Combining Language Sources and Robust Semantic Relatedness for Attribute-Based Knowledge Transfer
Abstract
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.
Cite
Text
Rohrbach et al. "Combining Language Sources and Robust Semantic Relatedness for Attribute-Based Knowledge Transfer." European Conference on Computer Vision Workshops, 2010. doi:10.1007/978-3-642-35749-7_2Markdown
[Rohrbach et al. "Combining Language Sources and Robust Semantic Relatedness for Attribute-Based Knowledge Transfer." European Conference on Computer Vision Workshops, 2010.](https://mlanthology.org/eccvw/2010/rohrbach2010eccvw-combining/) doi:10.1007/978-3-642-35749-7_2BibTeX
@inproceedings{rohrbach2010eccvw-combining,
title = {{Combining Language Sources and Robust Semantic Relatedness for Attribute-Based Knowledge Transfer}},
author = {Rohrbach, Marcus and Stark, Michael and Szarvas, György and Schiele, Bernt},
booktitle = {European Conference on Computer Vision Workshops},
year = {2010},
pages = {15-28},
doi = {10.1007/978-3-642-35749-7_2},
url = {https://mlanthology.org/eccvw/2010/rohrbach2010eccvw-combining/}
}