{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:52:31Z","timestamp":1742914351349,"version":"3.40.3"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031064296"},{"type":"electronic","value":"9783031064302"}],"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\/978-3-031-06430-2_23","type":"book-chapter","created":{"date-parts":[[2022,5,16]],"date-time":"2022-05-16T08:03:16Z","timestamp":1652688196000},"page":"275-286","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Panoptic Segmentation in\u00a0Industrial Environments Using Synthetic and\u00a0Real Data"],"prefix":"10.1007","author":[{"given":"Camillo","family":"Quattrocchi","sequence":"first","affiliation":[]},{"given":"Daniele","family":"Di Mauro","sequence":"additional","affiliation":[]},{"given":"Antonino","family":"Furnari","sequence":"additional","affiliation":[]},{"given":"Giovanni Maria","family":"Farinella","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,5,17]]},"reference":[{"issue":"12","key":"23_CR1","doi-asserted-by":"publisher","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","volume":"39","author":"V Badrinarayanan","year":"2017","unstructured":"Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(12), 2481\u20132495 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"5","key":"23_CR2","doi-asserted-by":"publisher","first-page":"744","DOI":"10.1109\/TCSVT.2015.2409731","volume":"25","author":"A Betancourt","year":"2015","unstructured":"Betancourt, A., Morerio, P., Regazzoni, C.S., Rauterberg, M.: The evolution of first person vision methods: a survey. IEEE Trans. Circuits Syst. Video Technol. 25(5), 744\u2013760 (2015)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"23_CR3","doi-asserted-by":"crossref","unstructured":"Chang, A., et al.: Matterport3D: learning from RGB-D data in indoor environments. In: International Conference on 3D Vision (3DV), pp. 667\u2013676 (2017)","DOI":"10.1109\/3DV.2017.00081"},{"key":"23_CR4","doi-asserted-by":"crossref","unstructured":"Chen, K., et al.: Hybrid task cascade for instance segmentation. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4974\u20134983 (2019)","DOI":"10.1109\/CVPR.2019.00511"},{"issue":"4","key":"23_CR5","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","volume":"40","author":"LC Chen","year":"2017","unstructured":"Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 834\u2013848 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"23_CR6","doi-asserted-by":"publisher","unstructured":"Csurka, G.: A comprehensive survey on domain adaptation for visual applications. In: Csurka, G. (ed.) Domain Adaptation in Computer Vision Applications. ACVPR, pp. 1\u201335. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-58347-1_1","DOI":"10.1007\/978-3-319-58347-1_1"},{"key":"23_CR7","doi-asserted-by":"crossref","unstructured":"Damen, D., et al.: The EPIC-KITCHENS dataset: collection, challenges and baselines. IEEE Trans. Pattern Anal. Mach. Intell. 43(11), 4125\u20134141 (2021)","DOI":"10.1109\/TPAMI.2020.2991965"},{"key":"23_CR8","doi-asserted-by":"crossref","unstructured":"Di Benedetto, M., Meloni, E., Amato, G., Falchi, F., Gennaro, C.: Learning safety equipment detection using virtual worlds. In: International Conference on Content-Based Multimedia Indexing (CBMI), pp. 1\u20136. IEEE (2019)","DOI":"10.1109\/CBMI.2019.8877466"},{"key":"23_CR9","doi-asserted-by":"crossref","unstructured":"Dutta, A., Zisserman, A.: The VIA annotation software for images, audio and video. In: Proceedings of the 27th ACM International Conference on Multimedia, pp. 2276\u20132279 (2019)","DOI":"10.1145\/3343031.3350535"},{"key":"23_CR10","doi-asserted-by":"crossref","unstructured":"Fabbri, M., et al.: Motsynth: how can synthetic data help pedestrian detection and tracking? In: Proceedings of the IEEE International Conference on Computer Vision, pp. 10849\u201310859 (2021)","DOI":"10.1109\/ICCV48922.2021.01067"},{"key":"23_CR11","doi-asserted-by":"crossref","unstructured":"Hu, Y.T., Chen, H.S., Hui, K., Huang, J.B., Schwing, A.G.: Sail-vos: semantic amodal instance level video object segmentation-a synthetic dataset and baselines. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3105\u20133115 (2019)","DOI":"10.1109\/CVPR.2019.00322"},{"key":"23_CR12","doi-asserted-by":"crossref","unstructured":"Hu, Y.T., Wang, J., Yeh, R.A., Schwing, A.G.: Sail-vos 3d: a synthetic dataset and baselines for object detection and 3d mesh reconstruction from video data. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1418\u20131428 (2021)","DOI":"10.1109\/CVPR46437.2021.00147"},{"key":"23_CR13","doi-asserted-by":"crossref","unstructured":"Hwang, J., Oh, S.W., Lee, J.Y., Han, B.: Exemplar-based open-set panoptic segmentation network. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1175\u20131184 (2021)","DOI":"10.1109\/CVPR46437.2021.00123"},{"key":"23_CR14","doi-asserted-by":"crossref","unstructured":"Kirillov, A., He, K., Girshick, R., Rother, C., Dollar, P.: Panoptic segmentation. