High-Resolution Representation Learning for Object Detection

Ke Sun     Yang Zhao     Borui Jiang     Tianheng Cheng     Bin Xiao     Dong Liu     Yadong Mu     Xinggang Wang     Wenyu Liu     Jingdong Wang

Abstract

We extend the high-resolution representation (HRNet) [1] by augmenting the high-resolution representation by aggregating the (upsampled) representations from all the parallel convolutions, leading to stronger representations. We build a multi-level representation from the high resolution and apply it to the Faster R-CNN, Mask R-CNN and Cascade R-CNN framework. This proposed approach achieves superior results to existing single-model networks on COCO object detection.
The code and models are publicly available at GitHub.

Architecture

Paper

paper

Code

We released the training and testing code and the pretrained model at GitHub

Other applications

more ...
Pose estimation Semantic segmentation Face alignment Image classification Object detection

Citation

@inproceedings{SunXLW19,
  title={Deep High-Resolution Representation Learning for Human Pose Estimation},
  author={Ke Sun and Bin Xiao and Dong Liu and Jingdong Wang},
  booktitle={CVPR},
  year={2019}
}
@article{SunZJCXLMWLW19,
  title={High-Resolution Representations for Labeling Pixels and Regions},
  author={Ke Sun and Yang Zhao and Borui Jiang and Tianheng Cheng and Bin Xiao
  and Dong Liu and Yadong Mu and Xinggang Wang and Wenyu Liu and Jingdong Wang},
  journal   = {CoRR},
  volume    = {abs/1904.04514},
  year={2019}
}

References

[1]  Deep High-Resolution Representation Learning for Human Pose Estimation. Ke Sun, Bin Xiao, Dong Liu, and Jingdong Wang. CVPR 2019.

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