RiFeNet

This is a PyTorch implementation of AAAI2024 paper "Relevant Intrinsic Feature Enhancement Network for Few-Shot Semantic Segmentation".

Usage

Requirements

Python==3.8
GCC==5.4
torch==1.6.0
torchvision==0.7.0
cython
tensorboardX
tqdm
PyYaml
opencv-python
pycocotools

Build Dependencies

cd model/ops/
bash make.sh
cd ../../

Data Preparation

PASCAL-5^i: Please refer to PFENet to prepare the PASCAL dataset for few-shot segmentation.
COCO-20^i: Please download COCO2017 dataset from here. Put or link the dataset to YOUR_PROJ_PATH/data/coco. And make the directory like this:

${YOUR_PROJ_PATH}
|-- data
`-- |-- coco
    `-- |-- annotations
        |   |-- instances_train2017.json
        |   `-- instances_val2017.json
        |-- train2017
        |   |-- 000000000009.jpg
        |   |-- 000000000025.jpg
        |   |-- 000000000030.jpg
        |   |-- ... 
        `-- val2017
            |-- 000000000139.jpg
            |-- 000000000285.jpg
            |-- 000000000632.jpg
            |-- ...

Then, run

python prepare_coco_data.py

to prepare COCO-20^i data.

Train

Download the ImageNet pretrained backbones and put them into the initmodel directory.

Then, run this command:

    sh train.sh {*dataset*} {*model_config*}

For example,

    sh train.sh pascal split0_resnet50

Test Only

Modify config file (specify checkpoint path)
Run the following command:

    sh test.sh {*dataset*} {*model_config*}

For example,

    sh test.sh pascal split0_resnet50

Acknowledgement

This project is built upon CyCTR and PFENet, thanks for their great works!

Citation

If you find our codes or models useful, please consider to give us a star or cite with:

@article{bao2023relevant,
  title={Relevant Intrinsic Feature Enhancement Network for Few-Shot Semantic Segmentation},
  author={Bao, Xiaoyi and Qin, Jie and Sun, Siyang and Zheng, Yun and Wang, Xingang},
  journal={arXiv preprint arXiv:2312.06474},
  year={2023}
}

baoxiaoyi/RiFeNet