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9396\u20139405 (2019)","DOI":"10.1109\/CVPR.2019.00963"},{"key":"23_CR15","doi-asserted-by":"crossref","unstructured":"Kr\u00e4henb\u00fchl, P.: Free supervision from video games. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2955\u20132964 (2018)","DOI":"10.1109\/CVPR.2018.00312"},{"key":"23_CR16","doi-asserted-by":"publisher","first-page":"156","DOI":"10.1016\/j.ins.2020.05.062","volume":"535","author":"M Lan","year":"2020","unstructured":"Lan, M., Zhang, Y., Zhang, L., Du, B.: Global context based automatic road segmentation via dilated convolutional neural network. Inf. Sci. 535, 156\u2013171 (2020)","journal-title":"Inf. Sci."},{"key":"23_CR17","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1016\/j.neucom.2019.02.003","volume":"338","author":"F Lateef","year":"2019","unstructured":"Lateef, F., Ruichek, Y.: Survey on semantic segmentation using deep learning techniques. Neurocomputing 338, 321\u2013348 (2019)","journal-title":"Neurocomputing"},{"key":"23_CR18","unstructured":"Li, Y., Liu, M., Rehg, J.: In the eye of the beholder: Gaze and actions in first person video. IEEE Trans. Pattern Anal. Mach. Intell. (2021). https:\/\/ieeexplore.ieee.org\/document\/9325929"},{"key":"23_CR19","doi-asserted-by":"publisher","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","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"23_CR20","doi-asserted-by":"crossref","unstructured":"Orlando, S.A., Furnari, A., Battiato, S., Farinella, G.M.: Image based localization with simulated egocentric navigations. In: International Conference on Computer Vision Theory and Applications, pp. 305\u2013312 (2019)","DOI":"10.5220\/0007356500002108"},{"key":"23_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.imavis.2021.104098","volume":"107","author":"G Pasqualino","year":"2021","unstructured":"Pasqualino, G., Furnari, A., Signorello, G., Farinella, G.M.: An unsupervised domain adaptation scheme for single-stage artwork recognition in cultural sites. Image Vis. Comput. 107, 104098 (2021)","journal-title":"Image Vis. Comput."},{"key":"23_CR22","doi-asserted-by":"crossref","unstructured":"Ragusa, F., Di Mauro, D., Palermo, A., Furnari, A., Farinella, G.M.: Semantic object segmentation in cultural sites using real and synthetic data. In: 25th International Conference on Pattern Recognition (ICPR), pp. 1964\u20131971 (2021)","DOI":"10.1109\/ICPR48806.2021.9412149"},{"key":"23_CR23","doi-asserted-by":"crossref","unstructured":"Ragusa, F., Furnari, A., Livatino, S., Farinella, G.M.: The meccano dataset: understanding human-object interactions from egocentric videos in an industrial-like domain. In: IEEE Winter Conference on Applications of Computer Vision, pp. 1569\u20131578 (2021)","DOI":"10.1109\/WACV48630.2021.00161"},{"key":"23_CR24","doi-asserted-by":"crossref","unstructured":"Rezatofighi, H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I., Savarese, S.: Generalized intersection over union: a metric and a loss for bounding box regression. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 658\u2013666 (2019)","DOI":"10.1109\/CVPR.2019.00075"},{"key":"23_CR25","doi-asserted-by":"crossref","unstructured":"Richter, S.R., Hayder, Z., Koltun, V.: Playing for benchmarks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2213\u20132222 (2017)","DOI":"10.1109\/ICCV.2017.243"},{"key":"23_CR26","doi-asserted-by":"crossref","unstructured":"Tian, Z., Shen, C., Wang, X., Chen, H.: Boxinst: high-performance instance segmentation with box annotations. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5443\u20135452 (2021)","DOI":"10.1109\/CVPR46437.2021.00540"},{"key":"23_CR27","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2881\u20132890 (2017)","DOI":"10.1109\/CVPR.2017.660"}],"container-title":["Lecture Notes in Computer Science","Image Analysis and Processing \u2013 ICIAP 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-06430-2_23","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T13:48:23Z","timestamp":1710337703000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-06430-2_23"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031064296","9783031064302"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-06430-2_23","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":"17 May 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIAP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Image Analysis and Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lecce","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","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 May 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 May 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iciap2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.iciap2021.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":"Microsoft","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"307","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":"168","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":"55% - 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":"4","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)"}}]}}