This is a job-talk in the context of the Lise Meitner Award Fellowship

http://lear.inrialpes.fr/people/akata/

AG 1, AG 2, AG 3, AG 4, AG 5, RG1, SWS, MMCI   Info

MPI Audience

English

45 Minutes

Saarbrücken

Building algorithms that classify images on a large scale is an essential task due to the difficulty in searching massive amount of unlabeled visual data available on the Internet. We aim at classifying images based on their content to simplify the manageability of such large-scale collections. Large-scale image classification is a difficult problem as datasets are large with respect to both the number of images and the number of classes. Some of these classes are fine grained and they may not contain any labeled representatives. In this thesis, we use state-of-the-art image representations and focus on efficient learning methods. Our contributions are (1) a benchmark of learning algorithms for large scale image classification, and (2) a novel learning algorithm based on label embedding for learning with scarce training data.

Bernt Schiele

--email hidden

passcode not visible

logged in users only