We are organizing a tutorial at CVPR 2010 on Semi-Supervised Learning in Vision. The following is a brief introduction and a set of topics we will cover in the tutorial. Please use the comment section bellow to give us feedback regarding this tutorial. For example, if you would like to see a method or topic which is not covered by the following list, or if you would like to see a topic discussed in more details, please let us know.

We also plan to publish a set of open/closed source software available for semi-supervised learning. So if you would like to have your package included here, please send us a short description of your method and a link to where it can be obtained.

Introduction:⌗

Current supervised approaches obtain high recognition rates if enough labeled training data is available. However, for most practical problems there is simply not enough labeled data available, whereas hand-labeling is tedious and expensive, in some cases not even feasible. This is especially true for applications in computer vision like object recognition and categorization from images and videos, where the human effort is needed to determine the true contents of the media. Semi-supervised methods offer an interesting solution to this problem by learning from both labeled and unlabeled data. These methods try to give an answer to the question: “How to improve classification accuracy using unlabeled data together with the labeled data?”.

This course will cover an introduction to semi-supervised learning, its applications in computer vision, and the open problems and challenges facing the future research in this field. Nowadays, the Internet offers a huge amount of data in form of unlabeled data (or labeled with high degree of uncertainty), and learning from Internet is becoming more and more widespread in computer vision. Therefore, we will have a special focus on issues dealing with large-scale and on-line semi-supervised learning tasks.

The first half of the course will address the basics of semi-supervised learning and its relations to other machine learning domains. It will cover the major works in this field from a unified point of view, and will discuss the advantages and disadvantages of these methods from theoretical and application perspectives. In the second part, we will focus on the applications of the semi-supervised learning in computer vision, and the open challenges and gaps in existing methods.

Slides:⌗

  • Introductions and motivations: Horst Bischof [slides]
  • Theory of Semi-Supervised Learning: Amir Saffari [slides]
  • Algorithms and Applications: Christian Leistner [slides]

Detailed Outline:⌗

  1. Motivations.
  2. A unified perspecive on learning from unlabeled data.
  3. Statistical models for semi-supervised learning.
  4. Semi-supervised learning with margins.
  5. Methods based on manifold learning.
  6. Co-training and learning from multiple-views.
  7. Semi-supervised learning for large-scale problems.
  8. The relations to other fields: unsupervised learning, one-shot learning, transfer learning, and multiple instance learning.
  9. Applications: object recognition and categorization.
  10. Applications: object segmentation.
  11. Applications: object tracking.
  12. Applications: activity recognition.
  13. Open problems and challenges.

Target Audience:⌗

The course will be interesting for those who investigate or apply learning methods in computer vision. The course is directed towards the researchers, practitioners and PhD students working on related topics. The tutorial is self-contained in the sense that it requires a minumum knowledge of basic mathematical concepts, such as statistics and linear algebra.

Organizers:⌗