{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T17:42:19Z","timestamp":1785865339038,"version":"3.56.0"},"publisher-location":"Cham","reference-count":36,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031781063","type":"print"},{"value":"9783031781070","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,12,2]],"date-time":"2024-12-02T00:00:00Z","timestamp":1733097600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,2]],"date-time":"2024-12-02T00:00:00Z","timestamp":1733097600000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-78107-0_8","type":"book-chapter","created":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T19:30:53Z","timestamp":1733081453000},"page":"117-134","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["SegXAL: Explainable Active Learning for\u00a0Semantic Segmentation in\u00a0Driving Scene Scenarios"],"prefix":"10.1007","author":[{"given":"Sriram","family":"Mandalika","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Athira","family":"Nambiar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,12,2]]},"reference":[{"key":"8_CR1","doi-asserted-by":"crossref","unstructured":"Khoei, T., Tala, H.O.S., Kaabouch, N.: Deep learning: systematic review, models, challenges, and research directions. Neural Comput. Appl. 35(31), 23103\u201323124 (2023)","DOI":"10.1007\/s00521-023-08957-4"},{"key":"8_CR2","doi-asserted-by":"crossref","unstructured":"Xie, S., Feng, Z., Chen, Y., Sun, S., Ma, C., Song, M.: DEAL: difficulty-aware active learning for semantic segmentation. In: Asian Conference on Computer Vision (2020)","DOI":"10.1007\/978-3-030-69525-5_40"},{"key":"8_CR3","doi-asserted-by":"crossref","unstructured":"Siddiqui, Y., Valentin, J., Nie\u00dfner, M.: ViewAL: active learning with viewpoint entropy for semantic segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9433\u20139443 (2020)","DOI":"10.1109\/CVPR42600.2020.00945"},{"key":"8_CR4","doi-asserted-by":"crossref","unstructured":"Ranftl, R., et al.: Towards robust monocular depth estimation: mixing datasets for zero-shot cross-dataset transfer. IEEE Trans. Pattern Anal. Mach. Intell. 44, 1623\u20131637 (2019)","DOI":"10.1109\/TPAMI.2020.3019967"},{"key":"8_CR5","unstructured":"Oquab, M., et al.: DINOv2: Learning robust visual features without supervision. arXiv preprint arXiv:2304.07193 (2023)"},{"key":"8_CR6","doi-asserted-by":"publisher","first-page":"3376","DOI":"10.1109\/JSTARS.2022.3166551","volume":"15","author":"G Lenczner","year":"2022","unstructured":"Lenczner, G., Chan-Hon-Tong, A., Le Saux, B., Luminari, N., Le Besnerais, G.: DIAL: deep interactive and active learning for semantic segmentation in remote sensing. IEEE J. Sel. Top. Appl. Earth Observations Remote Sens. 15, 3376\u20133389 (2022)","journal-title":"IEEE J. Sel. Top. Appl. Earth Observations Remote Sens."},{"key":"8_CR7","unstructured":"Ruder, S.: An overview of gradient descent optimization algorithms. arXiv preprint arXiv:1609.04747 (2016)"},{"key":"8_CR8","doi-asserted-by":"crossref","unstructured":"Lewis, D.D., Catlett, J.: Heterogeneous uncertainty sampling for supervised learning. In: Machine Learning Proceedings 1994, Morgan Kaufmann, pp. 148\u2013156 (1994)","DOI":"10.1016\/B978-1-55860-335-6.50026-X"},{"key":"8_CR9","doi-asserted-by":"crossref","unstructured":"Margatina, K., et al.: Active learning by acquiring contrastive examples. arXiv preprint arXiv:2109.03764 (2021)","DOI":"10.18653\/v1\/2021.emnlp-main.51"},{"issue":"1","key":"8_CR10","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1037\/a0031903","volume":"140","author":"BM Rottman","year":"2014","unstructured":"Rottman, B.M., Hastie, R.: Reasoning about causal relationships: inferences on causal networks. Psychol. Bull. 140(1), 109 (2014)","journal-title":"Psychol. Bull."},{"key":"8_CR11","unstructured":"Yang, S.C.-H., Folke, N.E.T., Shafto, P.: A psychological theory of explainability. In: International Conference on Machine Learning, PMLR (2022)"},{"key":"8_CR12","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1016\/j.inffus.2019.12.012","volume":"58","author":"AB Arrieta","year":"2020","unstructured":"Arrieta, A.B., et al.: Explainable artificial intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI. Inf. Fusion 58, 82\u2013115 (2020)","journal-title":"Inf. Fusion"},{"key":"8_CR13","doi-asserted-by":"crossref","unstructured":"Settles, B., Craven, M.: An analysis of active learning strategies for sequence labeling tasks. In: Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing (2008)","DOI":"10.3115\/1613715.1613855"},{"issue":"12","key":"8_CR14","doi-asserted-by":"publisher","first-page":"2591","DOI":"10.1109\/TCSVT.2016.2589879","volume":"27","author":"K Wang","year":"2016","unstructured":"Wang, K., et al.: Cost-effective active learning for deep image classification. IEEE Trans. Circuits Syst. Video Technol. 27(12), 2591\u20132600 (2016)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"8_CR15","unstructured":"Gal, Y., Islam, R., Ghahramani, Z.: Deep bayesian active learning with image data. In: International Conference on Machine Learning, PMLR (2017)"},{"key":"8_CR16","doi-asserted-by":"crossref","unstructured":"Beluch, W.H., et al.: The power of ensembles for active learning in image classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2018)","DOI":"10.1109\/CVPR.2018.00976"},{"key":"8_CR17","unstructured":"Settles, B.: Active learning literature survey. University of Wisconsin-Madison Department of Computer Sciences (2009)"},{"key":"8_CR18","doi-asserted-by":"crossref","unstructured":"Liebgott, A., et al.: Active learning for magnetic resonance image quality assessment. In: 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE (2016)","DOI":"10.1109\/ICASSP.2016.7471810"},{"key":"8_CR19","unstructured":"Goupilleau, A., Ceillier, T., Corbineau, M.