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• W2950762068 abstract "Zero-Shot Learning (ZSL) aims at classifying unlabeled objects by leveraging auxiliary knowledge, such as semantic representations. A limitation of previous approaches is that only intrinsic properties of objects, e.g. their visual appearance, are taken into account while their context, e.g. the surrounding objects in the image, is ignored. Following the intuitive principle that objects tend to be found in certain contexts but not others, we propose a new and challenging approach, context-aware ZSL, that leverages semantic representations in a new way to model the conditional likelihood of an object to appear in a given context. Finally, through extensive experiments conducted on Visual Genome, we show that contextual information can substantially improve the standard ZSL approach and is robust to unbalanced classes." @default.
• W2950762068 created "2019-06-27" @default.
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• W2950762068 date "2019-04-24" @default.
• W2950762068 modified "2023-09-25" @default.
• W2950762068 title "Context-Aware Zero-Shot Learning for Object Recognition" @default.
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• W2950762068 hasPublicationYear "2019" @default.
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