Handling label noise in video classification via multiple instance learning

@article{Leung2011HandlingLN,
  title={Handling label noise in video classification via multiple instance learning},
  author={Thomas Leung and Yang Song and John Zhang},
  journal={2011 International Conference on Computer Vision},
  year={2011},
  pages={2056-2063}
}
In many classification tasks, the use of expert-labeled data for training is often prohibitively expensive. The use of weakly-labeled data is an attractive solution but raises the problem of label noise. Multiple instance learning, whereby training samples are “bagged” instead of treated as singletons, offers a possible approach to mitigating the effects of label noise. In this paper, we propose the use of MILBoost [28] in a large-scale video taxonomic classification system comprised of… CONTINUE READING

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