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Context-Aware Zero-Shot Learning for Object Recognition
Eloi Zablocki · Patrick Bordes · Laure Soulier · Benjamin Piwowarski · Patrick Gallinari

Wed Jun 12 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #131

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.

Author Information

Eloi Zablocki (Sorbonne Université, LIP6)
Patrick Bordes (Laboratoire d'Informatique de PARIS VI)
Laure Soulier (Sorbonne Université)
Benjamin Piwowarski (Sorbonne Université)
Patrick Gallinari (LIP6, Sorbonne Universite)

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