Learning under Distributed Weak Supervision

B. C. Russell, A. Torralba, K. P. Murphy, and W. T. Freeman, “Labelme: a database and web-based tool for image annotation,” International journal of computer vision , vol. 77, no. 1-3, pp. 157–173, 2008

2008

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M. T. McKenna, S. Wang, T. B. Nguyen, J. E. Burns, N. Petrick, and R. M. Summers, “Strategies for improved interpretation of computer-aided detections for CT colonography utilizing distributed human intelligence,” Medical image analysis , vol. 16, no. 6, pp. 1280–1292, 2012

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R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Susstrunk, “SLIC superpixels compared to state-of-the-art superpixel methods,” Pattern Analysis and Machine Intelligence, IEEE Transactions on , vol. 34, no. 11, pp. 2274–2282, 2012

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Y. Taleb, M. Schweitzer, C. Studholme, M. Koob, J.-L. Dietemann, and F. Rousseau, “Automatic template-based brain extraction in fetal mr images,” 2013

2013

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M. Rajchl, J. Yuan, J. White, E. Ukwatta, J. Stirrat, C. Nambakhsh, F. Li, and T. Peters, “Interactive hierarchical max-flow segmentation of scar tissue from late-enhancement cardiac mr images,” IEEE Transactions on Medical Imaging , vol. 33, no. 1, pp. 159–172, 2014

2014

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T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick, “Microsoft coco: Common objects in context,” in Computer Vision–ECCV 2014 . Springer, 2014, pp. 740–755

2014

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L. Maier-Hein, S. Mersmann, D. Kondermann, S. Bodenstedt, A. Sanchez, C. Stock, H. G. Kenngott, M. Eisenmann, and S. Speidel, “Can Masses of Non-Experts Train Highly Accurate Image Classifiers?” in Medical Image Computing and Computer-Assisted Intervention–MICCAI 2014 , 2014, pp. 438–445

2014

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D. Haehn, J. Beyer, H. Pfister, S. Knowles-Barley, N. Kasthuri, J. Lichtman, and M. Roberts, “Design and evaluation of interactive proofreading tools for connectomics,” Computer Graphics, IEEE Transactions on , 2014

2014

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N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: A simple way to prevent neural networks from overfitting,” The Journal of Machine Learning Research , vol. 15, no. 1, pp. 1929–1958, 2014

2014

Cited alongside, same era.

Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell, “Caffe: Convolutional architecture for fast feature embedding,” in Proceedings of the ACM International Conference on Multimedia . ACM, 2014, pp. 675–678

T. Schlegl, S. M. Waldstein, W.-D. Vogl, U. Schmidt-Erfurth, and G. Langs, “Predicting semantic descriptions from medical images with convolutional neural networks,” in Information Processing in Medical Imaging . Springer, 2015, pp. 437–448

2015

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G. Papandreou, L.-C. Chen, K. Murphy, and A. L. Yuille, “Weakly-and semi-supervised learning of a DCNN for semantic image segmentation,” arXiv preprint arXiv:1502.02734 , 2015

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2015

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J. Dai, K. He, and J. Sun, “Boxsup: Exploiting bounding boxes to supervise convolutional networks for semantic segmentation,” in Proceedings of the IEEE International Conference on Computer Vision , 2015, pp. 1635–1643

2015

Later among the works it cites.

J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2015, pp. 3431–3440

2015

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