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.
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Ferrari, V., Zisserman, A.: Learning visual attributes. In: NIPS (2007)
Lampert, C.H., Nickisch, H., Harmeling, S.: Learning to detect unseen object classes by between-class attribute transfer. In: CVPR (2009)
Wang, G., Forsyth, D.: Joint learning of visual attributes, object classes and visual saliency. In: ICCV (2009)
Rohrbach, M., Stark, M., Szarvas, G., Gurevych, I., Schiele, B.: What helps where – and why? semantic relatedness for knowledge transfer. In: CVPR (2010)
Fink, M.: Object classification from a single example utilizing class relevance pseudo-metrics. In: NIPS (2004)
Marszalek, M., Schmid, C.: Semantic hierarchies for visual object recognition. In: CVPR (2007)
Zweig, A., Weinshall, D.: Exploiting object hierarchy: Combining models from different category levels. In: ICCV (2007)
Bart, E., Ullman, S.: Cross-generalization: Learning novel classes from a single example by feature replacement. In: CVPR (2005)
Thrun, S.: Is learning the n-th thing any easier than learning the first. In: NIPS 1996 (1996)
Fei-Fei, L., Fergus, R., Perona, P.: One-shot learning of object categories. PAMI 28 (2006)
Stark, M., Goesele, M., Schiele, B.: A shape-based object class model for knowledge transfer. In: ICCV (2009)
Kumar, N., Berg, A.C., Belhumeur, P.N., Nayar, S.K.: Attribute and simile classifiers for face verification. In: ICCV (2009)
Farhadi, A., Endres, I., Hoiem, D.: Attribute-centeric recognition for cross-category generalization. In: CVPR (2010)
Palatucci, M., Pomerleau, D., Hinton, G., Mitchell, T.: Zero-shot learning with semantic output codes. In: NIPS (2009)
Wang, J., Markert, K., Everingham, M.: Learning models for object recognition from natural language descriptions (2009)
Bart, E., Ullman, S.: Single-example learning of novel classes using representation by similarity. In: BMVC (2005)
Chen, H.H., Lin, M.S., Wei, Y.C.: Novel association measures using web search with double checking. In: ACL-44 (2006)
Delezoide, B., Pitel, G., Borgne, H.L., Greffenstette, G., Moëllic, P.A., Millet, C.: Object/background scene classification in photographs using linguistic statistics from the web. In: OntoImage (2008)
Kemp, C., Tenenbaum, J.B., Griffiths, T.L., Yamada, T., Ueda, N.: Learning systems of concepts with an infinite relational model. In: AAAI (2006)
Osherson, D.N., Stern, J., Wilkie, O., Stob, M., Smith, E.E.: Default probability. Cognitive Science 15, 251–269 (1991)
Wu, T.F., Lin, C.J., Weng, R.C.: Probability estimates for multi-class classification by pairwise coupling. JMLR 2004 (2004)
Fellbaum, C.: WordNet: An Electronical Lexical Database. The MIT Press (1998)
Lin, D.: An information-theoretic definition of similarity. In: ICML (1998)
Zesch, T., Gurevych, I.: Wisdom of crowds versus wisdom of linguists - measuring the semantic relatedness of words. JNLE 16 (2010)
Berland, M., Charniak, E.: Finding parts in very large corpora. In: ACL (1999)
Pirrò, G., Seco, N.: Design, implementation and evaluation of a new semantic similarity metric combining features and intrinsic information content. In: ODBASE (2008)
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Rohrbach, M., Stark, M., Szarvas, G., Schiele, B. (2012). Combining Language Sources and Robust Semantic Relatedness for Attribute-Based Knowledge Transfer. In: Kutulakos, K.N. (eds) Trends and Topics in Computer Vision. ECCV 2010. Lecture Notes in Computer Science, vol 6553. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35749-7_2
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DOI: https://doi.org/10.1007/978-3-642-35749-7_2
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