{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T20:55:46Z","timestamp":1743022546127,"version":"3.40.3"},"publisher-location":"Cham","reference-count":35,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031263125"},{"type":"electronic","value":"9783031263132"}],"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_28","type":"book-chapter","created":{"date-parts":[[2023,3,1]],"date-time":"2023-03-01T08:02:32Z","timestamp":1677657752000},"page":"460-476","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["MGRLN-Net: Mask-Guided Residual Learning Network for\u00a0Joint Single-Image Shadow Detection and\u00a0Removal"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9387-8477","authenticated-orcid":false,"given":"Leiping","family":"Jie","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1681-7926","authenticated-orcid":false,"given":"Hui","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,3,2]]},"reference":[{"key":"28_CR1","doi-asserted-by":"crossref","unstructured":"Chen, Z., Zhu, L., Wan, L., Wang, S., Feng, W., Heng, P.A.: A multi-task mean teacher for semi-supervised shadow detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5611\u20135620 (2020)","DOI":"10.1109\/CVPR42600.2020.00565"},{"key":"28_CR2","doi-asserted-by":"crossref","unstructured":"Chen, Z., Long, C., Zhang, L., Xiao, C.: CANet: a context-aware network for shadow removal. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 4743\u20134752 (2021)","DOI":"10.1109\/ICCV48922.2021.00470"},{"key":"28_CR3","doi-asserted-by":"crossref","unstructured":"Cun, X., Pun, C.M., Shi, C.: Towards ghost-free shadow removal via dual hierarchical aggregation network and shadow matting GAN. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 10680\u201310687 (2020)","DOI":"10.1609\/aaai.v34i07.6695"},{"key":"28_CR4","doi-asserted-by":"crossref","unstructured":"Ding, B., Long, C., Zhang, L., Xiao, C.: ARGAN: attentive recurrent generative adversarial network for shadow detection and removal. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV) (2019)","DOI":"10.1109\/ICCV.2019.01031"},{"issue":"1","key":"28_CR5","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1007\/s11263-009-0243-z","volume":"85","author":"GD Finlayson","year":"2009","unstructured":"Finlayson, G.D., Drew, M.S., Lu, C.: Entropy minimization for shadow removal. Int. J. Comput. Vision 85(1), 35\u201357 (2009)","journal-title":"Int. J. Comput. Vision"},{"issue":"1","key":"28_CR6","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1109\/TPAMI.2006.18","volume":"28","author":"GD Finlayson","year":"2006","unstructured":"Finlayson, G.D., Hordley, S.D., Lu, C., Drew, M.S.: On the removal of shadows from images. IEEE Trans. Pattern Anal. Mach. Intell. 28(1), 59\u201368 (2006). https:\/\/doi.org\/10.1109\/TPAMI.2006.18","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"28_CR7","doi-asserted-by":"crossref","unstructured":"Fu, L., et al.: Auto-exposure fusion for single-image shadow removal. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 10571\u201310580 (2021)","DOI":"10.1109\/CVPR46437.2021.01043"},{"key":"28_CR8","unstructured":"Goodfellow, I., et al.: Generative adversarial nets. In: Proceedings of International Conference on Neural Information Processing Systems (NeurIPS), pp. 2672\u20132680 (2014)"},{"issue":"5","key":"28_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2732407","volume":"34","author":"M Gryka","year":"2015","unstructured":"Gryka, M., Terry, M., Brostow, G.J.: Learning to remove soft shadows. ACM Trans. Graph. (TOG) 34(5), 1\u201315 (2015)","journal-title":"ACM Trans. Graph. (TOG)"},{"issue":"12","key":"28_CR10","doi-asserted-by":"publisher","first-page":"2956","DOI":"10.1109\/TPAMI.2012.214","volume":"35","author":"R Guo","year":"2012","unstructured":"Guo, R., Dai, Q., Hoiem, D.: Paired regions for shadow detection and removal. IEEE Trans. Pattern Anal. Mach. Intell. 35(12), 2956\u20132967 (2012)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"11","key":"28_CR11","doi-asserted-by":"publisher","first-page":"2795","DOI":"10.1109\/TPAMI.2019.2919616","volume":"42","author":"X Hu","year":"2019","unstructured":"Hu, X., Fu, C.W., Zhu, L., Qin, J., Heng, P.A.: Direction-aware spatial context features for shadow detection and removal. IEEE Trans. Pattern Anal. Mach. Intell. 