This is an official implementation of "Polarized Self-Attention: Towards High-quality Pixel-wise Regression"

Related tags

Deep Learning PSA
Overview

Polarized Self-Attention: Towards High-quality Pixel-wise Regression

This is an official implementation of:

Huajun Liu, Fuqiang Liu, Xinyi Fan and Dong Huang. Polarized Self-Attention: Towards High-quality Pixel-wise Regression Arxiv Version

PWC PWC PWC

Citation:

@article{Liu2021PSA,
  title={Polarized Self-Attention: Towards High-quality Pixel-wise Regression},
  author={Huajun Liu and Fuqiang Liu and Xinyi Fan and Dong Huang},
  journal={Arxiv Pre-Print arXiv:2107.00782 },
  year={2021}
}

Codes and Pre-trained models will be uploaded soon~

Top-down 2D pose estimation models pre-trained on the MS-COCO keypoint task(Table4 in the Arxiv version).

Model Name Backbone Input Size AP pth file
UDP-Pose-PSA(p) HRNet-W48 256x192 78.9 to be uploaded
UDP-Pose-PSA(p) HRNet-W48 384x288 79.5 to be uploaded
UDP-Pose-PSA(s) HRNet-W48 384x288 79.4 to be uploaded

Setup and inference:

Semantic segmentation models pre-trained on Cityscapes (Table5 in the Arxiv version).

Model Name Backbone val mIoU pth file
HRNetV2-OCR+PSA(p) HRNetV2-W48 86.95 download
HRNetV2-OCR+PSA(s) HRNetV2-W48 86.72 download

Setup and inference:

Comments
  • How to export the model as ONNX

    How to export the model as ONNX

    Hello,

    I'm interested in testing the HRNetV2-OCR+PSA(p) with OpenCV, but I believe there is no way to load a .pth file. I'm checking how to use pytorch to load the network and then export it to ONNX, but It seems that I need to know the model definition to properly load the network. Am I going in the right direction? Sorry if the question seems naive, I don't have a lot of experience with ML, especially with pytorch.

    Thanks in advance

    opened by LeonardoRez 0
  • PSA module missing in bottleneck block

    PSA module missing in bottleneck block

    The PSA module that is defined here is only used in the basic block of HRNet and not the bottleneck block. The paper says this though:

    For any baseline networks with the bottleneck or basic residual blocks, such as ResNet and HRnet, we add PSAs after the first 3x3 convolution in every residual blocks, respectively.

    opened by fferflo 1
  • I trained PSA on pose estimation, it doesn't have the same effect as the paper claims.

    I trained PSA on pose estimation, it doesn't have the same effect as the paper claims.

    I added the PSA_p module on BasicBlock as the segmentation code did here. However, it has no effect on the final results. The final results are even 0.1% less than directly using the HRNet v2. I used the HRNet pose estimation default training schedule.

    opened by ArchNew 2
  • How to use PSA in human pose estimation？

    How to use PSA in human pose estimation？

    It's my honor to see such an excellent job.

    I use hrnetv2.py in the network to train MPIIi dataset, but the effect has not changed. How do I use the PSA module?

    Look forward to your human pose estimation code.Thank you!

    opened by wangdong0556 0
  • It seems that the implementation of Channel only self attention and Spatial only self attention change each other.

    It seems that the implementation of Channel only self attention and Spatial only self attention change each other.

    Thanks for sharing your work!

    https://github.com/DeLightCMU/PSA/blob/588b370d9f240d38832061a70c275cb6eb81232e/semantic-segmentation/network/PSA.py#L64-L95

    image

    It seems that spatial_pool function is the same with Channel-only self attention module.

    opened by developer0hye 5
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