Deep learning for visual understanding: A review

Neurocomputing

Volumn 187, Issue , 2016, Pages 27-48

  Guo, Yanming ^a,c   Liu, Yu ^a   Oerlemans, Ard ^b   Lao, Songyang ^c   Wu, Song ^a   Lew, Michael S ^a  

^a LEIDEN UNIVERSITY   (Netherlands)

^b VDG Security BV   (Netherlands)

^c NATIONAL UNIVERSITY OF DEFENSE TECHNOLOGY   (China)

Author keywords

Applications; Challenges; Computer vision; Deep learning; Developments; Trends

Indexed keywords

ALGORITHMS; APPLICATIONS; ARTIFICIAL INTELLIGENCE; COMPUTER VISION; GESTURE RECOGNITION; IMAGE CLASSIFICATION; IMAGE SEGMENTATION; LEARNING SYSTEMS; MOTION ESTIMATION; OBJECT DETECTION; SEMANTICS;

CHALLENGES; DEEP LEARNING; DEEP NEURAL NETWORKS; DEVELOPMENTS; DISTRIBUTED REPRESENTATION; HUMAN POSE ESTIMATIONS; SEMANTIC SEGMENTATION; TRENDS;

LEARNING ALGORITHMS;

ARTICLE; ARTIFICIAL NEURAL NETWORK; BODY POSITION; CONVOLUTIONAL NEURAL NETWORK; DEEP BELIEF NETWORK; DEEP BOLTZMANN MACHINE; DEEP ENERGY MODEL; DEEP LEARNING; DENOISING AUTOENCODER; IMAGE DISPLAY; IMAGE RETRIEVAL; LEARNING ALGORITHM; MACHINE LEARNING; MATHEMATICAL COMPUTING; PRIORITY JOURNAL; RESTRICTED BOLTZMANN MACHINE; SPARSE AUTOENCODER; SPARSE CODING;

EID: 84957837518     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2015.09.116     Document Type: Article

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