The first International Workshop on Deep Learning for Pattern Recognition (DLPR 2016)
in conjunction with ICPR 2016
December 4, 2016. Cancun Center, Cancun, Q.Roo, Mexico
The major goal of this workshop is to provide a platform for researchers or graduate students around the world to share their progresses on deep learning for pattern recognition.
Paper Submission
Paper Submission must be written in English, with length between four and six double column pages adhering to the ICPR conference submission guidelines. The templates for 6 pages length Paper (A4 format) in Word or Latex can be downloaded from (http://www.icpr2016.org/site/guideline-for-initial-submission/).
Submission is handled through the EasyChair system avaiable from here. Reviewing will be single-blind. In submitting a paper the authors acknowledge that no paper substantially similar in content has been or will be submitted elsewhere during the DLPR16 review period. Accepted papers that were presented at the workshop will be published via IEEE Xplore. Some of accepted papers will be invited to the Journal Special Issues of Pattern Recognition Letters (Elsevier) and Frontier of Computer Science (Springer).
Poster Submission
This workshop will follow the Vision And Learning SEminar (VALSE) fashion to invite posters. Authors of recent relevant papers published in highly selective international journals and conferences are encouraged to contribute in the poster session providing highlights of their work. Poster application is by e-mail to Workshop Chair at [email protected]. Accepted posters that were invited will be presented at the workshop. Authors of the invited posters do not have to pay any registration fee for this workshop, only required registering to the main conference of ICPR16
Important dates (tentative)
31 Aug. 2016 Paper submission deadline
15 Sep. 2016 Notification of paper acceptance
30 Oct. 2016 Poster submission deadline
7 Nov. 2016 Notification of poster acceptance
TBA 2016 Camera-ready copy due
4 December 2016 Workshop
Scope and Topics
- Deep learning architectures for pattern recognition
- Optimization for deep learning
- Sparse coding in deep learning
- Transfer learning for deep learning
- Deep learning for feature representation
- Deep learning for facial analysis
- Deep learning for object recognition
- Deep learning for scene understanding
- Deep learning for document analysis
- Deep learning for dimension reduction
- Deep learning for activity recognition
- Deep learning for semantic segmentation
- Deep learning for generative modeling
- Deep learning for biometrics
- Multi-modal deep learning
- Performance evaluation of deep learning algorithms
Keynote Speakers
TBA
Workshop Chairs
Xiang Bai Huazhong University Science and Technology
Zhaoxiang Zhang Institute of Automation, Chinese Academy of Sciences
Shiguang Shan Institute of Computing Technology, Chinese Academy of Sciences
Chunhua Shen University of Adelaide
Yi Fang New York University Abu Dhabi and New York University
Jingdong Wang Microsoft Research Asia
Yangqing Jia Facebook
Shuicheng Yan National University of Singapore