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CNN-based RGB-D salient object detection: Learn, select, and fuse / Hao Chen in International journal of computer vision, vol 129 n° 7 (July 2021)  

Public [article]  inInternational journal of computer vision > vol 129 n° 7 (July 2021) . - pp 2076 - 2096 Titre : CNN-based RGB-D salient object detection: Learn, select, and fuse Type de document : Article/Communication Auteurs : Hao Chen, Auteur ; Yongjian Deng, Auteur ; Guosheng Lin, Auteur Année de publication : 2021 Article en page(s) : pp 2076 - 2096 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] approche hiérarchique [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] détection d'objet [Termes IGN] extraction de traits caractéristiques [Termes IGN] fusion de données [Termes IGN] image RVB [Termes IGN] profondeur [Termes IGN] saillance [Termes IGN] segmentation sémantique Résumé : (auteur) The goal of this work is to present a systematic solution for RGB-D salient object detection, which addresses the following three aspects with a unified framework: modal-specific representation learning, complementary cue selection, and cross-modal complement fusion. To learn discriminative modal-specific features, we propose a hierarchical cross-modal distillation scheme, in which we use the progressive predictions from the well-learned source modality to supervise learning feature hierarchies and inference in the new modality. To better select complementary cues, we formulate a residual function to incorporate complements from the paired modality adaptively. Furthermore, a top-down fusion structure is constructed for sufficient cross-modal cross-level interactions. The experimental results demonstrate the effectiveness of the proposed cross-modal distillation scheme in learning from a new modality, the advantages of the proposed multi-modal fusion pattern in selecting and fusing cross-modal complements, and the generalization of the proposed designs in different tasks. Numéro de notice : A2021-697 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s11263-021-01452-0 Date de publication en ligne : 05/05/2021 En ligne : https://doi.org/10.1007/s11263-021-01452-0 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98532 [article]