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Titlebook: Video Analytics. Face and Facial Expression Recognition; Third International Xiang Bai,Yi Fang,Qiang Ji Conference proceedings 2019 Spring

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發(fā)表于 2025-3-21 20:01:38 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Video Analytics. Face and Facial Expression Recognition
副標(biāo)題Third International
編輯Xiang Bai,Yi Fang,Qiang Ji
視頻videohttp://file.papertrans.cn/983/982898/982898.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Video Analytics. Face and Facial Expression Recognition; Third International  Xiang Bai,Yi Fang,Qiang Ji Conference proceedings 2019 Spring
描述.This book constitutes the proceedings of the Third Workshop on Face and Facial Expression Recognition from Real World Videos, FFER 2018, and the Second International Workshop on Deep Learning for Pattern Recognition, DLPR 2018, held at the 24th International Conference on Pattern Recognition, ICPR 2018, in Beijing, China, in August 2018...The 7 papers presented in this volume were carefully reviewed and selected from 9 submissions. They deal with topics such as histopathological images, action recognition, scene text detection, speech recognition, object classification, presentation attack detection, and driver drowsiness detection..
出版日期Conference proceedings 2019
關(guān)鍵詞artificial intelligence; background subtraction; computer vision; driver drowsiness detection; histopath
版次1
doihttps://doi.org/10.1007/978-3-030-12177-8
isbn_softcover978-3-030-12176-1
isbn_ebook978-3-030-12177-8Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer Nature Switzerland AG 2019
The information of publication is updating

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Three-Stream Convolution Networks After Background Subtraction for Action Recognition,as two important branches of deep learning, Two-Stream relies on optical flow with complex computation and 3D convolution network is difficult for training. In this paper, we propose a novel Three-Stream Convolution networks after feature extraction for action recognition. For feature, we introduce
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Scene Text Detection with a SSD and Encoder-Decoder Network Based Method,twork, which detects text in scene images with one forward propagation and a standard non-maximum suppression subsequently. In order to eliminate few scene background mistaken by Text Localization Neural Network, we propose a Text Verification Model based on the encoder-decoder network. Thus, precis
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Evaluation of Group Convolution in Lightweight Deep Networks for Object Classification,ated residual transformation (defined in ResNeXt that secured 2nd place in the ILSVRC 2016 classification task) for lightweight deep networks. The major contributions to this paper include (i) evaluation of group convolution, (ii) study on the impact of skip connection and various width for lightwei
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Conference proceedings 2019nd International Workshop on Deep Learning for Pattern Recognition, DLPR 2018, held at the 24th International Conference on Pattern Recognition, ICPR 2018, in Beijing, China, in August 2018...The 7 papers presented in this volume were carefully reviewed and selected from 9 submissions. They deal wit
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