• DOI:10.1109/CVPR.2016.320
Corpus ID: 3039286

Seeing through the Human Reporting Bias: Visual Classifiers from Noisy Human-Centric Labels

@article{Misra2016SeeingTT,
  title={Seeing through the Human Reporting Bias: Visual Classifiers from Noisy Human-Centric Labels},
  author={Ishan Misra and C. L. Zitnick and Margaret Mitchell and Ross B. Girshick},
  journal={2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2016},
  pages={2930-2939}
}

• Ishan Misra, C. L. Zitnick, +1 author Ross B. Girshick
• Published 2016
• Computer Science
• 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

When human annotators are given a choice about what to label in an image, they apply their own subjective judgments on what to ignore and what to mention. We refer to these noisy "human-centric" annotations as exhibiting human reporting bias. Examples of such annotations include image tags and keywords found on photo sharing sites, or in datasets containing image captions. In this paper, we use these noisy annotations for learning visually correct image classifiers. Such annotations do not use… Expand

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