101500 20210507085643.0 doi 10.1109/IROS.2018.8594185 sideral 107640 ART-2018-107640 eng Alonso Ruiz, Iñigo Universidad de Zaragoza (orcid)0000-0003-4638-4655 Semantic Segmentation from Sparse Labeling Using Multi-Level Superpixels 2018 Semantic segmentation is a challenging problemthat can benefit numerous robotics applications, since it pro-vides information about the content at every image pixel.Solutions to this problem have recently witnessed a boost onperformance and results thanks to deep learning approaches.Unfortunately, common deep learning models for semanticsegmentation present several challenges which hinder real lifeapplicability in many domains. A significant challenge is theneed of pixel level labeling on large amounts of trainingimages to be able to train those models, which implies avery high cost. This work proposes and validates a simplebut effective approach to train dense semantic segmentationmodels from sparsely labeled data. Labeling only a few pixelsper image reduces the human interaction required. We findmany available datasets, e.g., environment monitoring data, thatprovide this kind of sparse labeling. Our approach is basedon augmenting the sparse annotation to a dense one with theproposed adaptive superpixel segmentation propagation. Weshow that this label augmentation enables effective learning ofstate-of-the-art segmentation models, getting similar results tothose models trained with dense ground-truth. Access copy available to the general public Unrestricted info:eu-repo/grantAgreement/ES/DGA/T45-17R info:eu-repo/grantAgreement/ES/MINECO-FEDER/DPI2015-69376-R info:eu-repo/grantAgreement/ES/UZ/UZ2017-TEC-06 info:eu-repo/semantics/openAccess by http://creativecommons.org/licenses/by/3.0/es/ info:eu-repo/semantics/article info:eu-repo/semantics/acceptedVersion Murillo Arnal, Ana Cristina Universidad de Zaragoza (orcid)0000-0002-7580-9037 5007 520 Universidad de Zaragoza Dpto. Informát.Ingenie.Sistms. Área Ingen.Sistemas y Automát. 2018, 18401073 (2018), 5785-5792 Proc. IEEE/RSJ Int. Conf. Intell. Rob. Syst. Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems 2153-0858 3286918 http://zaguan.unizar.es/record/101500/files/texto_completo.pdf Postprint 3109547 http://zaguan.unizar.es/record/101500/files/texto_completo.jpg?subformat=icon icon Postprint oai:zaguan.unizar.es:101500 articulos driver 2021-05-07-08:04:19 ARTICLE