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Convolution

Known as: Convolve, Carson's integral, Continuous-time convolution 
In mathematics (and, in particular, functional analysis) convolution is a mathematical operation on two functions (f and g); it produces a third… Expand
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Related topics

Related topics

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Analog signal processingBlob detectionCausal filterChasys Draw IES
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Broader (1)

Image processing

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
Review
2020
Review
2020
Image and Video Compression With Neural Networks: A Review
In recent years, the image and video coding technologies have advanced by leaps and bounds. However, due to the popularization of… Expand
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Review
2019
Review
2019
Review of Deep Learning Algorithms and Architectures
Deep learning (DL) is playing an increasingly important role in our lives. It has already made a huge impact in areas, such as… Expand
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Review
2019
Review
2019
Review of Visual Saliency Detection With Comprehensive Information
The visual saliency detection model simulates the human visual system to perceive the scene and has been widely used in many… Expand
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Highly Cited
2017
Highly Cited
2017
ImageNet classification with deep convolutional neural networks
We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC… Expand
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Highly Cited
2017
Highly Cited
2017
Rethinking Atrous Convolution for Semantic Image Segmentation
In this work, we revisit atrous convolution, a powerful tool to explicitly adjust filter's field-of-view as well as control the… Expand
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Highly Cited
2017
Highly Cited
2017
Semi-Supervised Classification with Graph Convolutional Networks
We present a scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of… Expand
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Highly Cited
2017
Highly Cited
2017
ECO: Efficient Convolution Operators for Tracking
In recent years, Discriminative Correlation Filter (DCF) based methods have significantly advanced the state-of-the-art in… Expand
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Highly Cited
2015
Highly Cited
2015
Going deeper with convolutions
We propose a deep convolutional neural network architecture codenamed Inception that achieves the new state of the art for… Expand
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Highly Cited
2015
Highly Cited
2015
Very Deep Convolutional Networks for Large-Scale Image Recognition
In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition… Expand
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Highly Cited
2001
Highly Cited
2001
Convolution Kernels for Natural Language
We describe the application of kernel methods to Natural Language Processing (NLP) problems. In many NLP tasks the objects being… Expand
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