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

• 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

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

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

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

• 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

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}
}

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