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Sharing features between objects and their attributes
2011
CVPR 2011
Visual attributes expose human-defined semantics to object recognition models, but existing work largely restricts their influence to mid-level cues during classifier training. Rather than treat attributes as intermediate features, we consider how learning visual properties in concert with object categories can regularize the models for both. Given a low-level visual feature space together with attributeand object-labeled image data, we learn a shared lowerdimensional representation by
doi:10.1109/cvpr.2011.5995543
dblp:conf/cvpr/HwangSG11
fatcat:ui25bdqyt5hv5iszpin233zn5m