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- W2608362693 abstract "We consider the problem of object figure-ground segmentation when the object categories are not available during training (i.e. zero-shot). During training, we learn standard segmentation models for a handful of object categories (called “source objects”) using existing semantic segmentation datasets. During testing, we are given images of objects (called “target objects”) that are unseen during training. Our goal is to segment the target objects from the background. Our method learns to transfer the knowledge from the source objects to the target objects. Our experimental results demonstrate the effectiveness of our approach." @default.
- W2608362693 created "2017-05-05" @default.
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- W2608362693 date "2016-12-01" @default.
- W2608362693 modified "2023-09-23" @default.
- W2608362693 title "Object figure-ground segmentation using zero-shot learning" @default.
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- W2608362693 doi "https://doi.org/10.1109/icpr.2016.7900067" @default.
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