Lightweight Super-Resolution Head for Human Pose Estimation arxiv

Lightweight Super-Resolution Head for Human Pose Estimation
Accepted by ACM MM 2023
Haonan Wang, Jie Liu, Jie Tang, Gangshan Wu

News!

• [2023.08.03] The pretrained models are released in Google Drive!
• [2023.07.30] The codes for SRPose are released!
• [2023.07.29] Our paper ''Lightweight Super-Resolution Head for Human Pose Estimation'' has been accpeted by ACM MM 2023. If you find this repository useful please give it a star 🌟.

Introduction

This is the official implementation of Lightweight Super-Resolution Head for Human Pose Estimation. We present a Lightweight Super-Resolution Head , which predicts heatmaps with a spatial resolution higher than the input feature maps (or even consistent with the input image) by super-resolution, to effectively reduce the quantization error and the dependence on further post-processing. Besides, we propose SRPose to gradually recover the HR heatmaps from LR heatmaps and degraded features in a coarse-to-fine manner. To reduce the training difficulty of HR heatmaps, SRPose applies SR heads to supervise the intermediate features in each stage. In addition, the SR head is a lightweight and generic head that applies to top-down and bottom-up methods.

Experiments

Results on COCO validation set

Backbone	Scheme	GFLOPs	Params		w/ Post.		w/o Post.
			Backbone	Other	AP	AR	AP	AR
Top-down methods
Resnet-50	Simple head	5.46	23.51M	10.49M	71.7	77.3	69.8	75.8
	SR head (ours)	5.77	23.51M	10.59M	72.4	77.9	72.2	77.7
	SRPose (ours)	4.61	23.51M	1.29M	73.3	78.8	73.1	78.6
HRNet-W32	Simple head	7.70	28.54M	0.00M	74.5	79.9	72.3	78.2
	SR head (ours)	7.98	28.54M	0.09M	75.6	80.6	75.4	80.5
	SRPose (ours)	8.28	29.30M	0.65M	75.9	81.0	75.7	80.9
TransPose-R-A4	Simple head	8.91	4.93M	1.06M	71.8	77.3	69.7	75.5
	SR head (ours)	9.23	4.93M	1.16M	73.2	78.4	73.1	78.3
	SRPose (ours)	6.26	4.93M	0.55M	73.5	78.9	73.4	78.7
HRFormer-S	Simple head	2.82	7.89M	0.00M	74.0	79.2	72.1	77.6
	SR head (ours)	3.09	7.89M	0.09M	75.0	80.1	74.8	80.0
	SRPose (ours)	3.34	8.21M	0.65M	75.6	80.7	75.5	80.6
Bottpm-up methods
Resnet-50	Simple head	29.20	23.51M	10.49M	46.7	55.1	-	-
	SR head (ours)	30.86	23.51M	10.60M	48.4	56.6	-	-
HRNet-W32	Simple head	41.10	28.54M	0.00M	65.3	70.9	-	-
	SR head (ours)	42.57	28.54M	0.09M	67.1	71.7	-	-

Note:

• The resolution of input is 256x192 for top-down methods, 512x512 for bottom-up methods.
• Flip test is used.
• Person detector has person AP of 56.4 on COCO val2017 dataset for top-down methods.
• Post. = extra post-processing (empirical shift) towards refining the predicted keypoint coordinate.

Results on MPII val set

Method	Backbone	PCKh@0.5
SimBa	Resnet-50	88.2
HRNet	HRNet-W32	90.1
SimCC	HRNet-W32	90.0
SRPose (ours)	Resnet-50	89.1
SRPose (ours)	HRNet-W32	90.5

Note:

• Flip test is used.

Results on CrowdPose

Method	Backbone	AP	AP_E	AP_M	AP_H
SimBa	Resnet-50	63.7	73.9	65.0	50.6
HRNet	HRNet-W32	66.4	74.0	67.4	55.6
SimCC	HRNet-W32	66.7	74.1	67.8	56.2
SRPose (ours)	Resnet-50	64.7	74.9	65.8	52.3
SRPose (ours)	HRNet-W32	67.8	77.5	69.1	55.6

Note:

• Flip test is used.

Start to use

1. Dependencies installation & data preparation

Please refer to THIS to prepare the environment step by step.

2. Model Zoo

Pretrained models are provided in our model zoo.

3. Trainging

# for single machine
bash tools/dist_train.sh <Config PATH> <NUM GPUs> --cfg-options model.pretrained=<Pretrained PATH> --seed 0

# for multiple machines
python -m torch.distributed.launch --nnodes <Num Machines> --node_rank <Rank of Machine> --nproc_per_node <GPUs Per Machine> --master_addr <Master Addr> --master_port <Master Port> tools/train.py <Config PATH> --cfg-options model.pretrained=<Pretrained PATH> --launcher pytorch --seed 0

4. Testing

To test the pretrained models performance, please run

bash tools/dist_test.sh <Config PATH> <Checkpoint PATH> <NUM GPUs>

Acknowledgement

We acknowledge the excellent implementation from mmpose, HRNet and HRFormer.

Citations

If you use our code or models in your research, please cite with:

@article{wang2023lightweight,
  title={Lightweight Super-Resolution Head for Human Pose Estimation},
  author={Wang, Haonan and Liu, Jie and Tang, Jie and Wu, Gangshan},
  journal={arXiv preprint arXiv:2307.16765},
  year={2023}
}