Learning Detection with Diverse Proposals

@article{Azadi2017LearningDW,
  title={Learning Detection with Diverse Proposals},
  author={Samaneh Azadi and Jiashi Feng and Trevor Darrell},
  journal={2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2017},
  pages={7369-7377}
}
To predict a set of diverse and informative proposals with enriched representations, this paper introduces a differentiable Determinantal Point Process (DPP) layer that is able to augment the object detection architectures. Most modern object detection architectures, such as Faster R-CNN, learn to localize objects by minimizing deviations from the ground truth, but ignore correlation between multiple proposals and object categories. Non-Maximum Suppression (NMS) as a widely used proposal… CONTINUE READING
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