Geometric deep learning on graphs and manifolds using mixture model CNNs

Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017

Volumn 2017-January, Issue , 2017, Pages 5425-5434

  Monti, Federico ^a   Boscaini, Davide ^a   Masci, Jonathan ^a,d   Rodolà, Emanuele ^a   Svoboda, Jan ^a   Bronstein, Michael M ^a,b,c  

^a UNIVERSITY OF LUGANO   (Switzerland)

^b TEL AVIV UNIVERSITY   (Israel)

^c Intel Perceptual Computing   (Switzerland)

^d Nnaisense   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER GRAPHICS; COMPUTER VISION; NATURAL LANGUAGE PROCESSING SYSTEMS; NETWORK ARCHITECTURE; NEURAL NETWORKS; OBJECT DETECTION; OBJECT RECOGNITION; SPEECH RECOGNITION;

3D SHAPE ANALYSIS; ACOUSTIC SIGNALS; COMPUTATIONAL SOCIAL SCIENCE; CONVOLUTIONAL NEURAL NETWORKS (CNN); LEARNING METHODS; OBJECT DETECTION AND RECOGNITION; STATE-OF-THE-ART PERFORMANCE; UNIFIED FRAMEWORK;

DEEP LEARNING;

EID: 85024494220     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2017.576     Document Type: Conference Paper

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