ICML 2011 workshop on unsupervised
and transfer learning
Transfer Learning with Applications to
Multiclass Object Detection
Ruslan
Salakhutdinov
MIT,
Massachussetts, USA
We present a hierarchical Bayesian classification model that is able to
transfer higher-level structure, abstracted from object categories that
have many training examples, to learning novel visual categories with
only a few training examples. Unlike many of the existing object
detection and recognition systems that treat different classes as
unrelated entities, our model learns both a hierarchy for sharing
visual appearance across 200 object categories and hierarchical
parameters. We demonstrate on the challenging object localization and
detection task that the proposed model substantially improves the
accuracy of the standard single object detectors that ignore
hierarchical structure altogether.