Walterio Mayol-Cuevas

Convolutional Neural Network,Inference Time,Stochastic Gradient Descent,Computational Requirements,Convolutional Neural Network Model,Deeper Layers,F1 Score,Filters In Layer,Focal Plane,Image Descriptors,Memory Footprint,Neural Architecture Search,Neural Network,Number Of Filters,Resource Consumption,Similarity Measure,Triplet Loss,Video Segments,Accurate Point,Action Representation,Active Learning,Additional Loss,Adverbs,Affine Transformation,Artifact Removal,Artificial Neural Network,Attention Heads,Augmented Reality System,Automatic Search,Binary Classification,Binary Method,Bounding Box,CNN Inference,Central Loss,Color Channels,Color Histogram,Color Information,Color Space,Combined Loss,Compression Artifacts,Computer Vision,Convolutional Layers,Convolutional Model,Convolutional Network,Convolutional Network Model,Convolutional Neural Network Architecture,Current Image,Demand For Resources,Discrete Fourier Transform,Discriminative Feature Learning,