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科研机构
北京航空航天大学 [7]
沈阳自动化研究所 [5]
兰州理工大学 [1]
内容类型
会议论文 [13]
发表日期
2019 [13]
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发表日期:2019
内容类型:会议论文
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Small-sample image classification method of combining prototype and margin learning
会议论文
Lanzhou, China, November 18, 2019 - November 21, 2019
作者:
Li, Xiaoxu
;
Yu, Liyun
;
Cao, Jie
;
Chang, Dongliang
;
Ma, Zhanyu
收藏
  |  
浏览/下载:0/0
  |  
提交时间:2020/11/15
Classification (of information)
Deep neural networks
Image classification
Large dataset
Learning systems
Neural networks
Classification methods
Ensemble learning
Ensemble methods
Large-scale datasets
Prototype and margin learning
Prototype learning
Small sample datum
Small samples
Multi-scale Densely Connected Dehazing Network
会议论文
Shenyang, China, August 8-11, 2019
作者:
Zhang Z(张箴)
;
Tan JD(田建东)
;
Cui T(崔童)
;
Tang YD(唐延东)
收藏
  |  
浏览/下载:40/0
  |  
提交时间:2019/09/05
Deep learning image dehazing
Multi-scale dense network
One-in-all training
Large-scale dataset
Non-concentric Circular Texture Removal for Workpiece Defect Detection
会议论文
Shenyang, China, August 8-11, 2019
作者:
Chen, Heping
;
Xi N(席宁)
;
Qin SJ(秦书嘉)
;
Guo, Di
收藏
  |  
浏览/下载:19/0
  |  
提交时间:2019/09/05
Defect detection
Non-concentric circle
Small dataset
End-to-End Video Captioning with Multitask Reinforcement Learning
会议论文
2019 IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION (WACV), 2019-01-01
作者:
Li, Lijun
;
Gong, Boqing
收藏
  |  
浏览/下载:7/0
  |  
提交时间:2019/12/30
Computer vision
Large dataset
Machine learning
Recurrent neural networks
Convolutional neural network
Hardware constraints
Input videos
Large margins
Process video
Reinforcement learning approach
Search spaces
Testing phase
Reinforcement learning
An Efficient Density-Based Local Outlier Detection Approach for Scattered Data
会议论文
IEEE ACCESS, 2019-01-01
作者:
Su, Shubin
;
Xiao, Limin
;
Ruan, Li
;
Gu, Fei
;
Li, Shupan
收藏
  |  
浏览/下载:5/0
  |  
提交时间:2019/12/30
Outlier detection
local outlier factor
neighborhood variance
rough clustering
scattered dataset
Insulator Fault Recognition Based on Spatial Pyramid Pooling Networks with Transfer Learning (Match 2018)
会议论文
ICARM 2018 - 2018 3rd International Conference on Advanced Robotics and Mechatronics
作者:
Bai, R.
;
Cao, H.
;
Yu, Y.
;
Wang, F.
;
Dang, W.
收藏
  |  
浏览/下载:8/0
  |  
提交时间:2019/12/30
Deep neural networks
Electric insulators
Electric power transmission
Neural networks
Robotics
Fault recognition
small dataset
Spatial pyramids
Transfer learning
variance size of images
Image processing
Improving the Optical Flow Accuracy Based on the Total Variation of Local-Global method
会议论文
Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018, 2018-12-10
作者:
Zhang, Y.
;
Fan, H.
;
Zheng, J.
;
Zhang, C.
收藏
  |  
浏览/下载:10/0
  |  
提交时间:2019/12/30
Big data
Exponential functions
Median filters
Brightness constancies
Combined local-global
Flow accuracy
Global methods
Median filtering
Middlebury dataset
Optical flow estimation
Total variation
Optical flows
An Efficient Insertion Operator in Dynamic Ridesharing Services
会议论文
2019 IEEE 35TH INTERNATIONAL CONFERENCE ON DATA ENGINEERING (ICDE 2019), 2019-01-01
作者:
Xu, Yi
;
Tong, Yongxin
;
Shi, Yexuan
;
Tao, Qian
;
Xu, Ke
收藏
  |  
浏览/下载:17/0
  |  
提交时间:2019/12/30
Efficiency
Large dataset
Traffic control
Travel time
Index structure
Insertion
Intelligent transportation
Large-scale datasets
Origin-destination pairs
Ride-sharing
Time complexity
Time efficiencies
Dynamic programming
A monocular ORB-SLAM in dynamic environments
会议论文
2018 INTERNATIONAL CONFERENCE ON COMPUTER INFORMATION SCIENCE AND APPLICATION TECHNOLOGY, 2019-01-01
作者:
Cui, Linyan
;
Wen, Fei
收藏
  |  
浏览/下载:16/0
  |  
提交时间:2019/12/30
Physics
Application range
Current frame
Dynamic environments
Image blocks
Localization and mappings
Public dataset
Reference frame
Statistical tests
Data Mining and Recommendation of Engineering Note Items in MBD Dataset
会议论文
PROCEEDINGS OF 2019 11TH INTERNATIONAL CONFERENCE ON COMPUTER AND AUTOMATION ENGINEERING (ICCAE 2019), 2019-01-01
作者:
Yu Yong
;
Hu Deyu
;
Wang Hong
;
Zhao Gang
收藏
  |  
浏览/下载:1/0
  |  
提交时间:2019/12/30
association rules
MBD dataset
engineering note items
data mining
recommendation
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