R-FCN-3000 at 30fps: Decoupling Detection and Classification

@article{Singh2018RFCN3000A3,
  title={R-FCN-3000 at 30fps: Decoupling Detection and Classification},
  author={B. Singh and Hengduo Li and Abhishek Sharma and L. Davis},
  journal={2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year={2018},
  pages={1081-1090}
}
  • B. Singh, Hengduo Li, +1 author L. Davis
  • Published 2018
  • Computer Science
  • 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
We propose a modular approach towards large-scale real-time object detection by decoupling objectness detection and classification. We exploit the fact that many object classes are visually similar and share parts. Thus, a universal objectness detector can be learned for class-agnostic object detection followed by fine-grained classification using a (non)linear classifier. Our approach is a modification of the R-FCN architecture to learn shared filters for performing localization across… Expand
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