Sharing features between objects and their attributes

Sung Ju Hwang, Fei Sha, Kristen Grauman
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
more » ... g a joint loss function that favors common sparsity patterns across both types of prediction tasks. We adopt a recent kernelized formulation of convex multi-task feature learning, in which one alternates between learning the common features and learning task-specific classifier parameters on top of those features. In this way, our approach discovers any structure among the image descriptors that is relevant to both tasks, and allows the top-down semantics to restrict the hypothesis space of the ultimate object classifiers. We validate the approach on datasets of animals and outdoor scenes, and show significant improvements over traditional multi-class object classifiers and direct attribute prediction models.
doi:10.1109/cvpr.2011.5995543 dblp:conf/cvpr/HwangSG11 fatcat:ui25bdqyt5hv5iszpin233zn5m