Learning from ambiguously labeled images

@article{Cour2009LearningFA,
  title={Learning from ambiguously labeled images},
  author={Timoth{\'e}e Cour and Benjamin Sapp and Christopher T. Jordan and Ben Taskar},
  journal={2009 IEEE Conference on Computer Vision and Pattern Recognition},
  year={2009},
  pages={919-926}
}
In many image and video collections, we have access only to partially labeled data. For example, personal photo collections often contain several faces per image and a caption that only specifies who is in the picture, but not which name matches which face. Similarly, movie screenplays can tell us who is in the scene, but not when and where they are on the screen. We formulate the learning problem in this setting as partially-supervised multiclass classification where each instance is labeled… CONTINUE READING
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