Covered Topics

We solicit original contributions that deploy statistical deep learning methods employed to perform various computer vision tasks including, but not limited to:

  • Statistical Understanding of Deep Learning
    • Interpretable deep learning, quantitative measures and analyses
  • Statistical Normalization Methods
    • Feature, weight, gradient and hybrid normalization methods
  • Uncertainty in Deep Learning
    • Uncertainty measures, adversarial methods, intrinsic and extrinsic uncertainty of models
  • Information Theory of Deep Learning
    • Information geometry, information bottleneck, rate distortion, etc.
  • Probabilistic Deep Learning
    • Variational methods, graphical methods, Bayesian learning and inference
    • Bayesian deep learning
    • Neural network architecture search via probabilistic models
  • Stochastic Optimization for Deep Learning
    • Optimization on Riemannian manifolds, topological manifolds, and product manifolds
  • Probabilistic Programming for Deep Learning
    • Scene perception, logical reasoning, autonomous driving
  • Statistical Meta-learning Algorithms
    • Few-shot learning/incremental learning for image classification and beyond
    • Zero-shot learning for high-level vision tasks
  • Reinforcement Learning for Vision Systems
    • RL algorithms and vision problems
  • Causal Deep Learning
    • Causal inference, causal feature learning

Call for Papers

We invite submissions describing works in the domains suggested above or in closely-related areas. Accepted papers will be presented in oral\poster sessions at the workshop and appear in the CVF open access archive. The review process is single-blind. Each paper will receive strong accept (for oral candidate), accept or reject decision. Note that there is no author feedback phase during submission. We will also invite selected papers for submission to a special issue on Statistical Deep Learning for Computer Vision in the International Journal of Computer Vision (IJCV). Extended versions of selected papers will be invited for book chapter publication.


Paper submission deadline: July 31, 2019

Author Notification: Sep 4, 2019

Camera-ready deadline: Sep 25, 2019

Submission Instructions

Format and paper length

A paper submission has to be in English, in pdf format, and at most FOUR pages (excluding references). The paper format must follow the same guidelines as used in the ICCV 2019 submissions.

A latex template is given in the following webpage:

Workshop paper template download.

Submission site

For submission of papers, please go to the webpage: https://cmt3.research.microsoft.com/SDLCV2019.

If you encounter any problems regarding the submission, please feel free to drop us an email at:

Hongyang Li, yangli@ee.cuhk.edu.hk