• Corpus ID: 195346999

Transferring Common-Sense Knowledge for Object Detection

@article{Singh2018TransferringCK,
  title={Transferring Common-Sense Knowledge for Object Detection},
  author={Krishna Kumar Singh and Santosh Kumar Divvala and Ali Farhadi and Yong Jae Lee},
  journal={ArXiv},
  year={2018},
  volume={abs/1804.01077}
}
We propose the idea of transferring common-sense knowledge from source categories to target categories for scalable object detection. [] Key Method We acquire such common-sense cues automatically from readily-available knowledge bases without any extra human effort. On the challenging MS COCO dataset, we find that using common-sense knowledge substantially improves detection performance over existing transfer-learning baselines.

Analysing object detectors from the perspective of co-occurring object categories

  • Csaba NemesSándor Jordán
  • Computer Science
    2018 9th IEEE International Conference on Cognitive Infocommunications (CogInfoCom)
  • 2018
According to measurements, current detectors usually do not build strong dependency on contextual information at category level, however, when they does, they does it in a similar way, suggesting that contextual dependence of object categories is an independent property that is relevant to be transferred.

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