Learning to share visual appearance for multiclass object detection

Authors:
R. Salakhutdinov;A. Torralba;J. Tenenbaum
Affiliations:
Massachusetts Inst. of Technol., Cambridge, MA, USA;Massachusetts Inst. of Technol., Cambridge, MA, USA;Massachusetts Inst. of Technol., Cambridge, MA, USA
Venue:
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Year:
2011

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Abstract

We present a hierarchical classification model that allows rare objects to borrow statistical strength from related objects that have many training examples. Unlike many of the existing object detection and recognition systems that treat different classes as unrelated entities, our model learns both a hierarchy for sharing visual appearance across 200 object categories and hierarchical parameters. Our experimental results on the challenging object localization and detection task demonstrate that the proposed model substantially improves the accuracy of the standard single object detectors that ignore hierarchical structure altogether.