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2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2014)
Columbus, OH, USA
June 23, 2014 to June 28, 2014
ISSN: 1063-6919
ISBN: 978-1-4799-5118-5
pp: 1464-1471
In this paper, we tackle the problem of co-localization in real-world images. Co-localization is the problem of simultaneously localizing (with bounding boxes) objects of the same class across a set of distinct images. Although similar problems such as co-segmentation and weakly supervised localization have been previously studied, we focus on being able to perform co-localization in real-world settings, which are typically characterized by large amounts of intra-class variation, inter-class diversity, and annotation noise. To address these issues, we present a joint image-box formulation for solving the co-localization problem, and show how it can be relaxed to a convex quadratic program which can be efficiently solved. We perform an extensive evaluation of our method compared to previous state-of-the-art approaches on the challenging PASCAL VOC 2007 and Object Discovery datasets. In addition, we also present a large-scale study of co-localization on ImageNet, involving ground-truth annotations for 3, 624 classes and approximately 1 million images.
Noise measurement, Vectors, Joints, Noise, Feature extraction, Airplanes, Object recognition

K. Tang, A. Joulin, L. Li and L. Fei-Fei, "Co-localization in Real-World Images," 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, OH, USA, 2014, pp. 1464-1471.
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