The code is for the paper "A Self-Distillation Embedded Supervised Affinity Attention Model for Few-Shot Segmentation" [arxiv]

• config/ includes config files
• lists/ includes train/validation list files
• model/ includes related model and module
• util/ includes data processing, seed initialization

python==3.7, torch==1.8, scipy, opencv-python, tensorboardX

Please prepare related datasets:

• Pascal-5ⁱ (VOC 2012, SBD)
• COCO-20ⁱ (COCO 2014)

Specify the paths of datasets in config files, including data root and list files paths

We provide 8 pre-trained models: 4 ResNet-50 based models for Pascal-5i and 4 ResNet-101 based models for COCO-20i

• Download the pre-trained models [Pre-trained models]
• Specify the split setting, shot setting and path of weights in config files

• Use the following command for testing

  sh test.sh {data} {split_backbone}
  

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

sh test.sh pascal split0_resnet50

• Use the following command for training

  sh train.sh {data} {split_backbone}
  

E.g. Train SD-AANet with ResNet50 on the split 0 of PASCAL-5i:

sh train.sh pascal split0_resnet50

If you have any question, please discuss with me by sending email to liubinghao@buaa.edu.cn

Please consider citing the paper if you find it useful:

@ARTICLE{10057432,
  author={Zhao, Qi and Liu, Binghao and Lyu, Shuchang and Chen, Huojin},
  journal={IEEE Transactions on Cognitive and Developmental Systems}, 
  title={A Self-Distillation Embedded Supervised Affinity Attention Model for Few-Shot Segmentation}, 
  year={2023},
  pages={1-1},
  doi={10.1109/TCDS.2023.3251371}}

The code is based on PFENet and kd-pytorch. Thanks for their great works!