[New!] Presentation about the homography dataset

Introduction

Local features are at the centre of many fundamental computer vision problems. While local features have been subject of study in computer vision for almost twenty years, recent significant progress in this area has led to substantial improvements in many computer vision tasks including registration, stereo vision, motion estimation, matching, retrieval, recognition of objects and actions. With the advent of modern deep learning techniques, an area of particular interest is the development of learnable local feature descriptors. While this direction has already proved to be quite fruitful, there are still many challenges left for a variety of problem classes, including defining appropriate training sets, evaluation protocols, and benchmarks. This workshop will promote both a technical discussion on methods to construct and learn better local features as well as new approaches to a rigorous and realistic evaluation of their performance. Accepted papers and extended abstracts will be presented as posters / spotlights at the workshop, in areas including, but not limited to:

• Review of the state of the art and recent developments.
• Local features: open problems.
• Deep learning and local features.
• Review of datasets for learning and evaluation of descriptors.
• Introduction of novel datasets and benchmark protocols.
• Methods to make deep detectors/descriptors practical. Pushing the limits in terms of size, convolutions, binary output etc.

We also invite authors to submit results to our descriptor evaluation challenge. The final results will be presented at the workshop.