Sharing Features Between Objects and Their Attributes

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 optimizing 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 o...

• Type: Journal
• Year: 2011
• Where: CVPR
• Authors: Sung Ju Hwang, Fei Sha, Kristen Grauman
• Keywords: Computer Vision, Convex Multi-task Feature, CVPR 2011, Object Classifiers, Traditional Multi-class Object