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A survey of the recent architectures of deep convolutional neural networks [PDF]
Artificial Intelligence Review, 2019 Deep Convolutional Neural Network (CNN) is a special type of Neural Networks, which has shown exemplary performance on several competitions related to Computer Vision and Image Processing.
Asifullah Khan +3 more
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Performance analysis of different DCNN models in remote sensing image object detection
EURASIP Journal on Image and Video Processing, 2022 In recent years, deep learning, especially deep convolutional neural networks (DCNN), has made great progress. Many researchers use different DCNN models to detect remote sensing targets. Different DCNN models have different advantages and disadvantages.
Huaijin Liu +3 more
doaj +1 more source
A Comprehensive Survey on Graph Neural Networks [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2019 Deep learning has revolutionized many machine learning tasks in recent years, ranging from image classification and video processing to speech recognition and natural language understanding.
Zonghan Wu +5 more
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Contextual Convolutional Neural Networks [PDF]
2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), 2021 We propose contextual convolution (CoConv) for visual recognition. CoConv is a direct replacement of the standard convolution, which is the core component of convolutional neural networks. CoConv is implicitly equipped with the capability of incorporating contextual information while maintaining a similar number of parameters and computational cost ...
Radu Tudor Ionescu +2 more
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Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks [PDF]
Pattern Analysis and Applications, 2020 The 2019 novel coronavirus disease (COVID-19), with a starting point in China, has spread rapidly among people living in other countries and is approaching approximately 101,917,147 cases worldwide according to the statistics of World Health Organization.
A. Narin, Ceren Kaya, Ziynet Pamuk
semanticscholar +1 more source
A Hybrid Framework for Visual Positioning: Combining Convolutional Neural Networks with Ontologies
EAI Endorsed Transactions on Energy Web, 2022 Visual positioning is a new generation positioning technique which has been developed rapidly during recent years for many applications such as robotics, self-driving vehicles and positioning for visually impaired people due to advent of powerful image
Abdolreza Mosaddegh +4 more
doaj +1 more source
Orthogonal Convolutional Neural Networks [PDF]
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 Deep convolutional neural networks are hindered by training instability and feature redundancy towards further performance improvement. A promising solution is to impose orthogonality on convolutional filters. We develop an efficient approach to impose filter orthogonality on a convolutional layer based on the doubly block-Toeplitz matrix ...
Rudrasis Chakraborty +3 more
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Feature Extraction From Images Using Integrated Photonic Convolutional Kernel
IEEE Photonics Journal, 2022 Optical neural networks are expected to solve the problems of computational efficiency and energy consumption in neural networks. Herein, we experimentally implemented a 2 × 2 photonic convolutional kernel (PCK) using four on-chip micro-ring ...
Yulong Huang +6 more
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Canonical convolutional neural networks
2022 International Joint Conference on Neural Networks (IJCNN), 2022 We introduce canonical weight normalization for convolutional neural networks. Inspired by the canonical tensor decomposition, we express the weight tensors in so-called canonical networks as scaled sums of outer vector products. In particular, we train network weights in the decomposed form, where scale weights are optimized separately for each mode ...
Veeramacheneni, Lokesh +3 more
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Multi-view Convolutional Neural Networks for 3D Shape Recognition [PDF]
IEEE International Conference on Computer Vision, 2015 A longstanding question in computer vision concerns the representation of 3D shapes for recognition: should 3D shapes be represented with descriptors operating on their native 3D formats, such as voxel grid or polygon mesh, or can they be effectively ...
Hang Su +3 more
semanticscholar +1 more source