42(11), 2795\u20132808 (2019)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"28_CR12","doi-asserted-by":"crossref","unstructured":"Hu, X., Jiang, Y., Fu, C.W., Heng, P.A.: Mask-shadowGAN: learning to remove shadows from unpaired data. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 2472\u20132481 (2019)","DOI":"10.1109\/ICCV.2019.00256"},{"key":"28_CR13","doi-asserted-by":"crossref","unstructured":"Hu, X., Zhu, L., Fu, C.W., Qin, J., Heng, P.A.: Direction-aware spatial context features for shadow detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7454\u20137462 (2018)","DOI":"10.1109\/CVPR.2018.00778"},{"issue":"11","key":"28_CR14","doi-asserted-by":"publisher","first-page":"4187","DOI":"10.1109\/TCSVT.2020.3047977","volume":"31","author":"N Inoue","year":"2021","unstructured":"Inoue, N., Yamasaki, T.: Learning from synthetic shadows for shadow detection and removal. IEEE Trans. Circuits Syst. Video Technol. 31(11), 4187\u20134197 (2021). https:\/\/doi.org\/10.1109\/TCSVT.2020.3047977","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"28_CR15","doi-asserted-by":"crossref","unstructured":"Jie, L., Zhang, H.: A fast and efficient network for single image shadow detection. In: ICASSP 2022\u20132022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2634\u20132638 (2022)","DOI":"10.1109\/ICASSP43922.2022.9746703"},{"key":"28_CR16","doi-asserted-by":"crossref","unstructured":"Jie, L., Zhang, H.: RMLANet: random multi-level attention network for shadow detection. In: 2022 IEEE International Conference on Multimedia and Expo (ICME), pp. 1\u20136 (2022)","DOI":"10.1109\/ICME52920.2022.9860013"},{"key":"28_CR17","doi-asserted-by":"crossref","unstructured":"Jin, Y., Sharma, A., Tan, R.T.: DC-ShadowNet: single-image hard and soft shadow removal using unsupervised domain-classifier guided network. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 5027\u20135036 (2021)","DOI":"10.1109\/ICCV48922.2021.00498"},{"key":"28_CR18","doi-asserted-by":"crossref","unstructured":"Khan, S.H., Bennamoun, M., Sohel, F., Togneri, R.: Automatic feature learning for robust shadow detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1939\u20131946 (2014)","DOI":"10.1109\/CVPR.2014.249"},{"key":"28_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"322","DOI":"10.1007\/978-3-642-15552-9_24","volume-title":"Computer Vision \u2013 ECCV 2010","author":"J-F Lalonde","year":"2010","unstructured":"Lalonde, J.-F., Efros, A.A., Narasimhan, S.G.: Detecting ground shadows in outdoor consumer photographs. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6312, pp. 322\u2013335. Springer, Heidelberg (2010). https:\/\/doi.org\/10.1007\/978-3-642-15552-9_24"},{"key":"28_CR20","doi-asserted-by":"crossref","unstructured":"Le, H., Samaras, D.: Shadow removal via shadow image decomposition. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 8578\u20138587 (2019)","DOI":"10.1109\/ICCV.2019.00867"},{"key":"28_CR21","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"264","DOI":"10.1007\/978-3-030-58621-8_16","volume-title":"Computer Vision \u2013 ECCV 2020","author":"H Le","year":"2020","unstructured":"Le, H., Samaras, D.: From shadow segmentation to shadow removal. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12356, pp. 264\u2013281. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58621-8_16"},{"key":"28_CR22","doi-asserted-by":"crossref","unstructured":"Nguyen, V., Vicente, T.F.Y., Zhao, M., Hoai, M., Samaras, D.: Shadow detection with conditional generative adversarial networks. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 4510\u20134518 (2017)","DOI":"10.1109\/ICCV.2017.483"},{"key":"28_CR23","doi-asserted-by":"crossref","unstructured":"Qu, L., Tian, J., He, S., Tang, Y., Lau, R.W.H.: DeshadowNet: a multi-context embedding deep network for shadow removal. