOI":"10.1007\/978-3-030-58568-6_45","volume-title":"Computer Vision \u2013 ECCV 2020","author":"VA Sindagi","year":"2020","unstructured":"Sindagi, V.A., Oza, P., Yasarla, R., Patel, V.M.: Prior-based domain adaptive object detection for hazy and rainy conditions. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12359, pp. 763\u2013780. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58568-6_45"},{"key":"30_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1007\/978-3-030-58589-1_21","volume-title":"Computer Vision \u2013 ECCV 2020","author":"Y Sasagawa","year":"2020","unstructured":"Sasagawa, Y., Nagahara, H.: YOLO in the dark - domain adaptation method for merging multiple models. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12366, pp. 345\u2013359. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58589-1_21"},{"issue":"8","key":"30_CR20","first-page":"2623","volume":"43","author":"S Huang","year":"2021","unstructured":"Huang, S., Le, T., Jaw, D.: DSNet: joint semantic learning for object detection in inclement weather conditions. IEEE Trans. Pattern Anal. Mach. Intell. 43(8), 2623\u20132633 (2021)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"30_CR21","doi-asserted-by":"crossref","unstructured":"Cui, Z., Qi, G., Gu, L., You, S., Zhang, Z., Harada, T.: Multitask AET with orthogonal tangent regularity for dark object detection. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 2533\u20132542. Montreal, QC, Canada (2021)","DOI":"10.1109\/ICCV48922.2021.00255"},{"key":"30_CR22","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Zhang, J., Guo, X.: Kindling the darkness: a practical low-light image enhancer. In: Proceedings of the ACM International Conference on Multimedia (MM), pp. 1632\u20131640. Nice, France (2019)","DOI":"10.1145\/3343031.3350926"},{"key":"30_CR23","unstructured":"Wei, C., Wang, W., Yang, W., Liu, J.: Deep retinex decomposition for low-light enhancement. In: Proceedings of the British Machine Vision Conference (BMVC), p. 155. Newcastle, UK (2018)"},{"key":"30_CR24","doi-asserted-by":"crossref","unstructured":"Wu, W., Weng, J., Zhang, P., Wang, X., Yang, W., Jiang, J.: Uretinex-net: Retinex-based deep unfolding network for low-light image enhancement. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5901\u20135910. New Orleans, LA, USA (2022)","DOI":"10.1109\/CVPR52688.2022.00581"},{"key":"30_CR25","doi-asserted-by":"publisher","first-page":"2340","DOI":"10.1109\/TIP.2021.3051462","volume":"30","author":"Y Jiang","year":"2021","unstructured":"Jiang, Y., et al.: EnlightenGAN: deep light enhancement without paired supervision. IEEE Trans. Image Process. 30, 2340\u20132349 (2021)","journal-title":"IEEE Trans. Image Process."},{"key":"30_CR26","unstructured":"Lv, F., Lu, F., Wu, J., Lim, C.: MBLLEN: low-light image\/video enhancement using CNNs. In: Proceedings of the British Machine Vision Conference (BMVC), p. 220. Newcastle, UK (2018)"},{"key":"30_CR27","doi-asserted-by":"crossref","unstructured":"Guo, C., et al.: Zero-reference deep curve estimation for low-light image enhancement. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1777\u20131786. Seattle, WA, USA (2020)","DOI":"10.1109\/CVPR42600.2020.00185"},{"key":"30_CR28","doi-asserted-by":"crossref","unstructured":"Ma, L., Ma, T., Liu, R., Fan, X., Luo, Z.: Toward fast, flexible, and robust low-light image enhancement. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5637\u20135646. New Orleans, LA, USA (2022)","DOI":"10.1109\/CVPR52688.2022.00555"},{"key":"30_CR29","doi-asserted-by":"crossref","unstructured":"Liu, X., Ma, Y., Shi, Z., Chen, J.: GridDehazeNet: attention-based multi-scale network for image dehazing. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 7313\u20137322. Seoul, Korea (South) (2019)","DOI":"10.1109\/ICCV.2019.00741"},{"key":"30_CR30","doi-asserted-by":"crossref","unstructured":"Dong, H., Pan, J., Xiang, L., Hu, Z., Zhang, X., Wang, F., Yang, M.: Multi-scale boosted dehazing network with dense feature fusion. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2154\u20132164. Seattle, WA, USA (2020)","DOI":"10.1109\/CVPR42600.2020.00223"},{"key":"30_CR31","doi-asserted-by":"crossref","unstructured":"Zhang, H., Patel, V.M.: Densely connected pyramid dehazing network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3194\u20133203. Salt Lake City, UT, USA (2018)","DOI":"10.1109\/CVPR.2018.00337"},{"key":"30_CR32","doi-asserted-by":"crossref","unstructured":"Yang, Y., Wang, C., Liu, R., Zhang, L., Guo, X., Tao, D.: Self-augmented unpaired image dehazing via density and depth decomposition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2037\u20132046. New Orleans, LA, USA (2022)","DOI":"10.1109\/CVPR52688.2022.00208"},{"issue":"4","key":"30_CR33","doi-asserted-by":"publisher","first-page":"532","DOI":"10.1109\/TCOM.1983.1095851","volume":"31","author":"PJ Burt","year":"1983","unstructured":"Burt, P.J., Adelson, E.H.: The Laplacian pyramid as a compact image code. IEEE Trans. Commun. 31(4), 532\u2013540 (1983)","journal-title":"IEEE Trans. Commun."