Learning for Cognitive Vision

The main focus of this project part can be summarized as an attempt to create more autonomous learning algorithms for vision systems. We want to decrease the amount of human intervention which is necessary for successful learning: selection and labelling of training images, segmentation by hand, annotation of videos, etc. The motivation to focus research into this direction is twofold. First, human intervention is expensive and large scale vision systems seem feasible only if the need for such intervention can be reduced. Second, learning in biological cognitive systems seems to be rather autonomous with only very weak external supervision or feedback. Several interrelated research tasks will investigate approaches towards such more autonomous cognitive vision systems:
  1. The conservative learning framework is a bootstrap approach to learning where the performance of different but communicating learning algorithms is improved through co-training with minimal human intervention.
  2. Complementing conservative learning, active learning requests labelling only for the most informative examples. We will extend our boosting algorithm for object categorization to active learning.
  3. As a main tool for conservative learning and active learning we will research online boosting.
  4. As a more technical goal we want to reduce the amount of manual tuning for vision algorithms and instead use learning to improve the performance of vision components.
  5. Finally we will tackle the very ambitious goal to aggregate visual primitives into combined primitives to obtain short descriptions of scenes and objects.