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CAPL-FSSeg

This repo provides the implementation of CAPL in FS-Seg.

Get Started

Environment

• Python 3.7.9
• Torch 1.5.1
• cv2 4.4.0
• numpy 1.21.0
• CUDA 10.1

Datasets and Data Preparation

Please prepare the datasets (COCO-20i and PASCAL-5i) by following the instructions of PFENet.

Run Demo / Test with Pretrained Models

• Execute mkdir initmodel at the root directory.
• Download the ImageNet pretrained backbones and put them into the initmodel directory.
• Please download the pretrained models.
• We provide 16 pre-trained models: 8 for 1/5 shot results on PASCAL-5i and 8 for COCO.
• Update the config files by speficifying the target split, weights and val_shot for loading different checkpoints.

Train / Evaluate

• For training, please set the option only_evaluate to False in the configuration file. Then execute this command at the root directory:

  sh train.sh {dataset} {model_config}

• For evaluation only, please set the option only_evaluate to True in the corresponding configuration file.

Example: Train / Evaluate CAPL with 1-shot on the split 0 of PASCAL-5i:

sh train.sh pascal split0_1shot   

Acknowledgement

We gratefully thank the authors of PANet and PPNet that inspire our implementation.

Citation

If you find this project useful, please consider citing:

@InProceedings{tian2022gfsseg,
    title={Generalized Few-shot Semantic Segmentation},
    author={Zhuotao Tian and Xin Lai and Li Jiang and Shu Liu and Michelle Shu and Hengshuang Zhao and Jiaya Jia},
    booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    year={2022}
}