Few-Shot Object Detection via Feature Reweighting

@article{Kang2019FewShotOD,
  title={Few-Shot Object Detection via Feature Reweighting},
  author={Bingyi Kang and Z. Liu and Xin Wang and F. Yu and Jiashi Feng and Trevor Darrell},
  journal={2019 IEEE/CVF International Conference on Computer Vision (ICCV)},
  year={2019},
  pages={8419-8428}
}
  • Bingyi Kang, Z. Liu, +3 authors Trevor Darrell
  • Published 2019
  • Computer Science
  • 2019 IEEE/CVF International Conference on Computer Vision (ICCV)
  • This work aims to solve the challenging few-shot object detection problem where only a few annotated examples are available for each object category to train a detection model. [...] Key Method The base detection model is trained on several base classes with sufficient samples to offer basis features. The meta-model is trained to reweight importance of features from the base detection model over the input image and adapt these features to assist novel object detection from a few examples.Expand Abstract
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    Low-shot Object Detection via Classification Refinement
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    Incremental Few-Shot Object Detection
    2
    Frustratingly Simple Few-Shot Object Detection
    4

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