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4067\u20134075 (2017)","DOI":"10.1109\/CVPR.2017.248"},{"key":"28_CR24","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention (MICCAI) (2015)","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"28_CR25","doi-asserted-by":"crossref","unstructured":"Shen, L., Chua, T.W., Leman, K.: Shadow optimization from structured deep edge detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2067\u20132074 (2015)","DOI":"10.1109\/CVPR.2015.7298818"},{"key":"28_CR26","unstructured":"Shi, X., Chen, Z., Wang, H., Yeung, D.Y., Wong, W.K., Woo, W.C.: Convolutional lstm network: a machine learning approach for precipitation nowcasting. In: Advances in Neural Information Processing Systems 28 (2015)"},{"key":"28_CR27","unstructured":"Tan, M., Le, Q.: EfficientNet: rethinking model scaling for convolutional neural networks. In: Proceedings of the 36th International Conference on Machine Learning (ICML), pp. 6105\u20136114 (2019)"},{"issue":"3","key":"28_CR28","doi-asserted-by":"publisher","first-page":"682","DOI":"10.1109\/TPAMI.2017.2691703","volume":"40","author":"TFY Vicente","year":"2018","unstructured":"Vicente, T.F.Y., Hoai, M., Samaras, D.: Leave-one-out kernel optimization for shadow detection and removal. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 682\u2013695 (2018)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"28_CR29","doi-asserted-by":"crossref","unstructured":"Wang, J., Li, X., Yang, J.: Stacked conditional generative adversarial networks for jointly learning shadow detection and shadow removal. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1788\u20131797 (2018)","DOI":"10.1109\/CVPR.2018.00192"},{"key":"28_CR30","first-page":"6105","volume":"32","author":"C Xiao","year":"2019","unstructured":"Xiao, C., She, R., Xiao, D., Ma, K.L.: Fast shadow removal using adaptive multi-scale illumination transfer. Comput. Graph. Forum 32, 6105\u20136114 (2019)","journal-title":"Comput. Graph. Forum"},{"issue":"10","key":"28_CR31","doi-asserted-by":"publisher","first-page":"4361","DOI":"10.1109\/TIP.2012.2208976","volume":"21","author":"Q Yang","year":"2012","unstructured":"Yang, Q., Tan, K.H., Ahuja, N.: Shadow removal using bilateral filtering. IEEE Trans. Image Process. 21(10), 4361\u20134368 (2012)","journal-title":"IEEE Trans. Image Process."},{"issue":"11","key":"28_CR32","doi-asserted-by":"publisher","first-page":"4623","DOI":"10.1109\/TIP.2015.2465159","volume":"24","author":"L Zhang","year":"2015","unstructured":"Zhang, L., Zhang, Q., Xiao, C.: Shadow remover: image shadow removal based on illumination recovering optimization. IEEE Trans. Image Process. 24(11), 4623\u20134636 (2015)","journal-title":"IEEE Trans. Image Process."},{"key":"28_CR33","doi-asserted-by":"crossref","unstructured":"Zheng, Q., Qiao, X., Cao, Y., Lau, R.W.: Distraction-aware shadow detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5167\u20135176 (2019)","DOI":"10.1109\/CVPR.2019.00531"},{"key":"28_CR34","doi-asserted-by":"crossref","unstructured":"Zhu, J., Samuel, K.G., Masood, S.Z., Tappen, M.F.: Learning to recognize shadows in monochromatic natural images. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 223\u2013230 (2010)","DOI":"10.1109\/CVPR.2010.5540209"},{"key":"28_CR35","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1007\/978-3-030-01231-1_8","volume-title":"Computer Vision \u2013 ECCV 2018","author":"L Zhu","year":"2018","unstructured":"Zhu, L., et al.: Bidirectional feature pyramid network with recurrent attention residual modules for shadow detection. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11210, pp. 122\u2013137. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01231-1_8"}],"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_28","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,15]],"date-time":"2024-10-15T12:39:55Z","timestamp":1728995995000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-26313-2_28"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031263125","9783031263132"],"references-count":35,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-26313-2_28","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":"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)"}}]}}