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Xi Ouyang
Researcher at Shanghai Jiao Tong University
Publications - 39
Citations - 1635
Xi Ouyang is an academic researcher from Shanghai Jiao Tong University. The author has contributed to research in topics: Computer science & Convolutional neural network. The author has an hindex of 13, co-authored 30 publications receiving 923 citations. Previous affiliations of Xi Ouyang include Huazhong University of Science and Technology & Panasonic.
Papers
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Journal ArticleDOI
A deep hybrid learning model to detect unsafe behavior: Integrating convolution neural networks and long short-term memory
TL;DR: The results reveal that the developed hybrid model (CNN + LSTM) is able to accurately detect safe/unsafe actions conducted by workers on-site and exceeds the current state-of-the-art descriptor-based methods for detecting points of interest on images.
Journal ArticleDOI
Dual-Sampling Attention Network for Diagnosis of COVID-19 From Community Acquired Pneumonia
Xi Ouyang,Jiayu Huo,Liming Xia,Fei Shan,Jun Liu,Zhanhao Mo,Fuhua Yan,Zhongxiang Ding,Qi Yang,Bin Song,Feng Shi,Huan Yuan,Ying Wei,Xiaohuan Cao,Yaozong Gao,Dijia Wu,Qian Wang,Dinggang Shen +17 more
TL;DR: Wang et al. as mentioned in this paper developed a dual-sampling attention network to automatically diagnose COVID-19 from the community acquired pneumonia (CAP) in chest computed tomography (CT), and proposed a novel online attention module with a 3D convolutional network (CNN) to focus on the infection regions in lungs when making decisions of diagnoses.
Proceedings ArticleDOI
Sentiment Analysis Using Convolutional Neural Network
TL;DR: This paper proposes a framework called Word2vec + Convolutional Neural Network (CNN) using the word2vec proposed by Google to compute vector representations of words, which will be the input for the CNN, and designs a suitable CNN architecture for the sentiment analysis task.
Posted Content
Dual-Sampling Attention Network for Diagnosis of COVID-19 from Community Acquired Pneumonia
Xi Ouyang,Jiayu Huo,Liming Xia,Fei Shan,Jun Liu,Zhanhao Mo,Fuhua Yan,Zhongxiang Ding,Qi Yang,Bin Song,Feng Shi,Huan Yuan,Ying Wei,Xiaohuan Cao,Yaozong Gao,Dijia Wu,Qian Wang,Dinggang Shen +17 more
TL;DR: A dual-sampling attention network to automatically diagnose COVID-19 from the community acquired pneumonia (CAP) in chest computed tomography (CT) with a novel online attention module with a 3D convolutional network (CNN) to focus on the infection regions in lungs when making decisions of diagnoses.
Journal ArticleDOI
Convolutional neural networks: Computer vision-based workforce activity assessment in construction
TL;DR: An improved convolutional neural network that integrates Red-Green-Blue (RGB), optical flow, and gray stream CNNs, is proposed to accurately monitor and automatically assess workers' activities associated with installing reinforcement during construction.