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El eje central de la XII edición del congreso (COMPDES2019) ha sido nuevamente el Internet de las Cosas (Internet of Things). Este eje se tomado como orientación general de los trabajos con las siguientes líneas temá...

The discriminative model learning for image denoising has been recently attracting considerable attentions due to its favorable denoising performance. In this paper, we take one step forward by investigating the construc...

In this work we address the task of semantic image segmentation with Deep Learning and make three main contributions that are experimentally shown to have substantial practical merit. First, we highlight convolution with...

Convolutional neural networks (CNNs) are inherently limited to model geometric transformations due to the fixed geometric structures in their building modules. In this work, we introduce two new modules to enhance the tr...

Recent work has shown that convolutional networks can be substantially deeper, more accurate, and efficient to train if they contain shorter connections between layers close to the input and those close to the output. In...

Subjects / keywords: Convolutional neural network; Skin cancer; Artificial intelligence; Deep learning; Medicine; Computer science; Biopsy; Skin biopsy; Deep neural networks; Cancer; Pattern recognition (psychology); Dermatology

Feature pyramids are a basic component in recognition systems for detecting objects at different scales. But pyramid representations have been avoided in recent object detectors that are based on deep convolutional netwo...

The highest accuracy object detectors to date are based on a two-stage approach popularized by R-CNN, where a classifier is applied to a sparse set of candidate object locations. In contrast, one-stage detectors that are...

We propose a technique for producing `visual explanations' for decisions from a large class of Convolutional Neural Network (CNN)-based models, making them more transparent. Our approach - Gradient-weighted Class Activat...

We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 e...

Despite the breakthroughs in accuracy and speed of single image super-resolution using faster and deeper convolutional neural networks, one central problem remains largely unsolved: how do we recover the finer texture de...

Point cloud is an important type of geometric data structure. Due to its irregular format, most researchers transform such data to regular 3D voxel grids or collections of images. This, however, renders data unnecessaril...

Subjects / keywords: Convolutional neural network; Computer science; Deep learning; Artificial intelligence; Activation function; Artificial neural network; Computation; Machine learning; Graphics; Pattern recognition (psychology); Algorithm...

We present an interpretation of Inception modules in convolutional neural networks as being an intermediate step in-between regular convolution and the depthwise separable convolution operation (a depthwise convolution f...

We introduce YOLO9000, a state-of-the-art, real-time object detection system that can detect over 9000 object categories. First we propose various improvements to the YOLO detection method, both novel and drawn from prio...

Subjects / keywords: Discriminative model; Computer science; Artificial intelligence; Domain (mathematical analysis); Backpropagation; Artificial neural network; Context (archaeology); Test data; Transfer of learning; Identification (biology...