Results 281 to 290 of about 324,009 (309)
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Mobile Net Convolutional Neural Networks for Video Classification
2021Current data and correspondence advances give the foundation to send bits anyplace, however don't dare to deal with data at the semantic level.s This paper researches the utilization of video content investigation and high- light extraction and bunching strategies for additional video semantic arrangements.
Bhimavarapu Sravya Pranati +2 more
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SRGC-Nets: Sparse Repeated Group Convolutional Neural Networks
IEEE Transactions on Neural Networks and Learning Systems, 2020Group convolution is widely used in many mobile networks to remove the filter's redundancy from the channel extent. In order to further reduce the redundancy of group convolution, this article proposes a novel repeated group convolutional (RGC) kernel, which has M primary groups, and each primary group includes N tiny groups.
Yao Lu +4 more
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Recognizing objectionable images using convolutional neural nets
2015 Signal Processing and Intelligent Systems Conference (SPIS), 2015In recent years different methods for detecting objectionable images have proposed. All of the previous systems are based on extracting pre-defined and certain features from the images. In this paper a method is proposed in order to detect objectionable images using convolutional neural networks.
Reza Moradi, Rahman Yousefzadeh
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Traffic Signal Control Based on Reinforcement Learning with Graph Convolutional Neural Nets
International Conference on Intelligent Transportation Systems, 2018Traffic signal control can mitigate traffic congestion and reduce travel time. A model-free reinforcement learning (RL) approach is a powerful framework for learning a responsive traffic control policy for short-term traffic demand changes without prior ...
Tomoki Nishi +3 more
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Convolutional neural net bagging for online visual tracking
Computer Vision and Image Understanding, 2016The proposed CNN bagging method is simple yet effective.It addresses the label noise and model uncertainty problems simultaneously for CNN-based trackers.The state-of-the-art performances on 3 recent benchmarks i.e., CVPR2013, VOT2013 and TB50 illustrate the validity of the proposed algorithm.
Hanxi Li, Yi Li, Fatih Porikli
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Pulse-Net: Dynamic Compression of Convolutional Neural Networks
2019 IEEE 5th World Forum on Internet of Things (WF-IoT), 2019Convolutional Neural Networks (CNNs) are used in a range of computer vision tasks, with state-of-the-art CNNs such as AlexNet and VGG16 constructed using a large number of parameter and multiply-add operations (MACs). These tasks require high computational power and high energy requirements to run the CNNs, making them unsuitable for deployment on In ...
Browne Browne +2 more
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Wavelet J-Net: A Frequency Perspective on Convolutional Neural Networks
2021 International Joint Conference on Neural Networks (IJCNN), 2021It is well acknowledged in image processing domain that the information can be decomposed into different frequency parts and each part has its own merits. However, existing neural networks always ignore the distinctions and straightforwardly feed all the information into neural networks together, treating them equally. In this paper, we propose a novel
Chenglong Bao +3 more
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Generalized Net Model of the Deep Convolutional Neural Network
2020Generalized Nets (GNs) are constructed in a series of papers, representing the functioning and the results of the work of different types of Neural Networks (NNs). In the present research, we show the functioning and the results of the structure of a Convolutional Neural Networks.
Todor Petkov +4 more
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IEEE International Symposium on Biomedical Imaging, 2019
Radiomics and state-of-art convolutional neural networks (CNNs) have demonstrated their usefulness for predicting genotype in gliomas from brain MRI images.
Adnan Ahmad +6 more
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Radiomics and state-of-art convolutional neural networks (CNNs) have demonstrated their usefulness for predicting genotype in gliomas from brain MRI images.
Adnan Ahmad +6 more
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Recognition of traffic signs by convolutional neural nets for self-driving vehicles
Int. J. Knowl. Based Intell. Eng. Syst., 2018In this paper, a comprehensive Convolutional Neural Network (CNN) based classifier “WAF-LeNet” is proposed and developed to be used in traffic signs recognition and identification as an empowerment of autonomous driving technologies.
W. Farag
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