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

Website http://www.janconf.org/conference/DLCV2019/ | Edit Freely

Category Deep Learning; Computer Vision

Deadline: November 13, 2019 | Date: December 13, 2019-December 15, 2019

Venue/Country: Bangkok, Thailand

Updated: 2019-03-08 10:55:45 (GMT+9)

Call For Papers - CFP

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: vickykongwy126.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: vickykongwy126.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