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Deep Learning Datasets Challenges For Semantic Segmentation - A Survey

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dc.contributor.authorPonciano, Claire
dc.contributor.authorSchaffert, Markus
dc.contributor.authorPonciano, Jean-Jacques
dc.contributor.editorKlein, Maike
dc.contributor.editorKrupka, Daniel
dc.contributor.editorWinter, Cornelia
dc.contributor.editorWohlgemuth, Volker
dc.date.accessioned2023-11-29T14:50:29Z
dc.date.available2023-11-29T14:50:29Z
dc.date.issued2023
dc.description.abstractThis survey offers a comprehensive analysis of challenges encountered when employing large-scale datasets for deep learning-based semantic segmentation, an area with significant implica- tions for industries such as autonomous driving, precision agriculture, and medical imaging. Through a systematic review of 94 papers from Papers with Code, we identified 32 substantial challenges, which we categorized into six key areas: Data Quality and Quantity, Data Preprocessing, Resource Constraints, Data Management and Privacy, Generalization, and Data Compatibility. By identifying and explicating these challenges, our research provides a crucial reference point for future studies aiming to address these issues and enhance the performance of deep learning models for semantic segmentation. Future work will focus on leveraging AI and semantic technologies to provide solutions to these challenges.en
dc.identifier.doi10.18420/inf2023_04
dc.identifier.isbn978-3-88579-731-9
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/43159
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofINFORMATIK 2023 - Designing Futures: Zukünfte gestalten
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-337
dc.subjectDeep Learning
dc.subjectDeep Learning challenges
dc.subjectSemantic segmentation
dc.subjectData quality
dc.subjectResource constraints
dc.subjectGeneralization
dc.subjectData management
dc.subjectData privacy
dc.subjectData compatibility
dc.titleDeep Learning Datasets Challenges For Semantic Segmentation - A Surveyen
dc.typeText/Conference Paper
gi.citation.endPage70
gi.citation.publisherPlaceBonn
gi.citation.startPage57
gi.conference.date26.-29. September 2023
gi.conference.locationBerlin
gi.conference.sessiontitleYoung Scientists and early-stage research in Data Science Workshop (YSDS-23)

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P337 - INFORMATIK 2023 - Designing Futures: Zukünfte gestalten