Results 11 to 20 of about 1,028,974 (354)
I-CNet: Leveraging Involution and Convolution for Image Classification
IEEE Access, 2022 Convolution is widely adapted in deep learning models on image classification tasks for extracting hidden spatial-domain representations. However, as convolution is channel-specific, the potential cross-channel correlations in images are often neglected.
Guihuang Liang, Haoxiang Wang
doaj +1 more source
Involution: Inverting the Inherence of Convolution for Visual Recognition [PDF]
Computer Vision and Pattern Recognition, 2021 Convolution has been the core ingredient of modern neural networks, triggering the surge of deep learning in vision. In this work, we rethink the inherent principles of standard convolution for vision tasks, specifically spatial-agnostic and channel ...
Duo Li +7 more
semanticscholar +1 more source
Convolution in Convolution for Network in Network [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2018 A method of Convolutional Neural ...
Pang, Yanwei +4 more
openaire +5 more sources
Convolutional kernel function algebra
Frontiers in Computer Science, 2022 Many systems for image manipulation, signal analysis, machine learning, and scientific computing make use of discrete convolutional filters that are known before computation begins.
Edward Stow, Paul H. J. Kelly
doaj +1 more source
DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016 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.
Liang-Chieh Chen +4 more
semanticscholar +1 more source
Diverse Branch Block: Building a Convolution as an Inception-like Unit [PDF]
Computer Vision and Pattern Recognition, 2021 We propose a universal building block of Convolutional Neural Network (ConvNet) to improve the performance without any inference-time costs. The block is named Diverse Branch Block (DBB), which enhances the representational capacity of a single ...
Xiaohan Ding +3 more
semanticscholar +1 more source
Astronomy & Astrophysics, 2014 We introduce the concept of compressed convolution, a technique to convolve a given data set with a large number of non-orthogonal kernels. In typical applications our technique drastically reduces the effective number of computations. The new method is applicable to convolutions with symmetric and asymmetric kernels and can be easily controlled for an
Franz Elsner, Benjamin D. Wandelt
openaire +3 more sources
On convolutions of slanted half-plane mappings
Journal of Taibah University for Science, 2021 The convolution of convex harmonic univalent functions in the unit disk, unlike analytic functions, may not be convex or even univalent. The main purpose of this work is to develop previous work involving the convolution of convex harmonic functions ...
Elif Yaşar
doaj +1 more source
Industrial Scale RTD Measurement Using Gold Radiotracer [PDF]
Iranian Journal of Chemistry & Chemical Engineering, 2021 Residence Time Distribution (RTD) is a suitable method to find out the hydrodynamics of any industrial or lab-scale reactor. Radiotracer 198Au was used to trace the liquid phase of the industrial scale continuous three tube pulp digester. The radiotracer
Meenakshi Sheoran +4 more
doaj +1 more source
KPConv: Flexible and Deformable Convolution for Point Clouds [PDF]
IEEE International Conference on Computer Vision, 2019 We present Kernel Point Convolution (KPConv), a new design of point convolution, i.e. that operates on point clouds without any intermediate representation. The convolution weights of KPConv are located in Euclidean space by kernel points, and applied to
Hugues Thomas +5 more
semanticscholar +1 more source