Rohrbach, Marcus ; Stark, Michael ; Szarvas, György ; Schiele, Bernt ; Gurevych, Iryna (2010)
What Helps Where - And Why? Semantic Relatedness for Knowledge Transfer.
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (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.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2010 |
| Autor(en): | Rohrbach, Marcus ; Stark, Michael ; Szarvas, György ; Schiele, Bernt ; Gurevych, Iryna |
| Art des Eintrags: | Bibliographie |
| Titel: | What Helps Where - And Why? Semantic Relatedness for Knowledge Transfer |
| Sprache: | Englisch |
| Publikationsjahr: | Juni 2010 |
| Buchtitel: | Proceedings of the 23rd IEEE Conference on Computer Vision and Pattern Recognition |
| URL / URN: | https://ieeexplore.ieee.org/document/5540121/ |
| Zugehörige Links: | |
| Kurzbeschreibung (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. |
| Freie Schlagworte: | Semantic Information Management;UKP_a_SIM |
| ID-Nummer: | TUD-CS-2010-0079 |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Ubiquitäre Wissensverarbeitung |
| Hinterlegungsdatum: | 31 Dez 2016 14:29 |
| Letzte Änderung: | 24 Jan 2020 12:03 |
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