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Combining Language Sources and Robust Semantic Relatedness for Attribute-Based 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., & Schiele, B. (2010). Combining Language Sources and Robust Semantic Relatedness for Attribute-Based Knowledge Transfer. In R. Feris, T. Caetano, C. Lampert, & D. Forsyth (Eds.), First International Workshop on Parts and Attributes in conjunction with ECCV 2010 (pp. 1-14).


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-000F-15BB-1
Zusammenfassung
Knowledge transfer between object classes has been identified as an important tool for scalable recognition. However, determining which knowledge to transfer where remains a key challenge. While most approaches employ varying levels of human supervision, we follow the idea of mining linguistic knowledge bases to automatically infer transferable knowledge. In contrast to previous work, we explicitly aim to design robust semantic relatedness measures and to combine different language sources for attribute-based knowledge transfer. On the challenging Animals with Attributes (AwA) data set, we report largely improved attribute-based zero-shot object class recognition performance that matches the performance of human supervision.