DeNet: Scalable Real-Time Object Detection with Directed Sparse Sampling

@article{TychsenSmith2017DeNetSR,
  title={DeNet: Scalable Real-Time Object Detection with Directed Sparse Sampling},
  author={Lachlan Tychsen-Smith and L. Petersson},
  journal={2017 IEEE International Conference on Computer Vision (ICCV)},
  year={2017},
  pages={428-436}
}
We define the object detection from imagery problem as estimating a very large but extremely sparse bounding box dependent probability distribution. Subsequently we identify a sparse distribution estimation scheme, Directed Sparse Sampling, and employ it in a single end-to-end CNN based detection model. This methodology extends and formalizes previous state-of-the-art detection models with an additional emphasis on high evaluation rates and reduced manual engineering. We introduce two novelties… Expand
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