/ifsl

[CVPR'22] Official PyTorch implementation of Integrative Few-Shot Learning for Classification and Segmentation

Primary LanguagePythonMIT LicenseMIT

Integrative Few-Shot Learning
for Classification and Segmentation

Dahyun Kang      Minsu Cho



result

This repo is the official implementation of the CVPR 2022 paper: Integrative Few-Shot Learning for Classification and Segmentation.

📜 BibTex source

If you find our code or paper useful, please consider citing our paper:

@inproceedings{kang2022ifsl,
  author   = {Kang, Dahyun and Cho, Minsu},
  title    = {Integrative Few-Shot Learning for Classification and Segmentation},
  booktitle= {Proceedings of the {IEEE/CVF} Conference on Computer Vision and Pattern Recognition (CVPR)},
  year     = {2022}
}

⚙️ Conda environmnet installation

This project is built upon the following environment:

The package requirements can be installed via environment.yml, which includes

conda env create --name ifsl_pytorch1.7.0 --file environment.yml
conda activate ifsl_pytorch1.7.0

Make sure you need to replace YOURCONDAROOTDIR in the installation path with your own root dir

📚 Datasets

Download the datasets by following the file structure below and set args.datapath=YOUR_DATASET_DIR:

    YOUR_DATASET_DIR/
    ├── VOC2012/
    │   ├── Annotations/
    │   ├── JPEGImages/
    │   ├── ...
    ├── COCO2014/
    │   ├── annotations/
    │   ├── train2014/
    │   ├── val2014/
    │   ├── ...
    ├── ...

We follow the dataset protocol of HSNet and PFENet.

🔍 Related repos

Our project refers to and heavily borrows some the codes from the following repos:

  • [PANet]: Wang et al., Few-shot image semantic segmentation with prototype alignment, ICCV'19.
  • [PFENet]: Tian et al., Prior guided feature enrichment network for few-shot segmentation, TPAMI'20.
  • [HSNet]: Min et al., Hypercorrelation squeeze for few-shot segmentation, ICCV'21.