Results 31 to 40 of about 177,128 (280)
, 2017 In this paper, we consider convolutional neural networks operating on sparse inputs with an application to depth upsampling from sparse laser scan data.
Brox, Thomas +5 more
core +1 more source
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017 We present a conceptually simple, flexible, and general framework for object instance segmentation. Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance. The method, called Mask R-CNN, extends Faster R-CNN by adding a branch for predicting an object mask in parallel with
Kaiming He +3 more
openaire +4 more sources
, 2015 In this work we describe a Convolutional Neural Network (CNN) to accurately predict the scene illumination. Taking image patches as input, the CNN works in the spatial domain without using hand-crafted features that are employed by most previous methods.
Bianco, Simone +2 more
core +1 more source
Adaptive Deep Learning for Soft Real-Time Image Classification
Technologies, 2021 CNNs (Convolutional Neural Networks) are becoming increasingly important for real-time applications, such as image classification in traffic control, visual surveillance, and smart manufacturing.
Fangming Chai, Kyoung-Don Kang
doaj +1 more source
Graph Classification with 2D Convolutional Neural Networks
, 2019 Graph learning is currently dominated by graph kernels, which, while powerful, suffer some significant limitations. Convolutional Neural Networks (CNNs) offer a very appealing alternative, but processing graphs with CNNs is not trivial.
Meladianos, Polykarpos +3 more
core +2 more sources
Remote Sensing, 2017 Deep convolutional neural networks (CNNs) have been widely used to obtain high-level representation in various computer vision tasks. However, in the field of remote sensing, there are not sufficient images to train a useful deep CNN. Instead, we tend to
Jie Wang +4 more
doaj +1 more source
A Closer Look at Spatiotemporal Convolutions for Action Recognition
, 2018 In this paper we discuss several forms of spatiotemporal convolutions for video analysis and study their effects on action recognition. Our motivation stems from the observation that 2D CNNs applied to individual frames of the video have remained solid ...
LeCun, Yann +5 more
core +1 more source
Image Forgery Localization Based on Multi-Scale Convolutional Neural Networks
, 2018 In this paper, we propose to utilize Convolutional Neural Networks (CNNs) and the segmentation-based multi-scale analysis to locate tampered areas in digital images.
Cao, Yun +3 more
core +1 more source
Arthritis Care &Research, EarlyView.Systemic sclerosis (SSc) is a rare autoimmune disease defined by immune dysregulation, vasculopathy, and progressive fibrosis of the skin and internal organs. Despite advances in care, major complications such as interstitial lung disease (ILD) and myocardial involvement remain the leading causes of morbidity and mortality.
Cristiana Sieiro Santos +2 more
wiley +1 more source
Advanced Engineering Materials, EarlyView.This study presents an infrared monitoring approach for direct laser interference patterning (DLIP) combined with a convolutional neural network (CNN). Thermal emission data captured during structuring are used to predict surface topography parameters.
Lukas Olawsky +5 more
wiley +1 more source