Results 41 to 50 of about 1,925,096 (328)
Blind Image Quality Assessment Using a Deep Bilinear Convolutional Neural Network [PDF]
IEEE transactions on circuits and systems for video technology (Print), 2019 We propose a deep bilinear model for blind image quality assessment that works for both synthetically and authentically distorted images. Our model constitutes two streams of deep convolutional neural networks (CNNs), specializing in two distortion ...
Weixia Zhang +4 more
semanticscholar +1 more source
Detecting Distracted Driving with Deep Learning [PDF]
, 2017 © Springer International Publishing AG 2017Driver distraction is the leading factor in most car crashes and near-crashes. This paper discusses the types, causes and impacts of distracted driving.
A Nabo +9 more
core +2 more sources
A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection [PDF]
European Conference on Computer Vision, 2016 A unified deep neural network, denoted the multi-scale CNN (MS-CNN), is proposed for fast multi-scale object detection. The MS-CNN consists of a proposal sub-network and a detection sub-network.
Zhaowei Cai +3 more
semanticscholar +1 more source
De Computis, 2023 Convolutional neural networks (CNNs) are one of the main types of neural networks used for image recognition and classification. CNNs have several uses, some of which are object recognition, image processing, computer vision, and face recognition.
Mohammad Mustafa Taye
semanticscholar +1 more source
DialogueGCN: A Graph Convolutional Neural Network for Emotion Recognition in Conversation [PDF]
Conference on Empirical Methods in Natural Language Processing, 2019 Emotion recognition in conversation (ERC) has received much attention, lately, from researchers due to its potential widespread applications in diverse areas, such as health-care, education, and human resources.
Deepanway Ghosal +4 more
semanticscholar +1 more source
Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction [PDF]
Italian National Conference on Sensors, 2017 This paper proposes a convolutional neural network (CNN)-based method that learns traffic as images and predicts large-scale, network-wide traffic speed with a high accuracy. Spatiotemporal traffic dynamics are converted to images describing the time and
Xiaolei Ma +5 more
semanticscholar +1 more source
Understanding of Convolutional Neural Network (CNN): A Review
International Journal of Robotics and Control Systems, 2023 The application of deep learning technology has increased rapidly in recent years. Technologies in deep learning increasingly emulate natural human abilities, such as knowledge learning, problem-solving, and decision-making. In general, deep learning can
Purwono Purwono +5 more
semanticscholar +1 more source
ROLLING BEARING FAULT DIAGNOSIS BASED ON FUSION CNN AND PSO-SVM
Jixie qiangdu, 2021 Aiming at the problem that it is difficult to extract subtle fault features in the process of rolling bearing fault identification,this paper proposes a rolling bearing fault diagnosis method based on fusion convolutional neural network and support ...
WANG YongDing, JIN ZiQi
doaj
Long Short-Term Memory Spatial Transformer Network
, 2019 Spatial transformer network has been used in a layered form in conjunction with a convolutional network to enable the model to transform data spatially.
Chen, Tianyue, Feng, Shiyang, Sun, Hao
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Test-object recognition in thermal images [PDF]
Компьютерная оптика, 2019 The paper presents a comparative analysis of several methods for recognition of test-object position in a thermal image when setting and testing characteristics of thermal image channels in an automated mode.
Aleksandr Mingalev +4 more
doaj +1 more source