{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T16:26:49Z","timestamp":1771950409093,"version":"3.50.1"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031606052","type":"print"},{"value":"9783031606069","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-60606-9_26","type":"book-chapter","created":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T01:06:47Z","timestamp":1717204007000},"page":"436-445","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Reducing Human Annotation Effort Using Self-supervised Learning for\u00a0Image Segmentation"],"prefix":"10.1007","author":[{"given":"Thitirat","family":"Siriborvornratanakul","sequence":"first","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,6,1]]},"reference":[{"key":"26_CR1","unstructured":"Balestriero, R., et al.: A cookbook of self-supervised learning, pp. 1\u201371. arXiv:2304.12210v2 (2023)"},{"key":"26_CR2","doi-asserted-by":"crossref","unstructured":"Bashkirova, D., et al.: Zerowaste dataset: Towards deformable object segmentation in cluttered scenes. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Los Alamitos, CA, USA, pp. 21115\u201321125. IEEE Computer Society (Jun 2022)","DOI":"10.1109\/CVPR52688.2022.02047"},{"key":"26_CR3","doi-asserted-by":"crossref","unstructured":"Bunyang, S., et al.: Self-supervised learning advanced plant disease image classification with SimCLR. Adv. Comput. Intell. 3 (2023)","DOI":"10.1007\/s43674-023-00065-z"},{"key":"26_CR4","doi-asserted-by":"crossref","unstructured":"Chen, T., et al.: Sam-adapter: adapting segment anything in underperformed scenes. In: 2023 IEEE\/CVF International Conference on Computer Vision Workshops (ICCVW). (2023) 3359\u20133367","DOI":"10.1109\/ICCVW60793.2023.00361"},{"key":"26_CR5","first-page":"1","volume":"61","author":"XD Chen","year":"2023","unstructured":"Chen, X.D., Wu, W., Yang, W., Qin, H., Wu, X., Mao, X.: Make segment anything model perfect on shadow detection. IEEE Trans. Geosci. Remote Sens. 61, 1\u201313 (2023)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"26_CR6","doi-asserted-by":"crossref","unstructured":"Cordts, M., et al.: The cityscapes dataset for semantic urban scene understanding. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Los Alamitos, CA, USA, pp. 3213\u20133223. IEEE Computer Society (Jun 2016)","DOI":"10.1109\/CVPR.2016.350"},{"key":"26_CR7","doi-asserted-by":"publisher","first-page":"45547","DOI":"10.1109\/ACCESS.2023.3274193","volume":"11","author":"B Fang","year":"2023","unstructured":"Fang, B., Li, X., Han, G., He, J.: Rethinking pseudo-labeling for semi-supervised facial expression recognition with contrastive self-supervised learning. IEEE Access 11, 45547\u201345558 (2023)","journal-title":"IEEE Access"},{"key":"26_CR8","unstructured":"Gansbeke, W.V., Vandenhende, S., Georgoulis, S., Gool, L.V.: Unsupervised semantic segmentation by contrasting object mask proposals. In: IEEE\/CVF International Conference on Computer Vision (ICCV), Los Alamitos, CA, USA, pp. 10032\u201310042. IEEE Computer Society (oct 2021)"},{"issue":"6","key":"26_CR9","doi-asserted-by":"publisher","first-page":"7457","DOI":"10.1109\/TPAMI.2022.3218275","volume":"45","author":"S Gao","year":"2023","unstructured":"Gao, S., Li, Z.Y., Yang, M.H., Cheng, M.M., Han, J., Torr, P.: Large-scale unsupervised semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 45(6), 7457\u20137476 (2023)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"26_CR10","doi-asserted-by":"crossref","unstructured":"Kirillov, A., He, K., Girshick, R., Rother, C., Doll\u00e1r, P.: Panoptic segmentation. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9396\u20139405 (2019)","DOI":"10.1109\/CVPR.2019.00963"},{"key":"26_CR11","doi-asserted-by":"crossref","unstructured":"Kirillov, A., et al.: Segment anything. In: IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 4015\u20134026 (October 2023)","DOI":"10.1109\/ICCV51070.2023.00371"},{"key":"26_CR12","unstructured":"Kirillov, A., et al.: Segment anything, pp. 1\u201330. arXiv:2304.02643v1 (2023)"},{"key":"26_CR13","doi-asserted-by":"crossref","unstructured":"Kittipongdaja, P., Siriborvornratanakul, T.: Automatic kidney segmentation using 2.5D ResUNet and 2.5D DenseUNet for malignant potential analysis in complex renal cyst based on CT images. EURASIP J. Image Video Process. 2022(5) (2022)","DOI":"10.1186\/s13640-022-00581-x"},{"key":"26_CR14","first-page":"740","volume-title":"European Conference on Computer Vision (ECCV)","author":"TY 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.) European Conference on Computer Vision (ECCV), pp. 740\u2013755. Springer International Publishing, Cham (2014)"},{"key":"26_CR15","doi-asserted-by":"crossref","unstructured":"Miao, J., et al.: Large-scale video panoptic segmentation in the wild: A benchmark. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), (2022) 21001\u201321011","DOI":"10.1109\/CVPR52688.2022.02036"},{"issue":"7","key":"26_CR16","doi-asserted-by":"publisher","first-page":"1837","DOI":"10.1109\/TMI.2022.3150682","volume":"41","author":"C Ouyang","year":"2022","unstructured":"Ouyang, C., Biffi, C., Chen, C., Kart, T., Qiu, H., Rueckert, D.: Self-supervised learning for few-shot medical image segmentation. IEEE Trans. Med. Imaging 41(7), 1837\u20131848 (2022)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"26_CR17","doi-asserted-by":"crossref","unstructured":"Pan, S., Liu, X., Xie, N., Chong, Y.: EG-TransUNet: a transformer-based U-Net with enhanced and guided models for biomedical image segmentation. BMC Bioinform. 