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• W1569394107 abstract "In this paper we propose a non-metric ranking-based representation of semantic similarity that allows natural aggregation of semantic information from multiple heterogeneous sources. We apply the ranking-based representation to zero-shot learning problems, and present deterministic and probabilistic zero-shot classifiers which can be built from pre-trained classifiers without retraining. We demonstrate their the advantages on two large real-world image datasets. In particular, we show that aggregating different sources of semantic information, including crowd-sourcing, leads to more accurate classification." @default.
• W1569394107 created "2016-06-24" @default.
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• W1569394107 date "2015-02-27" @default.
• W1569394107 modified "2023-09-27" @default.
• W1569394107 title "Probabilistic Zero-shot Classification with Semantic Rankings." @default.
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• W1569394107 hasPublicationYear "2015" @default.
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