Learning Semantic Segmentation with Diverse Supervision

Preprint English OPEN
Ye, Linwei ; Liu, Zhi ; Wang, Yang (2018)
  • Subject: Computer Science - Computer Vision and Pattern Recognition
    acm: ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION

Models based on deep convolutional neural networks (CNN) have significantly improved the performance of semantic segmentation. However, learning these models requires a large amount of training images with pixel-level labels, which are very costly and time-consuming to ... View more
  • References (28)
    28 references, page 1 of 3

    [1] V. Badrinarayanan, A. Kendall, and R. Cipolla. Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017.

    [2] L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille. Semantic image segmentation with deep convolutional nets and fully connected crfs. In International Conference on Learning Representations, 2015.

    [3] L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille. Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. arXiv:1606.00915, 2016.

    [4] J. Dai, K. He, and J. Sun. Boxsup: Exploiting bounding boxes to supervise convolutional networks for semantic segmentation. In IEEE International Conference on Computer Vision, 2015.

    [5] J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. FeiFei. Imagenet: A large-scale hierarchical image database. In IEEE Conference on Computer Vision and Pattern Recognition, 2009.

    [6] M. Everingham, L. Van Gool, C. K. Williams, J. Winn, and A. Zisserman. The pascal visual object classes (voc) challenge. International Journal of Computer Vision, 2010.

    [7] R. Girshick, J. Donahue, T. Darrell, and J. Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. In IEEE Conference on Computer Vision and Pattern Recognition, 2014.

    [8] M. Guillaumin, D. Ku¨ttel, and V. Ferrari. Imagenet autoannotation with segmentation propagation. International Journal of Computer Vision, 2014.

    [9] B. Hariharan, P. Arbela´ez, L. Bourdev, S. Maji, and J. Malik. Semantic contours from inverse detectors. In International Conference on Computer Vision, 2011.

    [10] A. Khoreva, R. Benenson, J. Hosang, M. Hein, and B. Schiele. Simple does it: Weakly supervised instance and semantic segmentation. In IEEE Conference on Computer Vision and Pattern Recognition, 2017.

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