Quaternion-valued Correlation Learning for Few-Shot Semantic Segmentation

This is the implementation of our paper Quaternion-valued Correlation Learning for Few-Shot Semantic Segmentation that has been accepted to IEEE Transactions on Circuits and Systems for Video Technology (TCSVT).

Requirements

• Python 3.7
• PyTorch 1.5.1
• cuda 10.1
• tensorboard 1.14

Datasets

• PASCAL-5ⁱ: VOC2012 + SBD

• COCO-20ⁱ: COCO2014

  Please see OSLSM and FWB for more details on datasets.

Test and Train

Using PASCAL-5i as an example

Training

PASCAL-5ⁱ

python train.py  backbone=$BACKBONE$ fold=$FOLD$  dataset=$DATASET$  batch_size=$BATCH_SIZE$  

Testing

PASCAL-5ⁱ

python test.py  backbone=$BACKBONE$ fold=$FOLD$  dataset=$DATASET$  batch_size=$BATCH_SIZE$  load=$BEST_MODEL_PTH$

Visualization

References

This repo is mainly built based on HSNet. Thanks for their great work!

Citation

If you find this project useful, please consider citing:

@article{zheng2022qclnet,
  title={Quaternion-valued Correlation Learning for Few-Shot Semantic Segmentation},
  author={Zheng, Zewen and Huang, Guoheng and Yuan, Xiaochen and Pun, Chi-Man and Liu, Hongrui and Ling, Wing-Kuen},
  journal={IEEE Transactions on Circuits and Systems for Video Technology},
  year={2023}
}