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检索条件"机构=CAS Key Lab of Technology in Geo-spatial Information Processing and Application System"
214 条 记 录,以下是31-40 订阅
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Transform-invariant convolutional neural networks for image classification and search  24
Transform-invariant convolutional neural networks for image ...
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24th ACM Multimedia Conference, MM 2016
作者: Shen, Xu Tian, Xinmei He, Anfeng Sun, Shaoyan Tao, Dacheng CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei Anhui230027 China Centre for Quantum Computation and Intelligent Systems Faculty of Engineering and Information Technology University of Technology SydneyNSW2007 Australia
Convolutional neural networks (CNNs) have achieved stateof-the-art results on many visual recognition tasks. However, current CNN models still exhibit a poor ability to be invariant to spatial transformations of image... 详细信息
来源: 评论
3-D context entropy model for improved practical image compression
arXiv
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arXiv 2020年
作者: Guo, Zongyu Wu, Yaojun Feng, Runsen Zhang, Zhizheng Chen, Zhibo CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China
In this paper, we present our image compression framework designed for CLIC 2020 competition. Our method is based on Variational AutoEncoder (VAE) architecture which is strengthened with residual structures. In short,... 详细信息
来源: 评论
A Multicast Architecture of SVC Streaming Over OpenFlow Networks
A Multicast Architecture of SVC Streaming Over OpenFlow Netw...
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IEEE Global Communications Conference
作者: Enzhong Yang Yongyi Ran Shuangwu Chen Jian Yang CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China
In video streaming service, multicast mode is a promising way to complement unicast delivery of content, since it deliveries the video content to a broad range of receivers. It is considered as an efficient scheme to ... 详细信息
来源: 评论
3-D Context Entropy Model for Improved Practical Image Compression
3-D Context Entropy Model for Improved Practical Image Compr...
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IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
作者: Zongyu Guo Yaojun Wu Runsen Feng Zhizheng Zhang Zhibo Chen CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China
In this paper, we present our image compression framework designed for CLIC 2020 competition. Our method is based on Variational AutoEncoder (VAE) architecture which is strengthened with residual structures. In short,... 详细信息
来源: 评论
Accelerate Neural Image Compression with Channel-Adaptive Arithmetic Coding
Accelerate Neural Image Compression with Channel-Adaptive Ar...
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IEEE International Symposium on Circuits and systems
作者: Zongyu Guo Jun Fu Runsen Feng Zhibo Chen CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China
We have witnessed the revolutionary progress of learned image compression despite a short history of this field. Some challenges still remain such as computational complexity that prevent the practical application of ... 详细信息
来源: 评论
Action recognition with novel high-level pose features
Action recognition with novel high-level pose features
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IEEE International Conference on Multimedia and Expo Workshops (ICMEW)
作者: Jiayi Fan Zhengjun Zha Xinmei Tian University of Science and Technology of China CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System
Recently high-level pose features (HLPF) have been shown to be efficient for action recognition in joint-annotated tasks. However, the relative positions between pairs of joints in actual situations and the spatio-tem... 详细信息
来源: 评论
GUID-based mobile visual communication using NDN mechanism
GUID-based mobile visual communication using NDN mechanism
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IEEE Visual Communications and Image processing (VCIP)
作者: Yuanzun Zhang Xiaobin Tan Hao Liu Weiping Li CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China
With the explosive growth in the number of mobile terminals, the demand for visual communication with mobility is increasing. However, traditional solutions for mobility over IP network cannot always meet the demand o... 详细信息
来源: 评论
Learned video compression with feature-level residuals
arXiv
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arXiv 2020年
作者: Feng, Runsen Wu, Yaojun Guo, Zongyu Zhang, Zhizheng Jin, Xin Chen, Zhibo CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China
In this paper, we present an end-to-end video compression network for P-frame challenge on CLIC. We focus on deep neural network (DNN) based video compression, and improve the current frameworks from three aspects. Fi... 详细信息
来源: 评论
Learned Video Compression with Feature-level Residuals
Learned Video Compression with Feature-level Residuals
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IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
作者: Runsen Feng Yaojun Wu Zongyu Guo Zhizheng Zhang Zhibo Chen CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China
In this paper, we present an end-to-end video compression network for P-frame challenge on CLIC. We focus on deep neural network (DNN) based video compression, and improve the current frameworks from three aspects. Fi... 详细信息
来源: 评论
An interactive system for low-poly illustration generation from images using adaptive thinning
An interactive system for low-poly illustration generation f...
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IEEE International Conference on Multimedia and Expo (ICME)
作者: Yiting Ma Xuejin Chen Yu Bai University of Science and Technology of China CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System
Low-poly style illustrations, which have 3D abstract appearance, have become a popular stylish recently. Most previous methods require special knowledges in 3D modeling and need tedious interactions. We present an int... 详细信息
来源: 评论