https://github.com/chunbolang/HPA

Official PyTorch Implementation of Holistic Prototype Activation for Few-Shot Segmentation (TPAMI'22).
https://github.com/chunbolang/HPA

computer-vision few-shot-segmentation

Last synced: about 2 months ago
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Official PyTorch Implementation of Holistic Prototype Activation for Few-Shot Segmentation (TPAMI'22).

• Host: GitHub
• URL: https://github.com/chunbolang/HPA
• Owner: chunbolang
• License: mit
• Created: 2021-09-02T02:15:42.000Z (about 3 years ago)
• Default Branch: main
• Last Pushed: 2022-07-22T07:17:50.000Z (about 2 years ago)
• Last Synced: 2024-07-05T05:04:28.103Z (3 months ago)
• Topics: computer-vision, few-shot-segmentation
• Language: Python
• Homepage:
• Size: 7.82 MB
• Stars: 21
• Watchers: 1
• Forks: 3
• Open Issues: 3
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• Awesome-Few-Shot-Semantic-Segmentation - CODE

README

        
# Holistic Prototype Activation for Few-Shot Segmentation

This repo contains the code for our **IEEE TPAMI 2022** paper "*Holistic Prototype Activation for Few-Shot Segmentation*" by Gong Cheng, Chunbo Lang, and Junwei Han.

## 📋 Note

Please refer to our BAM [repository](https://github.com/chunbolang/BAM) for the latest **training/testing** scripts. HPA can also be naturally combined with BAM (state-of-the-art) as a stronger meta-learner, with potential for further improvement.

##

### Dependencies

- Python 3.6

- PyTorch 1.3.1

- cuda 9.0

- torchvision 0.4.2

- tensorboardX 2.1

### Datasets

- PASCAL-5i:  [VOC2012](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/) + [SBD](http://home.bharathh.info/pubs/codes/SBD/download.html)

- COCO-20i:  [COCO2014](https://cocodataset.org/#download)

   Please see [OSLSM](https://arxiv.org/abs/1709.03410) and [FWB](https://openaccess.thecvf.com/content_ICCV_2019/html/Nguyen_Feature_Weighting_and_Boosting_for_Few-Shot_Segmentation_ICCV_2019_paper.html) for more details on datasets. 

### Usage

1. Download the prior prototypes of base categories from our [Google Drive](https://drive.google.com/file/d/11-VHCAAO6NcnP2OzZdT2rNrGpC9LqKPh/view?usp=sharing) and put them under `HPA/initmodel/prototypes`. 

2. Download the pre-trained backbones from [here](https://drive.google.com/file/d/1AQcvMHHpURZM67MMgV-S3T0Kz-h2q7FR/view?usp=sharing).

3. Change configuration via the `.yaml` files in `HPA/config`, then run the `.sh` scripts for training and testing.

### To-Do List

- [x] Support different backbones

- [x] Support various annotations for training/testing

- [ ] Zero-Shot Segmentation (ZSS)

- [ ] FSS-1000 dataset

- [ ] Multi-GPU training

### References

This repo is built based on [PFENet](https://github.com/dvlab-research/PFENet) and [DANet](https://github.com/junfu1115/DANet). Thanks for their great work!

### BibTeX

If you find our work and this repository useful. Please consider giving a star :star: and citation 📚.

```bibtex

@article{lang2022hpa,

  title={Holistic Prototype Activation for Few-Shot Segmentation},

  author={Cheng, Gong and Lang, Chunbo and Han, Junwei},

  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},

  year={2022},

}

```