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CWT-for-FSS

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Official code for "Simpler is Better: Few-shot Semantic Segmentation with Classifier Weight Transformer. ICCV2021".

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최종 버전 다운로드 (.zip)

- coco.yaml
- coco2pascal.yaml
- pascal.yaml
- pascal2coco.yaml
- framework.jpg
- - train.txt
  - val.txt
- - train.txt
  - val.txt
- test.sh
- train.sh
- - classifier.cpython-36.pyc
  - classifier.cpython-37.pyc
  - optimizer.cpython-36.pyc
  - optimizer.cpython-37.pyc
  - test.cpython-36.pyc
  - test.cpython-37.pyc
  - test_denet.cpython-37.pyc
  - test_gen.cpython-36.pyc
  - test_gen.cpython-37.pyc
  - train.cpython-37.pyc
  - train_gen.cpython-37.pyc
  - train_transformer.cpython-36.pyc
  - train_transformer.cpython-37.pyc
  - util.cpython-36.pyc
  - util.cpython-37.pyc
- - classes.py
  - dataset.py
  - transform.py
  - utils.py
- - pspnet.py
  - resnet.py
  - transformer.py
  - vgg.py
- optimizer.py
- test.py
- train.py
- util.py
- - __init__.cpython-36.pyc
  - __init__.cpython-37.pyc
  - metrics.cpython-36.pyc
  - metrics.cpython-37.pyc
  - operations.cpython-36.pyc
  - operations.cpython-37.pyc
- __init__.py
- metrics.py
- operations.py
LICENSE
README.md

// repository documentation

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# Simpler is Better: Few-shot Semantic Segmentation with Classifier Weight Transformer. ICCV2021.

## Introduction
We proposed a novel model training paradigm for few-shot semantic segmentation. Instead of meta-learning the whole, complex segmentation model, we focus on the simplest
classifier part to make new-class adaptation more tractable. Also, a novel meta-learning algorithm that leverages a Classifier Weight Transformer (CWT) for adapting dynamically the classifier weights to every query sample is introduced to eliminate the impact of intra-class discripency.

## Architecture
<a href="url"><img src="https://github.com/zhiheLu/CWT-for-FSS/blob/main/doc/framework.jpg" align="center" height="350" width="900" ></a>

## Environment
Other configurations can also work, but the results may be slightly different.
- torch==1.6.0
- numpy==1.19.1
- cv2==4.4.0
- pyyaml==5.3.1

## Dataset
We follow the same rule to download and process dataset as that in https://github.com/Jia-Research-Lab/PFENet. After processing, please change the "data_root" and "train/val_list" in config files accordingly.

## Pre-trained models in the first stage
For convenience, we provide the pre-trained models on base classes for each split. Download it here: https://drive.google.com/file/d/1VPBquiy4DZXu8b6qsSB6XtO5_6jTpXgo/view?usp=sharing, and change "resume_weights" to this folder.

## Episodic training and inference
- The general training script
```python
sh scripts/train.sh {data} {split} {[gpu_ids]} {layers} {shots}
```
- This is an example with 1-shot, ResNet-50, split-0 on PASCAL and GPU device [0].
```python
sh scripts/train.sh pascal 0 [0] 50 1
```
- Inference script
```python
sh scripts/test.sh {data} {shot} {[gpu_ids]} {layers} {split}
```

## Contact
Please write down issues or contact me via zhihe.lu [at] surrey.ac.uk if you have any questions.

## Citation
If you feel helpful of this work, please cite it.

```
@inproceedings{lu2021simpler,
  title={Simpler is Better: Few-shot Semantic Segmentation with Classifier Weight Transformer},
  author={Lu, Zhihe and He, Sen and Zhu, Xiatian and Zhang, Li and Song, Yi-Zhe and Xiang, Tao},
  booktitle={ICCV},
  year={2021}
}
```

## Acknowledgments
Thanks to the code contributors. Some parts of code are borrowed from https://github.com/Jia-Research-Lab/PFENet and https://github.com/mboudiaf/RePRI-for-Few-Shot-Segmentation.