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Cited by 2,867

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

A discriminatively trained, multiscale, deformable part model

Felzenszwalb, Pedro, McAllester, David, Ramanan, Deva·2008·2008 IEEE Conference on Computer Vision and Pattern Recognition
DOI 10.1109/cvpr.2008.4587597
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Abstract

This paper describes a discriminatively trained, multiscale, deformable part model for object detection. Our system achieves a two-fold improvement in average precision over the best performance in the 2006 PASCAL person detection challenge. It also outperforms the best results in the 2007 challenge in ten out of twenty categories. The system relies heavily on deformable parts. While deformable part models have become quite popular, their value had not been demonstrated on difficult benchmarks such as the PASCAL challenge. Our system also relies heavily on new methods for discriminative training. We combine a margin-sensitive approach for data mining hard negative examples with a formalism we call latent SVM. A latent SVM, like a hidden CRF, leads to a non-convex training problem. However, a latent SVM is semi-convex and the training problem becomes convex once latent information is specified for the positive examples. We believe that our training methods will eventually make possible the effective use of more latent information such as hierarchical (grammar) models and models involving latent three dimensional pose.

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Research works printed but not linked: 19

  1. 10.1109/TPAMI.2007.1124 · DOI · Paper page
  2. 10.1023/B:VISI.0000011202.85607.00 · DOI · Paper page
  3. joachims, 1999, making large-scale svm learning practical, Advances in Kernel Methods - Support Vector Learning
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  5. lecun, 2006, a tutorial on energy-based learning, Predicting Structured Data
  6. ioffe, 2001, probabilistic methods for finding people, IJCV, 43, 45, 10.1023/A:1011179004708 · DOI
  7. jin, 2006, context and hierarchy in a probabilistic image model, CVPR pages II, 2145
  8. 10.1109/CVPR.2005.25 · DOI
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  15. 10.1007/s11263-006-0033-9 · DOI
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  17. 10.1109/CVPR.2007.383086 · DOI
  18. crandall, 2006, weakly supervised learning of part-based spatial models for visual object recognition, ECCV pages I, 16
  19. felzenszwalb, 0, distance transforms of sampled functions, Cornell Computing and Information Science Technical Report

Cited by

Metadata

Authors
Felzenszwalb, P., McAllester, D., & Ramanan, D.
Venue
2008 IEEE Conference on Computer Vision and Pattern Recognition
Publisher
IEEE
Published
2008-06
Pages
1-8
ISSN
1063-6919 (ISSN-L)
DOI
10.1109/cvpr.2008.4587597
OpenAlex
W2120419212
Cited by
2,867
References
2
FWCI
108.45
Type
conference paper
Language
en
Open access
closed

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