24 (2023)","DOI":"10.1186\/s12859-023-05196-1"},{"key":"26_CR18","unstructured":"Purushwalkam, S., Gupta, A.: Demystifying contrastive self-supervised learning: invariances, augmentations and dataset biases. In: Neural Information Processing Systems (NeurIPS), pp. 3407\u20133418 (2020)"},{"key":"26_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s12198-023-00270-4","volume":"17","author":"W Sarai","year":"2024","unstructured":"Sarai, W., Monbut, N., Youngchoay, N., Phookriangkrai, N., Sattabun, T., Siriborvornratanakul, T.: Enhancing baggage inspection through computer vision analysis of x-ray images. J. Transp. Secur. 17, 1\u201313 (2024)","journal-title":"J. Transp. Secur."},{"key":"26_CR20","doi-asserted-by":"crossref","unstructured":"Scheibenreif, L., Hanna, J., Mommert, M., Borth, D.: Self-supervised vision transformers for land-cover segmentation and classification. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 1421\u20131430 (2022)","DOI":"10.1109\/CVPRW56347.2022.00148"},{"key":"26_CR21","doi-asserted-by":"publisher","first-page":"84134","DOI":"10.1109\/ACCESS.2023.3302913","volume":"11","author":"H Shi","year":"2023","unstructured":"Shi, H., Sakai, T.: Self-supervised and few-shot contrastive learning frameworks for text clustering. IEEE Access 11, 84134\u201384143 (2023)","journal-title":"IEEE Access"},{"key":"26_CR22","unstructured":"Singh, S., et al.: Self-supervised feature learning for semantic segmentation of overhead imagery. In: The British Machine Vision Conference (BMVC), Newcaltle, UK, 1\u201313 (Sep 2018)"},{"key":"26_CR23","doi-asserted-by":"publisher","unstructured":"Siriborvornratanakul, T.: Advanced artificial intelligence methods for medical applications. In: Duffy, V.G. (ed.) Digital Human Modeling and Applications in Health, pp. 329\u2013340. Safety, Ergonomics and Risk Management. Springer Nature Switzerland, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-35748-0_24","DOI":"10.1007\/978-3-031-35748-0_24"},{"issue":"16","key":"26_CR24","doi-asserted-by":"publisher","first-page":"2300","DOI":"10.1111\/mice.13010","volume":"38","author":"T Siriborvornratanakul","year":"2023","unstructured":"Siriborvornratanakul, T.: Pixel-level thin crack detection on road surface using convolutional neural network for severely imbalanced data. Computer-aided Civil Infrastruct. Eng. 38(16), 2300\u20132316 (2023)","journal-title":"Computer-aided Civil Infrastruct. Eng."},{"key":"26_CR25","doi-asserted-by":"publisher","first-page":"1033","DOI":"10.1007\/s10994-021-06110-7","volume":"112","author":"H Wang","year":"2023","unstructured":"Wang, H., Chen, T., Wang, Z., Ma, K.: Troubleshooting image segmentation models with human-in-the-loop. Mach. Learn. 112, 1033\u20131051 (2023)","journal-title":"Mach. Learn."},{"key":"26_CR26","doi-asserted-by":"publisher","first-page":"18887","DOI":"10.1007\/s10489-023-04488-6","volume":"53","author":"J Wang","year":"2023","unstructured":"Wang, J., Wu, J., Jia, C., Zhang, Z.: Self-supervised variational autoencoder towards recommendation by nested contrastive learning. Appl. Intell. 53, 18887\u201318897 (2023)","journal-title":"Appl. Intell."},{"key":"26_CR27","doi-asserted-by":"crossref","unstructured":"Wei, D., et al.: Youmvos: an actor-centric multi-shot video object segmentation dataset. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 21012\u201321021 (2022)","DOI":"10.1109\/CVPR52688.2022.02037"},{"key":"26_CR28","unstructured":"Zadaianchuk, A., Kleindessner, M., Zhu, Y., Locatello, F., Brox, T.: Unsupervised semantic segmentation with self-supervised object-centric representations. In: International Conference on Learning Representations (ICLR) (2023)"},{"issue":"11","key":"26_CR29","doi-asserted-by":"publisher","first-page":"7040","DOI":"10.1109\/TITS.2020.3001154","volume":"22","author":"B Zhang","year":"2021","unstructured":"Zhang, B., Zhang, J.: A traffic surveillance system for obtaining comprehensive information of the passing vehicles based on instance segmentation. IEEE Trans. Intell. Transp. Syst. 22(11), 7040\u20137055 (2021)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"26_CR30","doi-asserted-by":"crossref","unstructured":"Zhou, B., Zhao, H., Puig, X., Fidler, S., Barriuso, A., Torralba, A.: Scene Parsing through ADE20K Dataset. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5122\u20135130 (2017)","DOI":"10.1109\/CVPR.2017.544"}],"container-title":["Lecture Notes in Computer Science","Artificial Intelligence in HCI"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-60606-9_26","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T01:10:47Z","timestamp":1717204247000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-60606-9_26"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031606052","9783031606069"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-60606-9_26","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"1 June 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"HCII","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Human-Computer Interaction","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Washington DC","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"USA","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 June 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 July 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"hcii2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2024.hci.international\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}