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Konferenzbeitrag

What helps Where - and Why? Semantic Relatedness for Knowledge Transfer

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons45307

Rohrbach,  Marcus
Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons45541

Stark,  Michael
Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons45383

Schiele,  Bernt
Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society;

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Zitation

Rohrbach, M., Stark, M., Szarvas, G., Gurevych, I., & Schiele, B. (2010). What helps Where - and Why? Semantic Relatedness for Knowledge Transfer. In 2010 IEEE Conference on Computer Vision and Pattern Recognition (pp. 910-917). Piscataway, NJ: IEEE. doi:10.1109/CVPR.2010.5540121.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-000F-15DD-4
Zusammenfassung
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 related work. We also give insights into the applicability (why) of different knowledge sources and similarity measures for knowledge transfer.