#PFENet This is the implementation of our paper PFENet: Prior Guided Feature Enrichment Network for Few-shot Segmentation that has been accepted to IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI).

Get Started

#Environment torch==1.4.0 (torch version >= 1.0.1.post2 should be okay to run this repo) numpy==1.18.4 tensorboardX==1.8 cv2==4.2.0 Datasets and Data Preparation Please download the following datasets:

PASCAL-5i is based on the PASCAL VOC 2012 and SBD where the val images should be excluded from the list of training samples.

#COCO 2014.

This code reads data from .txt files where each line contains the paths for image and the correcponding label respectively. Image and label paths are seperated by a space. Example is as follows:

image_path_1 label_path_1 image_path_2 label_path_2 image_path_3 label_path_3 ... image_path_n label_path_n Then update the train/val/test list paths in the config files.

[Update] We have uploaded the lists we use in our paper. The train/val lists for COCO contain 82081 and 40137 images respectively. They are the default train/val splits of COCO. The train/val lists for PASCAL5i contain 5953 and 1449 images respectively. The train list should be voc_sbd_merge_noduplicate.txt and the val list is the original val list of pascal voc (val.txt). To get voc_sbd_merge_noduplicate.txt: We first merge the original VOC (voc_original_train.txt) and SBD (sbd_data.txt) training data. [Important] sbd_data.txt does not overlap with the PASCALVOC 2012 validation data. The merged list (voc_sbd_merge.txt) is then processed by the script (duplicate_removal.py) to remove the duplicate images and labels. Run Demo / Test with Pretrained Models Please download the pretrained models.

We provide 8 pre-trained models: 4 ResNet-50 based models for PASCAL-5i and 4 VGG-16 based models for COCO.

Update the config file by speficifying the target split and path (weights) for loading the checkpoint.

Execute mkdir initmodel at the root directory.

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

Then execute the command:

sh test.sh {dataset} {model_config}

Example: Test PFENet with ResNet50 on the split 0 of PASCAL-5i:

sh test.sh pascal split0_resnet50 Train Execute this command at the root directory:

sh train.sh {dataset} {model_config} Related Repositories This project is built upon a very early version of SemSeg: https://github.com/hszhao/semseg.

#Other projects in few-shot segmentation:

OSLSM: https://github.com/lzzcd001/OSLSM CANet: https://github.com/icoz69/CaNet PANet: https://github.com/kaixin96/PANet FSS-1000: https://github.com/HKUSTCV/FSS-1000 AMP: https://github.com/MSiam/AdaptiveMaskedProxies On the Texture Bias for FS Seg: https://github.com/rezazad68/fewshot-segmentation SG-One: https://github.com/xiaomengyc/SG-One FS Seg Propogation with Guided Networks: https://github.com/shelhamer/revolver Many thanks to their greak work!

#Citation If you find this project useful, please consider citing:

@article{tian2020pfenet, title={Prior Guided Feature Enrichment Network for Few-Shot Segmentation}, author={Tian, Zhuotao and Zhao, Hengshuang and Shu, Michelle and Yang, Zhicheng and Li, Ruiyu and Jia, Jiaya}, journal={TPAMI}, year={2020} }