I agree that this site is using cookies. You can find further informations
here
.
X
Login
Merkliste (
0
)
Home
About us
Home About us
Our history
Profile
Press & public relations
Friends
The library in figures
Exhibitions
Projects
Training, internships, careers
Films
Services & Information
Home Services & Information
Lending and interlibrary loans
Returns and renewals
Training and library tours
My Account
Library cards
New to the library?
Download Information
Opening hours
Learning spaces
PC, WLAN, copy, scan and print
Catalogs and collections
Home Catalogs and Collections
Rare books and manuscripts
Digital collections
Subject Areas
Our sites
Home Our sites
Central Library
Law Library (Juridicum)
BB Business and Economics (BB11)
BB Physics and Electrical Engineering
TB Engineering and Social Sciences
TB Economics and Nautical Sciences
TB Music
TB Art & Design
TB Bremerhaven
Contact the library
Home Contact the library
Staff Directory
Open access & publishing
Home Open access & publishing
Reference management: Citavi & RefWorks
Publishing documents
Open Access in Bremen
zur Desktop-Version
Toggle navigation
Merkliste
1 Ergebnisse
1
Occlusion Data Augmentation for Object Detectors based on R..:
, In:
2020 20th International Conference on Control, Automation and Systems (ICCAS)
,
Heo, Jiseong
;
Wang, Yooseung
;
Park, Jihun
- p. 1068-1072 , 2020
Link:
https://doi.org/10.23919/ICCAS50221.2020.9268339
RT T1 <cTitle>2020 20th International Conference on Control, Automation and Systems (ICCAS)</cTitle>: T1 <Title>Occlusion Data Augmentation for Object Detectors based on Random Erasing and Bounding Box Tailoring</Title> UL https://suche.suub.uni-bremen.de/peid=ieee-9268339&Exemplar=1&LAN=DE A1 Heo, Jiseong A1 Wang, Yooseung A1 Park, Jihun YR 2020 SN 2642-3901 K1 Detectors K1 Object detection K1 Military vehicles K1 Buildings K1 Training K1 Testing K1 Gaussian distribution K1 data augmentation K1 object detection K1 deep learning K1 convolutional neural network K1 occlusion SP 1068 OP 1072 LK http://dx.doi.org/https://doi.org/10.23919/ICCAS50221.2020.9268339 DO https://doi.org/10.23919/ICCAS50221.2020.9268339 SF ELIB - SuUB Bremen
Export
RefWorks (nur Desktop-Version!)
Flow
(Zuerst in
Flow
einloggen, dann importieren)
