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

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.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-000F-15DD-4 Version Permalink: https://hdl.handle.net/11858/00-001M-0000-0024-3056-4
Genre: Conference Paper

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 Creators:
Rohrbach, Marcus1, Author           
Stark, Michael1, Author           
Szarvas, György2, Author
Gurevych, Iryna2, Author
Schiele, Bernt1, Author           
Affiliations:
1Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society, ou_1116547              
2External Organizations, ou_persistent22              

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 Abstract: 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.

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Language(s): eng - English
 Dates: 20102010
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: eDoc: 536668
DOI: 10.1109/CVPR.2010.5540121
URI: http://dx.doi.org/10.1109/CVPR.2010.5540121
Other: Local-ID: C12576EE0048963A-3A1B857E4F1356ACC12577DF005C92B6-rohrbach10cvpr
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Title: 2010 IEEE Conference on Computer Vision and Pattern Recognition
Place of Event: San Francisco, USA
Start-/End Date: 2010-06-15 - 2010-06-17

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Title: 2010 IEEE Conference on Computer Vision and Pattern Recognition
  Abbreviation : CVPR 2010
Source Genre: Proceedings
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Publ. Info: Piscataway, NJ : IEEE
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 910 - 917 Identifier: ISBN: 978-1-4244-6984-0