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- W2120995095 abstract "Many approaches to object recognition are founded on probability theory, and can be broadly characterized as either generative or discriminative according to whether or not the distribution of the image features is modelled. Generative and discriminative methods have very different characteristics, as well as complementary strengths and weaknesses. In this paper we introduce new generative and discriminative models for object detection and classification based on weakly labelled training data. We use these models to illustrate the relative merits of the two approaches in the context of a data set of widely varying images of non-rigid objects (animals). Our results support the assertion that neither approach alone will be sufficient for large scale object recognition, and we discuss techniques for combining them." @default.
- W2120995095 created "2016-06-24" @default.
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- W2120995095 date "2005-07-27" @default.
- W2120995095 modified "2023-10-12" @default.
- W2120995095 title "Generative versus Discriminative Methods for Object Recognition" @default.
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- W2120995095 doi "https://doi.org/10.1109/cvpr.2005.167" @default.
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