Localization-Aware Active Learning for Object Detection

Preprint English OPEN
Kao, Chieh-Chi; Lee, Teng-Yok; Sen, Pradeep; Liu, Ming-Yu;
(2018)
  • Subject: Computer Science - Computer Vision and Pattern Recognition

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 ... View more
  • References (32)
    32 references, page 1 of 4

    [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

    [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

    [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

  • Metrics
Share - Bookmark