QPENet_TMM2024

Runmin Cong, Hang Xiong, Jinpeng Chen, Wei Zhang, Qingming Huang, and Yao Zhao, Query-guided Prototype Evolution Network for Few-Shot Segmentation, IEEE Transactions on Multimedia. In Press.

Network

Our overall framework:

GBC Module

Requirement:

Pleasure configure the environment according to the given version:

• python 3.6.13
• pytorch 1.8.2+cu111
• torchvision 0.9.2
• numpy 1.19.5
• opencv-python 4.6.0.66
• pycocotools 2.0.6

Data Preparation

Please follow the tips to download the processed datasets:

1. PASCAL-5i: Please refer to PFENet to prepare the PASCAL dataset for few-shot segmentation.
2. COCO-20i: Please download COCO2017 dataset from here. Put or link the dataset to YOUR_PROJ_PATH/data/coco.

Training and Testing

Training command : Download the ImageNet pretrained backbones and put them into the initmodel directory.

Then, run this command:

sh train.sh

Testing command :

• Change configuration via the .yaml files in config (specify checkpoint path)
• Run the following command:

sh test.sh

We provide 16 pre-trained models: ResNet-50 and ResNet-101 based models for PASCAL-5i and COCO.

Results

Citation

  @article{crm/tmm24/QPENet,
           author={Cong, Runmin and Xiong, Hang and Chen, Jinpeng and Zhang, Wei and Huang, Qingming and Zhao, Yao},
           journal={IEEE Transactions on Multimedia}, 
           title={Query-guided Prototype Evolution Network for Few-Shot Segmentation}, 
           year={2024},
           }

Contact Us

If you have any questions, please contact Runmin Cong at [email protected] or Hang Xiong at [email protected].