},{"issue":"4","key":"30_CR34","doi-asserted-by":"publisher","first-page":"1153","DOI":"10.1007\/s11263-020-01418-8","volume":"129","author":"J Liu","year":"2021","unstructured":"Liu, J., Xu, D., Yang, W., Fan, M., Huang, H.: Benchmarking low-light image enhancement and beyond. Int. J. Comput. Vision 129(4), 1153\u20131184 (2021)","journal-title":"Int. J. Comput. Vision"},{"key":"30_CR35","doi-asserted-by":"crossref","unstructured":"Liu, W., Ren, G., Yu, R., Guo, S., Zhu, J., Zhang, L.: Image-adaptive yolo for object detection in adverse weather conditions. In: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), vol. 36, pp. 1792\u20131800 (2022)","DOI":"10.1609\/aaai.v36i2.20072"},{"issue":"6","key":"30_CR36","first-page":"33","volume":"29","author":"EH Adelson","year":"1984","unstructured":"Adelson, E.H., Anderson, C.H., Bergen, J.R., Burt, P.J., Ogden, J.M.: Pyramid methods in image processing. RCA Eng. 29(6), 33\u201341 (1984)","journal-title":"RCA Eng."},{"key":"30_CR37","doi-asserted-by":"crossref","unstructured":"Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1\u20139. Boston, MA, USA (2015)","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"30_CR38","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-01234-2_1","volume-title":"Computer Vision \u2013 ECCV 2018","author":"S Woo","year":"2018","unstructured":"Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: CBAM: convolutional block attention module. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 3\u201319. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01234-2_1"},{"key":"30_CR39","doi-asserted-by":"crossref","unstructured":"Wang, X., Yu, K., Dong, C., Change Loy, C.: Recovering realistic texture in image super-resolution by deep spatial feature transform. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 606\u2013615. Salt Lake City, UT, USA (2018)","DOI":"10.1109\/CVPR.2018.00070"},{"key":"30_CR40","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: Proceedings of the International Conference on Learning Representations (ICLR), San Diego, CA, USA (2015)"},{"key":"30_CR41","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1016\/j.cviu.2018.10.010","volume":"178","author":"YP Loh","year":"2019","unstructured":"Loh, Y.P., Chan, C.S.: Getting to know low-light images with the exclusively dark dataset. Comput. Vis. Image Underst. 178, 30\u201342 (2019)","journal-title":"Comput. Vis. Image Underst."},{"issue":"1","key":"30_CR42","doi-asserted-by":"publisher","first-page":"492","DOI":"10.1109\/TIP.2018.2867951","volume":"28","author":"B Li","year":"2019","unstructured":"Li, B., et al.: Benchmarking single-image dehazing and beyond. IEEE Trans. Image Process. 28(1), 492\u2013505 (2019)","journal-title":"IEEE Trans. Image Process."},{"key":"30_CR43","unstructured":"McCartney, E.J.: Optics of the atmosphere: scattering by molecules and particles. New York (1976)"},{"key":"30_CR44","doi-asserted-by":"crossref","unstructured":"Nayar, S.K., Narasimhan, S.G.: Vision in bad weather. In: Proceedings of the Seventh IEEE International Conference on Computer Vision, vol. 2, pp. 820\u2013827. IEEE (1999)","DOI":"10.1109\/ICCV.1999.790306"},{"issue":"6","key":"30_CR45","doi-asserted-by":"publisher","first-page":"713","DOI":"10.1109\/TPAMI.2003.1201821","volume":"25","author":"SG Narasimhan","year":"2003","unstructured":"Narasimhan, S.G., Nayar, S.K.: Contrast restoration of weather degraded images. IEEE Trans. Pattern Anal. Mach. Intell. 25(6), 713\u2013724 (2003)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ACCV 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-26313-2_30","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,1]],"date-time":"2023-03-01T08:15:59Z","timestamp":1677658559000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-26313-2_30"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031263125","9783031263132"],"references-count":45,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-26313-2_30","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":"2 March 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ACCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Asian Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Macao","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","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":"4 December 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 December 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"accv2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.accv2022.org","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 Microsoft","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"836","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":"277","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":"33% - 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":"3.3","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"For the ACCV 2022 workshops 25 papers have been accepted from 40 submissions","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)"}}]}}