- Read more about WHERE TO PLACE: A REAL-TIME VISUAL SALIENCY BASED LABEL PLACEMENT FOR AUGMENTED REALITY APPLICATIONS
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- Read more about R-COVNET: RECURRENT NEURAL CONVOLUTION NETWORK FOR 3D OBJECT RECOGNITION
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Point cloud are precise digital record of an objects in space. It starts to getting more attention due to the additional information it provides compared to 2D images. In this paper, we propose a new deep learning architecture called R-CovNets, designed for 3D object recognition. Unlike to previous approaches that usually sample or convert point cloud into three-dimensional grids, R-CovNets does not reckon on any preprocessing. Our architecture is specially designed for cloud point, permutation invariant and can take as input, a data of any size.
R-COVNET.pdf
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- Read more about DISCOVER THE EFFECTIVE STRATEGY FOR FACE RECOGNITION MODEL COMPRESSION BY IMPROVED KNOWLEDGE DISTILLATION
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- Read more about DISCOVER THE EFFECTIVE STRATEGY FOR FACE RECOGNITION MODEL COMPRESSION BY IMPROVED KNOWLEDGE DISTILLATION
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- Read more about SIGPORT FOR THE 2018 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) for paper 1630
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- Read more about SIGPORT FOR THE 2018 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) for paper 1630
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- Read more about CONDITIONAL DISTRIBUTION LEARNING WITH NEURAL NETWORKS AND ITS APPLICATION TO UNIVERSAL IMAGE DENOISING
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A simple and scalable denoising algorithm is proposed that can be applied to a wide range of source and noise models. At the core of the proposed CUDE algorithm is symbol-by-symbol universal denoising used by the celebrated DUDE algorithm, whereby the optimal estimate of the source from an unknown distribution is computed by inverting the empirical distribution of the noisy observation sequence by a deep neural network, which naturally and implicitly aggregates multiple contexts of similar characteristics and estimates the conditional distribution more accurately.
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