https://github.com/dahyun-kang/ifsl

[CVPR'22] Official PyTorch implementation of Integrative Few-Shot Learning for Classification and Segmentation
https://github.com/dahyun-kang/ifsl

computer-vision cvpr2022 deep-learning few-shot-classification few-shot-learning few-shot-segmentation pytorch pytorch-lightning

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

• Host: GitHub
• URL: https://github.com/dahyun-kang/ifsl
• Owner: dahyun-kang
• License: mit
• Created: 2022-03-03T15:00:04.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2024-01-02T02:30:12.000Z (10 months ago)
• Last Synced: 2024-04-20T17:00:29.825Z (7 months ago)
• Topics: computer-vision, cvpr2022, deep-learning, few-shot-classification, few-shot-learning, few-shot-segmentation, pytorch, pytorch-lightning
• Language: Python
• Homepage: https://arxiv.org/abs/2203.15712
• Size: 8.54 MB
• Stars: 119
• Watchers: 5
• Forks: 15
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        


  
 Integrative Few-Shot Learning 
 for Classification and Segmentation





  


Dahyun Kang      Minsu Cho










  

  








  



This repo is the official implementation of the CVPR 2022 paper: [Integrative Few-Shot Learning for Classification and Segmentation](https://arxiv.org/abs/2203.15712).

## :scroll: BibTex source

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

```BibTeX

@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}

}

```

## :gear: Conda environmnet installation

This project is built upon the following environment:

* [Ubuntu 16.04](https://ubuntu.com/download)

* [Python 3.7](https://pytorch.org)

* [CUDA 11.0](https://developer.nvidia.com/cuda-toolkit)

* [PyTorch 1.7.0](https://pytorch.org)

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

* [`pytorch`](https://pytorch.org)==1.7.0

* [`torchvision`](https://pytorch.org/vision/stable/index.html)==0.8.1

* [`cudatoolkit`](https://developer.nvidia.com/cuda-toolkit)==11.0.3

* [`pytorch-lightning`](https://www.pytorchlightning.ai/)==1.3.8

* [`einops`](https://einops.rocks/pytorch-examples.html)==0.3.0

```bash

conda env create --name ifsl_pytorch1.7.0 --file environment.yml -p YOURCONDADIR/envs/ifsl_pytorch1.7.0

conda activate ifsl_pytorch1.7.0

```

Make sure to replace `YOURCONDADIR` in the installation path with your conda dir, e.g., `~/anaconda3`

## :books: Datasets

* [PASCAL VOC 2012](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/) and [SBD](http://home.bharathh.info/pubs/codes/SBD/download.html)

* [Microsoft COCO 2014](https://cocodataset.org/#download)

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](https://github.com/juhongm999/hsnet) and [PFENet](https://github.com/dvlab-research/PFENet).

## :mag: Related repos

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

* [[PANet]](https://github.com/kaixin96/PANet): Wang _et al_.,  Few-shot image semantic segmentation with prototype alignment, ICCV'19.

* [[PFENet]](https://github.com/dvlab-research/PFENet): Tian _et al_., Prior guided feature enrichment network for few-shot segmentation, TPAMI'20.

* [[HSNet]](https://github.com/juhongm999/hsnet):  Min _et al_., Hypercorrelation squeeze for few-shot segmentation, ICCV'21.

## :bow: Acknowledgements

This work was supported by Samsung Advanced Institute of Technology (SAIT) and also by Center for Applied Research in Artificial Intelligence (CARAI) grant funded by DAPA and ADD (UD190031RD).

We also thank [Ahyun Seo](https://github.com/ahyunSeo) and [Deunsol Jung](https://github.com/hesedjds) for their helpful discussion.