Results 11 to 20 of about 21,019 (26)
Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network [PDF]
, 2016 Recently, several models based on deep neural networks have achieved great success in terms of both reconstruction accuracy and computational performance for single image super-resolution. In these methods, the low resolution (LR) input image is upscaled
Aitken, AP +7 more
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Diving Deep into Sentiment: Understanding Fine-tuned CNNs for Visual Sentiment Prediction [PDF]
, 2015 Visual media are powerful means of expressing emotions and sentiments. The constant generation of new content in social networks highlights the need of automated visual sentiment analysis tools. While Convolutional Neural Networks (CNNs) have established
Jiang Y.-G. +6 more
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Learning to count with deep object features
, 2015 Learning to count is a learning strategy that has been recently proposed in the literature for dealing with problems where estimating the number of object instances in a scene is the final objective.
Pujol, Oriol +2 more
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, 2009 (ABRIDGED) In previous work, two platforms have been developed for testing computer-vision algorithms for robotic planetary exploration (McGuire et al. 2004b,2005; Bartolo et al. 2007).
A. Bonnici +21 more
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Image Restoration using Total Variation Regularized Deep Image Prior
, 2018 In the past decade, sparsity-driven regularization has led to significant improvements in image reconstruction. Traditional regularizers, such as total variation (TV), rely on analytical models of sparsity.
Kamilov, Ulugbek S. +3 more
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Deep learning architectures for Computer Vision [PDF]
, 2016 Deep learning has become part of many state-of-the-art systems in multiple disciplines (specially in computer vision and speech processing). In this thesis Convolutional Neural Networks are used to solve the problem of recognizing people in images, both ...
Roig Marí, Carlos
core
How is Gaze Influenced by Image Transformations? Dataset and Model
, 2019 Data size is the bottleneck for developing deep saliency models, because collecting eye-movement data is very time consuming and expensive. Most of current studies on human attention and saliency modeling have used high quality stereotype stimuli.
Borji, Ali +5 more
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Semi-Supervised Hierarchical Semantic Object Parsing
, 2017 Models based on Convolutional Neural Networks (CNNs) have been proven very successful for semantic segmentation and object parsing that yield hierarchies of features.
Amindavar, Hamidreza, Mirakhorli, Jalal
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U-Net: Convolutional Networks for Biomedical Image Segmentation
, 2015 There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a network and training strategy that relies on the strong use of data augmentation to use the available ...
Brox, Thomas +2 more
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DPC-Net: Deep Pose Correction for Visual Localization
, 2018 We present a novel method to fuse the power of deep networks with the computational efficiency of geometric and probabilistic localization algorithms. In contrast to other methods that completely replace a classical visual estimator with a deep network ...
Kelly, Jonathan, Peretroukhin, Valentin
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