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Improving Convolutional Networks with Self-Calibrated Convolutions

Introduction

[ALGORITHM]

@inproceedings{liu2020improving,
  title={Improving Convolutional Networks with Self-Calibrated Convolutions},
  author={Liu, Jiang-Jiang and Hou, Qibin and Cheng, Ming-Ming and Wang, Changhu and Feng, Jiashi},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={10096--10105},
  year={2020}
}

Results and models

2d Human Pose Estimation

Results on COCO val2017 with detector having human AP of 56.4 on COCO val2017 dataset

Arch Input Size AP AP50 AP75 AR AR50 ckpt log
pose_scnet_50 256x192 0.728 0.899 0.807 0.784 0.938 ckpt log
pose_scnet_50 384x288 0.751 0.906 0.818 0.802 0.943 ckpt log
pose_scnet_101 256x192 0.733 0.903 0.813 0.790 0.941 ckpt log
pose_scnet_101 384x288 0.752 0.906 0.823 0.804 0.943 ckpt log

Results on MPII val set

Arch Input Size Mean [email protected] ckpt log
pose_scnet_50 256x256 0.888 0.290 ckpt log
pose_scnet_101 256x256 0.886 0.293 ckpt log