Visual Knowledge Transfer Using Semantic Relatedness Measures
Marcus Rohrbach · TUbilio (Technical University of Darmstadt) · 2009
Recognition of object classes has advanced to master difficult scenarios such as background clutter or multiple objects per image.Scaling to large numbers of classes, however, remains a challenge for state-of-the-art recognition approaches.In order to exploit similarities between classes, knowledge transfer between classes has been advocated.In this paper we examine the special case of transferring knowledge from known to unseen classes (zero-shot recognition).In previous work the decision which knowledge to transfer has been provided mostly by supervision in the form of manual associations between known and unseen classes or a few training examples, limiting the scalability of these approaches.We promote semantic relatedness to replace supervision in order to provide the missing link between the sources (known classes) and targets (unseen classes) of knowledge transfer.We provide a rigorous experimental evaluation of several state-of-the-art semantic relatedness measures and language resources which we evaluate on the challenging Animals with Attributes image dataset.This extensive evaluation provides insights into the different qualities and applicability of the different measures and resources.