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Semantic understanding of scenes through the ADE20K dataset / Bolei Zhou in International journal of computer vision, vol 127 n° 3 (March 2019)  

Public [article]  inInternational journal of computer vision > vol 127 n° 3 (March 2019) . - pp 302 - 321 Titre : Semantic understanding of scenes through the ADE20K dataset Type de document : Article/Communication Auteurs : Bolei Zhou, Auteur ; Hang Zhao, Auteur ; Xavier Puig, Auteur ; Tete Xiao, Auteur ; Sanja Fidler, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 302 - 321 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] apprentissage profond [Termes IGN] compréhension de l'image [Termes IGN] détection d'objet [Termes IGN] jeu de données localisées [Termes IGN] réseau neuronal artificiel [Termes IGN] scène [Termes IGN] segmentation sémantique Résumé : (Auteur) Semantic understanding of visual scenes is one of the holy grails of computer vision. Despite efforts of the community in data collection, there are still few image datasets covering a wide range of scenes and object categories with pixel-wise annotations for scene understanding. In this work, we present a densely annotated dataset ADE20K, which spans diverse annotations of scenes, objects, parts of objects, and in some cases even parts of parts. Totally there are 25k images of the complex everyday scenes containing a variety of objects in their natural spatial context. On average there are 19.5 instances and 10.5 object classes per image. Based on ADE20K, we construct benchmarks for scene parsing and instance segmentation. We provide baseline performances on both of the benchmarks and re-implement state-of-the-art models for open source. We further evaluate the effect of synchronized batch normalization and find that a reasonably large batch size is crucial for the semantic segmentation performance. We show that the networks trained on ADE20K are able to segment a wide variety of scenes and objects. Numéro de notice : A2018-602 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s11263-018-1140-0 Date de publication en ligne : 07/12/2018 En ligne : https://doi.org/10.1007/s11263-018-1140-0 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92529 [article]