Deep Learning, which can be treated as the most significant breakthrough in the past 10 years in the field of pattern recognition and machine learning, has greatly affected the methodology of related fields like computer vision and achieved terrific progress in both academy and industry. It can be seen as a resolution to change the whole pattern recognition system. It achieved an end-to-end pattern recognition, merging the previous steps of pre-processing, feature extraction, classifier design and post-processing. It is expected that the development of deep learning theories and applications would further influence the field of pattern recognition. The deep learning technique has been widely used in face analysis, biometrics, object recognition, document analysis, scene understanding and etc. The purpose of this workshop is to bring together researchers who are working on developing deep learning and pattern recognition to report or exchange their progresses on deep learning for pattern recognition. The major goal of this workshop is to provide a platform for researchers or graduate students around the world to report or exchange their progresses on deep learning for pattern recognition.

We call for both regular paper submissions to the workshop and poster submissions for exhibitions following the Vision And Learning SEminar (VALSE) fashion.


  • Submission Requirements for camera ready are updated, please refer to the submission page. New
  • Call for Paper is announced here! New

Important dates

  • Paper Submission deadline: June 15, 2018
  • Notification of paper acceptance: July 30, 2018
  • Camera ready dealine: August 10, 2018
  • Workshop Date: August 20, 2018

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
  • Video face recognition
  • Video facial expression recognition
  • Face and facial expression recognition from facial dynamics
  • Face detection and tracking from video
  • Multi-face clustering from video
  • 3D face modeling from video
  • Applications of video face recognition
  • Applications of video facial expression recognition