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LeSSS: Learned Shared Semantic Spaces for Relating Multi‐Modal Representations of 3D Shapes
Author(s) -
Herzog Robert,
Mewes Daniel,
Wand Michael,
Guibas Leonidas,
Seidel HansPeter
Publication year - 2015
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/cgf.12703
Subject(s) - computer science , embedding , artificial intelligence , modal , similarity (geometry) , rank (graph theory) , metric (unit) , space (punctuation) , key (lock) , pattern recognition (psychology) , natural language processing , mathematics , image (mathematics) , chemistry , operations management , computer security , combinatorics , polymer chemistry , economics , operating system
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 attributes are related to the observed geometry. Correlations between similar labels are captured by simultaneously embedding labels and shape descriptors into a common latent space in which an inner product corresponds to similarity. The mapping is learned robustly by optimizing a rank‐based loss function under a sparseness prior for the spectrum of the matrix of all classifiers. Second, we extend this framework towards relating multi‐modal representations of the geometric objects. The key idea is that weak cues from shared human labels are sufficient to obtain a consistent embedding of related objects even though their representations are not directly comparable. We evaluate our method against common base‐line approaches, investigate the influence of different geometric descriptors, and demonstrate a prototypical multi‐modal browser that relates 3D‐objects with text, photographs, and 2D line sketches.