The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.
What helps where – and why? Semantic relatedness for knowledge transfer
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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Zero-Shot Learning—A Comprehensive Evaluation of the Good, the Bad and the Ugly
Xian, Y., Lampert, C. H., Schiele, B., & Akata, Z. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2857768 · Cited by 1,678
Self-citationAttribute-Based Classification for Zero-Shot Visual Object Categorization
Lampert, C. H., Nickisch, H., & Harmeling, S. (2014) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.140 · Cited by 1,627
Zero-Shot Learning — The Good, the Bad and the Ugly
Xian, Y., Schiele, B., & Akata, Z. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.328 · Cited by 891
Self-citationParikh, D., & Grauman, K. (2011) · 2011 International Conference on Computer Vision · doi:10.1109/iccv.2011.6126281 · Cited by 851
A Survey of Zero-Shot Learning: Settings, Methods, and Applications
Wang, W., Zheng, V. W., Yu, H., & Miao, C. (2019) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3293318 · Cited by 528
Metadata
- Authors
- Rohrbach, M., Stark, M., Szarvas, G., Gurevych, I., & Schiele, B.
- Venue
- 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2010-06
- Pages
- 910-917
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W1992454046
- Cited by
- 273
- References
- 31
- FWCI
- 18.71
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Rohrbach, M., Stark, M., Szarvas, G., Gurevych, I., & Schiele, B. (2010). What helps where – and why? Semantic relatedness for knowledge transfer. 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/cvpr.2010.5540121
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