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CVPR
2010
IEEE

What Helps Where - And Why? Semantic Relatedness for Knowledge Transfer

14 years 1 days ago
What Helps Where - And Why? Semantic Relatedness for Knowledge Transfer
Remarkable performance has been reported to recognize single object classes. Scalability to large numbers of classes however remains an important challenge for today's recognition methods. Several authors have promoted knowledge transfer between classes as a key ingredient to address this challenge. However, in previous work the decision which knowledge to transfer has required either manual supervision or at least a few training examples limiting the scalability of these approaches. In this work we explicitly address the question of how to automatically decide which information to transfer between classes without the need of any human intervention. For this we tap into linguistic knowledge bases to provide the semantic link between sources (what) and targets (where) of knowledge transfer. We provide a rigorous experimental evaluation of different knowledge bases and state-of-the-art techniques from Natural Language Processing which goes far beyond the limited use of language in ...
Marcus Rohrbach, Michael Stark, Gyö Szarvas, Bern
Added 31 Mar 2010
Updated 14 May 2010
Type Conference
Year 2010
Where CVPR
Authors Marcus Rohrbach, Michael Stark, Gyö Szarvas, Bernt Schiele, Iryna Gurevych
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