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Basic Information

The Intl Conference on Deep Learning and Computer Vision (DLCV 2019)

Website: http://www.janconf.org/conference/DLCV2019/

Place

Bangkok, Thailand

Conference Date

Dec 13 - Dec 15, 2019

Submission Deadline

Nov 13, 2019

Venue

Arnoma Grand Bangkok

Subjects: Computer Science and Technologies;

Sponsorship:

Indexing: Google Scholar

Short Description

The Int'l Conference on Deep Learning and Computer Vision (DLCV 2019)

Conference Date: December 13-15, 2019
Conference Venue: Bangkok, Thailand
Website: http://www.janconf.org/conference/DLCV2019/
Online Registration System: http://www.janconf.org/RegistrationSubmission/default.aspx?ConferenceID=1201
Email: vickykongwy@126.com

The Int'l Conference on Deep Learning and Computer Vision (DLCV 2019) will be held in Bangkok, Thailand during December 13-15, 2019. DLCV 2019 will be a valuable and important platform for inspiring Int’l and interdisciplinary exchange at the forefront of Deep Learning and Computer Vision.

If you wish to serve the conference as an invited speaker, please send email to us with your CV. We'll contact with you asap.

Publication and Presentation

Publication: Open Access Journal，please contact us for detailed information
Index: CNKI and Google Scholar
Note: If you want to present your research results but do NOT wish to publish a paper, you may simply submit an Abstract to our Registration System.

Contact Us

Email: vickykongwy@126.com
Tel:+86 150 7134 3477
QQ: 3025797047
WeChat: 3025797047

Attendance Methods

1. Submit full paper ( Regular Attendance+Paper Publication+Presentation )
You are welcome to submit full paper, all the accepted papers will be published by Open access journal.
2. Submit abstract ( Regular Attendance+Abstract+Presentation )
3. Regular Attendance ( No Submission Required )

Call for Papers

3D Computer Vision
3D from Multiview and Sensors
3D from Single Images
Action Recognition
Adaptive Systems
Biomedical image analysis
Biometrics, face and gesture
Computational photography, photometry
Computer Vision Theory
Data Mining for the Web
Deep Learning Techniques
Deep model-based and data-efficient reinforcement learning
Efficient (Bayesian) inference for deep learning
Generative models as regularization
Hyper-parameter optimization
Image and Video Synthesis
Image/Video Processing
Large-scale generative modelling
Large-scale optimization
Learning representations for reinforcement learning
Low-level vision and Image Processing
Machine Vision
Model structure optimization
Motion and Tracking
Neurocomputing
Recognition: detection, categorization, indexing and matching
Robot Vision
Segmentation, grouping and shape representation
Semi-supervised learning
Statistical learning
Structured learning
Temporal models with long-term dependencies
Unsupervised/generative modeling

Contact

E-mail: engii_service@163.com

Tel: 13264702250

Rank: ★★★★

Indexing

Online Proceedings:

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