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ASGNet

The code is for the paper "Adaptive Prototype Learning and Allocation for Few-Shot Segmentation" (accepted to CVPR 2021) [arxiv]

Overview

  • data/ includes config files and train/validation list files
  • model/ includes related model and module
  • tool/ includes training and testing scripts
  • util/ includes data processing, seed initialization

Usage

Requirements

python==3.7, torch==1.6, scipy, opencv-python, tensorboardX

Dataset

Prepare related datasets: Pascal-5i (VOC 2012, SBD) and COCO-20i (COCO 2014)

Pre-trained models

  • Pre-trained backbones and models can be found in Google Driver
  • Download backbones and put the pth files under initmodel/ folder

Test and Train

  • Specify the path of datasets and pre-trained models in the data/config file
  • Use the following command
    sh tool/test.sh|train.sh {data} {model} {split_backbone}
    

E.g. Test ASGNet with ResNet50 on the split 0 of PASCAL-5i:

sh tool/test.sh pascal asgnet split0_resnet50

Citation

Please consider citing the paper if you find it useful:

@inproceedings{li2021AdaptivePL,
  title={Adaptive Prototype Learning and Allocation for Few-Shot Segmentation},
  author={Gen Li and Varun Jampani and Laura Sevilla-Lara and Deqing Sun and Jonghyun Kim and Joongkyu Kim},
  booktitle={CVPR},
  year={2021}
}

References

The code is based on semseg and PFENet. Thanks for their great work!

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Adaptive Prototype Learning and Allocation for Few-Shot Segmentation (CVPR 2021)

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