-C.: Active learning for object detection in high-resolution satellite images. arXiv preprint arXiv:2101.02480 (2021)"},{"key":"8_CR20","unstructured":"Shapiro, D.: What Is Active Learning? NVIDIA blog (2020). https:\/\/blogs.nvidia.com\/blog\/what-is-active-learning\/"},{"key":"8_CR21","doi-asserted-by":"crossref","unstructured":"Rangnekar, A., Kanan, C., Hoffman, M.: Semantic segmentation with active semi-supervised learning. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision (2023)","DOI":"10.1109\/WACV56688.2023.00591"},{"key":"8_CR22","doi-asserted-by":"crossref","unstructured":"Schmidt, S., et al.: Advanced active learning strategies for object detection. In: 2020 IEEE Intelligent Vehicles Symposium (IV), IEEE (2020)","DOI":"10.1109\/IV47402.2020.9304565"},{"key":"8_CR23","doi-asserted-by":"crossref","unstructured":"Sinha, S., Ebrahimi, S., Darrell, T.: Variational adversarial active learning. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (2019)","DOI":"10.1109\/ICCV.2019.00607"},{"key":"8_CR24","unstructured":"Atakishiyev, S., et al.: Explainable artificial intelligence for autonomous driving: A comprehensive overview and field guide for future research directions. arXiv preprint arXiv:2112.11561 (2021)"},{"issue":"24","key":"8_CR25","doi-asserted-by":"publisher","first-page":"4180","DOI":"10.1093\/bioinformatics\/bty497","volume":"34","author":"J Zuallaert","year":"2018","unstructured":"Zuallaert, J., et al.: SpliceRover: interpretable convolutional neural networks for improved splice site prediction. Bioinformatics 34(24), 4180\u20134188 (2018)","journal-title":"Bioinformatics"},{"issue":"1","key":"8_CR26","doi-asserted-by":"publisher","first-page":"3958","DOI":"10.1038\/s41598-020-61055-6","volume":"10","author":"P Rajpurkar","year":"2020","unstructured":"Rajpurkar, P., et al.: AppendiXNet: deep learning for diagnosis of appendicitis from a small dataset of CT exams using video pretraining. Sci. Rep. 10(1), 3958 (2020)","journal-title":"Sci. Rep."},{"key":"8_CR27","doi-asserted-by":"publisher","first-page":"317","DOI":"10.5194\/isprs-annals-V-3-2021-317-2021","volume":"3","author":"T Stomberg","year":"2021","unstructured":"Stomberg, T., et al.: Jungle-net: using explainable machine learning to gain new insights into the appearance of wilderness in satellite imagery. ISPRS Ann. Photogrammetry Remote Sens. Spatial Inf. Sci. 3, 317\u2013324 (2021)","journal-title":"ISPRS Ann. Photogrammetry Remote Sens. Spatial Inf. Sci."},{"key":"8_CR28","doi-asserted-by":"crossref","unstructured":"Cordts, M., et al.: The cityscapes dataset for semantic urban scene understanding. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016)","DOI":"10.1109\/CVPR.2016.350"},{"key":"8_CR29","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015)","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"8_CR30","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2014 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"issue":"4","key":"8_CR31","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","volume":"40","author":"L-C Chen","year":"2017","unstructured":"Chen, L.-C., et al.: 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."},{"issue":"3","key":"8_CR32","doi-asserted-by":"publisher","first-page":"379","DOI":"10.1002\/j.1538-7305.1948.tb01338.x","volume":"27","author":"CE Shannon","year":"1948","unstructured":"Shannon, C.E.: A mathematical theory of communication. Bell Syst. Techn. J. 27(3), 379\u2013423 (1948)","journal-title":"Bell Syst. Techn. J."},{"key":"8_CR33","unstructured":"Liang, S., Li, Y., Srikant, R.: Principled detection of out-of-distribution examples in neural networks CoRR, abs\/1706.02690 (2017)"},{"issue":"3","key":"8_CR34","doi-asserted-by":"publisher","first-page":"1623","DOI":"10.1109\/TPAMI.2020.3019967","volume":"44","author":"R Ranftl","year":"2020","unstructured":"Ranftl, R., et al.: Towards robust monocular depth estimation: mixing datasets for zero-shot cross-dataset transfer. IEEE Trans. Pattern Anal. Mach. Intell. 44(3), 1623\u20131637 (2020)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"8_CR35","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., et al.: Grad-cAM: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision (2017)","DOI":"10.1109\/ICCV.2017.74"},{"key":"8_CR36","unstructured":"Sener, O., Savarese. S., Active learning for convolutional neural networks: a core-set approach. International Conference on Learning Representations (ICLR) (2018)"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-78107-0_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T20:04:08Z","timestamp":1733083448000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-78107-0_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,2]]},"ISBN":["9783031781063","9783031781070"],"references-count":36,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-78107-0_8","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,2]]},"assertion":[{"value":"2 December 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICPR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kolkata","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"India","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":"1 December 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 December 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icpr2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/icpr2024.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}