Much Ado About Time: Exhaustive Annotation of Temporal Data

@inproceedings{Sigurdsson2016MuchAA,
  title={Much Ado About Time: Exhaustive Annotation of Temporal Data},
  author={Gunnar A. Sigurdsson and Olga Russakovsky and Ali Farhadi and Ivan Laptev and Abhinav Gupta},
  booktitle={HCOMP},
  year={2016}
}
Large-scale annotated datasets allow AI systems to learn from and build upon the knowledge of the crowd. Many crowdsourcing techniques have been developed for collecting image annotations. These techniques often implicitly rely on the fact that a new input image takes a negligible amount of time to perceive. In contrast, we investigate and determine the most cost-effective way of obtaining high-quality multilabel annotations for temporal data such as videos. Watching even a short 30-second… CONTINUE READING
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Key Quantitative Results

  • When compared with a one-question-per-video baseline, our method is able to achieve a 10% improvement in recall (76.7% ours versus 66.7% baseline) at comparable precision (83.8% ours versus 83.0% baseline) in about half the annotation time (3.8 minutes ours compared to 7.1 minutes baseline).
  • When compared with a one-question-at-a-time baseline, our method achieves a 10% improvement in recall (76.7% ours versus 66.7% baseline) at comparable precision (83.8% ours versus 83.0% baseline) in about half the annotation time (3.8 minutes ours compared to 7.1 minutes baseline).

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