LeSSS: Learned Shared Semantic Spaces for Relating Multi-Modal Representations of 3D Shapes

In this paper, we propose a new method for structuring multi‐modal representations of shapes according to semantic relations. We learn a metric that links semantically similar objects represented in different modalities. First, 3D‐shapes are associated with textual labels by learning how textual att...

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Vydané v:	Computer graphics forum Ročník 34; číslo 5; s. 141 - 151
Hlavní autori:	Herzog, Robert, Mewes, Daniel, Wand, Michael, Guibas, Leonidas, Seidel, Hans-Peter
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Oxford Blackwell Publishing Ltd 01.08.2015
Predmet:	3D-shape descriptors Analysis and object representations Artificial Intelligence [I.2.10]: Vision and Scene Understanding Categories and Subject Descriptors (according to ACM CCS) collaborative filtering Computer Graphics [I.3.5]: Computational Geometry and Object Modeling-Curve Computer Graphics [I.3.5]: Computational Geometry and Object Modeling—Curve, surface, solid, and object representations Correlation Image processing systems Labels multi-modal learning object recognition object retrieval Representations semantic correspondences Semantics Shape Image Processing and Computer Vision [I.4.8]: Scene Analysis-Object recognition Similarity solid Studies surface Texts Three dimensional Topological manifolds
ISSN:	0167-7055, 1467-8659
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