Efficient large-scale structured learning
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Efficient large-scale structured learning. / Branson, Steve; Beijbom, Oscar; Belongie, Serge.
In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2013, p. 1806-1813.Research output: Contribution to journal › Conference article › Research › peer-review
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TY - GEN
T1 - Efficient large-scale structured learning
AU - Branson, Steve
AU - Beijbom, Oscar
AU - Belongie, Serge
PY - 2013
Y1 - 2013
N2 - We introduce an algorithm, SVM-IS, for structured SVM learning that is computationally scalable to very large datasets and complex structural representations. We show that structured learning is at least as fast-and often much faster-than methods based on binary classification for problems such as deformable part models, object detection, and multiclass classification, while achieving accuracies that are at least as good. Our method allows problem-specific structural knowledge to be exploited for faster optimization by integrating with a user-defined importance sampling function. We demonstrate fast train times on two challenging large scale datasets for two very different problems: Image Net for multiclass classification and CUB-200-2011 for deformable part model training. Our method is shown to be 10-50 times faster than {SVM}{struct}for cost-sensitive multiclass classification while being about as fast as the fastest 1-vs-all methods for multiclass classification. For deformable part model training, it is shown to be 50-1000 times faster than methods based on SVM struct, mining hard negatives, and Pegasos-style stochastic gradient descent. Source code of our method is publicly available.
AB - We introduce an algorithm, SVM-IS, for structured SVM learning that is computationally scalable to very large datasets and complex structural representations. We show that structured learning is at least as fast-and often much faster-than methods based on binary classification for problems such as deformable part models, object detection, and multiclass classification, while achieving accuracies that are at least as good. Our method allows problem-specific structural knowledge to be exploited for faster optimization by integrating with a user-defined importance sampling function. We demonstrate fast train times on two challenging large scale datasets for two very different problems: Image Net for multiclass classification and CUB-200-2011 for deformable part model training. Our method is shown to be 10-50 times faster than {SVM}{struct}for cost-sensitive multiclass classification while being about as fast as the fastest 1-vs-all methods for multiclass classification. For deformable part model training, it is shown to be 50-1000 times faster than methods based on SVM struct, mining hard negatives, and Pegasos-style stochastic gradient descent. Source code of our method is publicly available.
KW - cost-sensitive SVM
KW - deformable part models
KW - object detection
KW - optimization
KW - structured learning
KW - sub-gradient
UR - http://www.scopus.com/inward/record.url?scp=84887346435&partnerID=8YFLogxK
U2 - 10.1109/CVPR.2013.236
DO - 10.1109/CVPR.2013.236
M3 - Conference article
AN - SCOPUS:84887346435
SP - 1806
EP - 1813
JO - I E E E Conference on Computer Vision and Pattern Recognition. Proceedings
JF - I E E E Conference on Computer Vision and Pattern Recognition. Proceedings
SN - 1063-6919
M1 - 6619080
T2 - 26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2013
Y2 - 23 June 2013 through 28 June 2013
ER -
ID: 293218609