Machine Learning for Intelligent Image and Video Processing

December 2015

ICCV 2015 workshop, Santiago, Chile


Image processing methods are highly relevant in a large variety of industrial and consumer applications. Traditionally some of the successful methods have been derived based on a careful consideration of the particular imaging modality and task, or on an adhoc basis by image processing practitioners. More recently statistical machine learning models have been proposed for tasks such as denoising, deblurring, inpainting, etc., often leading to significant gains in image quality. Machine learning methods require training data to learn about the image statistics and the task, and challenges arise in how this data should be collected and how ground truth is obtained.

The goal of this workshop is to bring together researchers from the image processing and machine learning community to discuss all issues related to machine learning models for image processing applications. In particular,

  • Statistical modelling of image processing tasks
  • Runtime and data efficiency
  • Tractable estimation
  • Deep learning for image processing applications
  • Procedures to obtain ground truth data sets
In all aspects the ICCV community has been at the forefront of developing new ideas and we hope to continue this development through this workshop.

Invited Speakers

Important Dates

  • Submission Deadline: Friday, September 25th, 2015
  • Author Notification: Friday, October 16th, 2015
  • Final Version of submission: Friday, October 23rd, 2015
  • Workshop Date: 17 December 2015


  • Papers should be in ICCV style
  • Maximum paper length is 6 pages (including references)
  • Papers will be reviewed in a double blind process
  • Accepted papers are not published as part of IEEE Proceedings but inofficially on this website
Please use the submission server at


8:55-9:00 Workshop Opening
9:00-9:40 Invited Talk: Michael Brown Data-driven White-Balance: A Cautionary Tale on Using Machine Learning
9:40-10:00 Contributed Talk: Sergio Hernandez Fixed-lag parameter learning for visual tracking
10:00-10:30 Coffee Break with Poster Session
10:30-10:50 Contributed Talk: Jacopo Cavazza and Vittorio Murino People Counting by Huber Loss Regression
10:50-11:30 Invited Talk: Stefan Roth Generative or discriminative modeling for image processing
11:30-11:50 Contributed Talk: Liangke Gui and Louis-Philippe Morency Learning and Transferring Deep ConvNet Representations with Group-Sparse Factorization
11:50-12:00 Concluding Remarks

PC Committee

  • Jeremy Jancsary, Nuance Communications
  • Peter Gehler, MPI for Intelligent Systems
  • Stefan Harmeling, University of Duesseldorf
  • Martin Kiefel, MPI for Intelligent Systems
  • Dilip Krishnan, Google
  • Uwe Schmidt, MPI of Molecular Cell Biology and Genetics
  • Christian Schuler, Google
  • Jian Sun, Microsoft Research
  • Stefan Roth, TU Darmstadt
  • Daniel Zoran, MIT