publication . Part of book or chapter of book . Preprint . 2018

Localization-Aware Active Learning for Object Detection

Chieh-Chi Kao; Teng-Yok Lee; Pradeep Sen; Ming-Yu Liu;
Open Access
  • Published: 16 Jan 2018
  • Publisher: Springer International Publishing
Abstract
Active learning - a class of algorithms that iteratively searches for the most informative samples to include in a training dataset - has been shown to be effective at annotating data for image classification. However, the use of active learning for object detection is still largely unexplored as determining informativeness of an object-location hypothesis is more difficult. In this paper, we address this issue and present two metrics for measuring the informativeness of an object hypothesis, which allow us to leverage active learning to reduce the amount of annotated data needed to achieve a target object detection performance. Our first metric measures 'locali...
Persistent Identifiers
Subjects
free text keywords: Computer Science - Computer Vision and Pattern Recognition, Contextual image classification, Active learning, Training set, Region proposal, Computer science, Artificial intelligence, business.industry, business, Pattern recognition, Object detection
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http://arxiv.org/pdf/1801.0512...
Part of book or chapter of book
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Part of book or chapter of book . 2019
Provider: Crossref
32 references, page 1 of 3

[1] A. Bietti. Active learning for object detection on satellite images. Technical report, California Institute of Technology, Jan 2012. 2, 3

[2] J. Dai, K. He, and J. Sun. Instance-aware semantic segmentation via multi-task network cascades. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016. 5, 6, 7

[3] S. Dutt Jain and K. Grauman. Active image segmentation propagation. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016. 1

[4] M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman. The PASCAL Visual Object Classes (VOC) challenge. International Journal of Computer Vision (IJCV), 88(2):303-338, 2010. 3, 5, 6, 15 [OpenAIRE]

[5] A. Freytag, E. Rodner, and J. Denzler. Selecting influential examples: Active learning with expected model output changes. In European Conference on Computer Vision (ECCV). Springer, 2014. 1 [OpenAIRE]

[6] R. Girshick. Fast R-CNN. In International Conference on Computer Vision (ICCV), 2015. 2

[7] I. J. Goodfellow, J. Shlens, and C. Szegedy. Explaining and harnessing adversarial examples. In ICLR, 2015. 12

[8] M. Hasan and A. K. Roy-Chowdhury. Continuous learning of human activity models using deep nets. In European Conference on Computer Vision (ECCV). Springer, 2014. 1

[9] M. Hasan and A. K. Roy-Chowdhury. Context aware active learning of activity recognition models. In International Conference on Computer Vision (ICCV), 2015. 1

[10] J. Hoffman, S. Guadarrama, E. Tzeng, R. Hu, J. Donahue, R. Girshick, T. Darrell, and K. Saenko. LSDA: Large scale detection through adaptation. In Advances in Neural Information Processing Systems (NIPS), 2014. 2

[11] R. Islam. Active learning for high dimensional inputs using bayesian convolutional neural networks. Master's thesis, Department of Engineering, University of Cambridge, 8 2016. 2

[12] A. Kapoor, K. Grauman, R. Urtasun, and T. Darrell. Active learning with gaussian processes for object categorization. In International Conference on Computer Vision (ICCV). IEEE, 2007. 1 [OpenAIRE]

[13] A. Kapoor, K. Grauman, R. Urtasun, and T. Darrell. Gaussian processes for object categorization. International Journal of Computer Vision (IJCV), 88(2):169-188, 2010. 2 [OpenAIRE]

[14] V. Karasev, A. Ravichandran, and S. Soatto. Active frame, location, and detector selection for automated and manual video annotation. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2014. 2

[15] K. Konyushkova, R. Sznitman, and P. Fua. Introducing geometry in active learning for image segmentation. In The IEEE International Conference on Computer Vision (ICCV), December 2015. 1 [OpenAIRE]

32 references, page 1 of 3
Abstract
Active learning - a class of algorithms that iteratively searches for the most informative samples to include in a training dataset - has been shown to be effective at annotating data for image classification. However, the use of active learning for object detection is still largely unexplored as determining informativeness of an object-location hypothesis is more difficult. In this paper, we address this issue and present two metrics for measuring the informativeness of an object hypothesis, which allow us to leverage active learning to reduce the amount of annotated data needed to achieve a target object detection performance. Our first metric measures 'locali...
Persistent Identifiers
Subjects
free text keywords: Computer Science - Computer Vision and Pattern Recognition, Contextual image classification, Active learning, Training set, Region proposal, Computer science, Artificial intelligence, business.industry, business, Pattern recognition, Object detection
Download fromView all 2 versions
http://arxiv.org/pdf/1801.0512...
Part of book or chapter of book
Provider: UnpayWall
http://link.springer.com/conte...
Part of book or chapter of book . 2019
Provider: Crossref
32 references, page 1 of 3

[1] A. Bietti. Active learning for object detection on satellite images. Technical report, California Institute of Technology, Jan 2012. 2, 3

[2] J. Dai, K. He, and J. Sun. Instance-aware semantic segmentation via multi-task network cascades. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016. 5, 6, 7

[3] S. Dutt Jain and K. Grauman. Active image segmentation propagation. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016. 1

[4] M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman. The PASCAL Visual Object Classes (VOC) challenge. International Journal of Computer Vision (IJCV), 88(2):303-338, 2010. 3, 5, 6, 15 [OpenAIRE]

[5] A. Freytag, E. Rodner, and J. Denzler. Selecting influential examples: Active learning with expected model output changes. In European Conference on Computer Vision (ECCV). Springer, 2014. 1 [OpenAIRE]

[6] R. Girshick. Fast R-CNN. In International Conference on Computer Vision (ICCV), 2015. 2

[7] I. J. Goodfellow, J. Shlens, and C. Szegedy. Explaining and harnessing adversarial examples. In ICLR, 2015. 12

[8] M. Hasan and A. K. Roy-Chowdhury. Continuous learning of human activity models using deep nets. In European Conference on Computer Vision (ECCV). Springer, 2014. 1

[9] M. Hasan and A. K. Roy-Chowdhury. Context aware active learning of activity recognition models. In International Conference on Computer Vision (ICCV), 2015. 1

[10] J. Hoffman, S. Guadarrama, E. Tzeng, R. Hu, J. Donahue, R. Girshick, T. Darrell, and K. Saenko. LSDA: Large scale detection through adaptation. In Advances in Neural Information Processing Systems (NIPS), 2014. 2

[11] R. Islam. Active learning for high dimensional inputs using bayesian convolutional neural networks. Master's thesis, Department of Engineering, University of Cambridge, 8 2016. 2

[12] A. Kapoor, K. Grauman, R. Urtasun, and T. Darrell. Active learning with gaussian processes for object categorization. In International Conference on Computer Vision (ICCV). IEEE, 2007. 1 [OpenAIRE]

[13] A. Kapoor, K. Grauman, R. Urtasun, and T. Darrell. Gaussian processes for object categorization. International Journal of Computer Vision (IJCV), 88(2):169-188, 2010. 2 [OpenAIRE]

[14] V. Karasev, A. Ravichandran, and S. Soatto. Active frame, location, and detector selection for automated and manual video annotation. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2014. 2

[15] K. Konyushkova, R. Sznitman, and P. Fua. Introducing geometry in active learning for image segmentation. In The IEEE International Conference on Computer Vision (ICCV), December 2015. 1 [OpenAIRE]

32 references, page 1 of 3
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