Description

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.

Call For Papers

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

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 Sensors3D from Single ImagesAction Recognition Adaptive SystemsBiomedical image analysis Biometrics, face and gesture Computational photography, photometryComputer Vision TheoryData Mining for the WebDeep Learning TechniquesDeep model-based and data-efficient reinforcement learningEfficient (Bayesian) inference for deep learningGenerative models as regularizationHyper-parameter optimizationImage and Video SynthesisImage/Video ProcessingLarge-scale generative modellingLarge-scale optimizationLearning representations for reinforcement learningLow-level vision and Image Processing Machine VisionModel structure optimizationMotion and Tracking NeurocomputingRecognition: detection, categorization, indexing and matching Robot Vision Segmentation, grouping and shape representation Semi-supervised learningStatistical learningStructured learningTemporal models with long-term dependenciesUnsupervised/generative modeling