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检索条件"任意字段=Neural and Stochastic Methods in Image and Signal Processing II"
540 条 记 录,以下是151-160 订阅
MULTI-STREAM SINGLE SHOT SPATIAL-TEMPORAL ACTION DETECTION  26
MULTI-STREAM SINGLE SHOT SPATIAL-TEMPORAL ACTION DETECTION
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26th IEEE International Conference on image processing (ICIP)
作者: Zhang, Pengfei Cao, Yu Liu, Benyuan Univ Massachusetts Dept Comp Sci Lowell MA 01854 USA
We present a 3D Convolutional neural Networks (CNNs) based single shot detector for spatial-temporal action detection tasks. Our model includes: (i) two short-term appearance and motion streams, with single RGB and op... 详细信息
来源: 评论
Global Message Passing in Networks Via Task-Driven Random Walks for Semantic Segmentation of Remote Sensing images  24
Global Message Passing in Networks Via Task-Driven Random Wa...
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2020 24th ISPRS Congress on Technical Commission ii
作者: Mou, L. Hua, Y. Jin, P. Zhu, X.X. German Aerospace Center Wessling Germany Signal Processing in Earth Observation Technical University of Munich Munich Germany
The capability of globally modeling and reasoning about relations between image regions is crucial for complex scene understanding tasks such as semantic segmentation. Most current semantic segmentation methods fall b... 详细信息
来源: 评论
BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets
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Nature methods 2023年 第6期20卷 824-835页
作者: Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro Bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer E Bouchard Lin Gu Weidong Cai Shuiwang Ji Badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou Erhan Bas Eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua Bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-Bellido Jinhyun Kim Edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch Erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit
BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is r... 详细信息
来源: 评论
Superpixel Segmentation Via Convolutional neural Networks with Regularized Information Maximization
Superpixel Segmentation Via Convolutional Neural Networks wi...
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IEEE International Conference on Acoustics, Speech and signal processing
作者: Teppei Suzuki Denso IT Laboratory Inc. Tokyo Japan
We propose an unsupervised superpixel segmentation method by optimizing a randomly-initialized convolutional neural network (CNN) in inference time. Our method generates superpixels via CNN from a single image without...
来源: 评论
Accurate Single image Super-Resolution Using Deep Aggregation Network  26th
Accurate Single Image Super-Resolution Using Deep Aggregatio...
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26th International Conference on neural Information processing (ICONIP) of the Asia-Pacific-neural-Network-Society (APNNS)
作者: Chen, Xiaozhen Lu, Yao Wang, Xuebo Li, Weiqi Wang, Zijian Beijing Inst Technol Beijing Lab Intelligent Informat Technol Beijing Peoples R China China Cent Televis Beijing Peoples R China
Recent studies have shown that effectively combining rich representations of convolution neural network can significantly boost the performance of single image super resolution. Although dense skip connections can agg... 详细信息
来源: 评论
Graphs for deep learning representations
Graphs for deep learning representations
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作者: Rosar Kós Lassance, Carlos Eduardo Ecole nationale supérieure Mines-Télécom Atlantique Bretagne Pays de la Loire
学位级别:博士
Ces dernières années, les méthodes d'apprentissage profond ont atteint l'état de l'art dans une vaste gamme de tâches d'apprentissage automatique, y compris la classification d... 详细信息
来源: 评论
Learning dynamical systems in noise using convolutional neural networks
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CHAOS 2020年 第10期30卷 103125-103125页
作者: Mukhopadhyay, Sumona Banerjee, Santo York Univ Elect Engn & Comp Sci 4700 Keele St Toronto ON M3J 1P3 Canada Politecn Torino Dept Math Sci Corso Duca Abruzzi 24 I-10129 Turin Italy
The problem of distinguishing deterministic chaos from non-chaotic dynamics has been an area of active research in time series analysis. Since noise contamination is unavoidable, it renders deterministic chaotic dynam...
来源: 评论
Automatic detection of passable roads after floods in remote sensed and social media data
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signal processing-image COMMUNICATION 2019年 74卷 110-118页
作者: Ahmad, Kashif Pogorelov, Konstantin Riegler, Michael Ostroukhova, Olga Halvorsen, Pal Conci, Nicola Dahyot, Rozenn Trinity Coll Dublin Sch Comp Sci & Stat Adapt Ctr Dublin Ireland Simula Res Lab Oslo Norway Sbnula Metropolitan Ctr Digital Engn Oslo Norway Univ Oslo Oslo Norway Res Inst Multiprocessor Computat Syst Taganrog Russia Univ Trento Trento Italy
This paper addresses the problem of floods classification and floods aftermath detection based on both social media and satellite imagery. Automatic detection of disasters such as floods is still a very challenging ta... 详细信息
来源: 评论
A BENCHMARK STUDY OF BACKDOOR DATA POISONING DEFENSES FOR DEEP neural NETWORK CLASSIFIERS AND A NOVEL DEFENSE  29
A BENCHMARK STUDY OF BACKDOOR DATA POISONING DEFENSES FOR DE...
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IEEE 29th International Workshop on Machine Learning for signal processing (MLSP)
作者: Xiang, Zhen Miller, David J. Kesidis, George Penn State Univ State Coll PA 16801 USA
While data poisoning attacks on classifiers were originally proposed to degrade a classifier's usability, there has been strong recent interest in backdoor data poisoning attacks, where the classifier learns to cl... 详细信息
来源: 评论
SOFT DROPOUT AND ITS VARIATIONAL BAYES APPROXIMATION  29
SOFT DROPOUT AND ITS VARIATIONAL BAYES APPROXIMATION
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IEEE 29th International Workshop on Machine Learning for signal processing (MLSP)
作者: Xie, Jiyang Ma, Zhanyu Zhang, Guoqiang Xue, Jing-Hao Tan, Zheng-Hua Guo, Jun Beijing Univ Posts & Telecommun Pattern Recognit & Intelligent Syst Lab Beijing Peoples R China Univ Technol Sydney Sch Elect & Data Engn Sydney NSW Australia UCL Dept Stat Sci London England Aalborg Univ Dept Elect Syst Aalborg Denmark
Soft dropout, a generalization of standard "hard" dropout, is introduced to regularize the parameters in neural networks and prevent overfitting. We replace the "hard" dropout mask following a Bern... 详细信息
来源: 评论