DeepID-Net: Deformable deep convolutional neural networks for object detection

@article{Ouyang2015DeepIDNetDD,
  title={DeepID-Net: Deformable deep convolutional neural networks for object detection},
  author={Wanli Ouyang and Xiaogang Wang and X. Zeng and Shi Qiu and Ping Luo and Yonglong Tian and Hongsheng Li and S. Yang and Zhe Wang and Chen Change Loy and X. Tang},
  journal={2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2015},
  pages={2403-2412}
}
  • Wanli Ouyang, Xiaogang Wang, +8 authors X. Tang
  • Published 2015
  • Computer Science, Medicine
  • 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
  • In this paper, we propose deformable deep convolutional neural networks for generic object detection. This new deep learning object detection framework has innovations in multiple aspects. In the proposed new deep architecture, a new deformation constrained pooling (def-pooling) layer models the deformation of object parts with geometric constraint and penalty. A new pre-training strategy is proposed to learn feature representations more suitable for the object detection task and with good… CONTINUE READING
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