Semantic correspondence

Few-shot segmentation

Cost Aggregation with 4D Convolutional Swin Transformer for Few-Shot Segmentation (ECCV'22)

Check out project [Project Page] and the paper on [arXiv]. We will update codes and pretrained weights will be ready before the conference.

ECCV'22 camera ready version can be found here : [arXiv].

Semantic matching codes are available at semantic-matching branch.

Network

Our model VAT is illustrated below:

Environment Settings

git clone https://github.com/Seokju-Cho/Volumetric-Aggregation-Transformer.git

cd Volumetric-Aggregation-Transformer

conda env create -f environment.yaml

Preparing Few-Shot Segmentation Datasets

Download following datasets:

1. PASCAL-5ⁱ

Download PASCAL VOC2012 devkit (train/val data):

wget http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar

Download PASCAL VOC2012 SDS extended mask annotations from our [Google Drive].

2. COCO-20ⁱ

Download COCO2014 train/val images and annotations:

wget http://images.cocodataset.org/zips/train2014.zip
wget http://images.cocodataset.org/zips/val2014.zip
wget http://images.cocodataset.org/annotations/annotations_trainval2014.zip

Download COCO2014 train/val annotations from our Google Drive: [train2014.zip], [val2014.zip]. (and locate both train2014/ and val2014/ under annotations/ directory).

3. FSS-1000

Download FSS-1000 images and annotations from our [Google Drive].

Create a directory '../Datasets_VAT' for the above three few-shot segmentation datasets and appropriately place each dataset to have following directory structure:

../                         # parent directory
└── Datasets_VAT/
    ├── VOC2012/            # PASCAL VOC2012 devkit
    │   ├── Annotations/
    │   ├── ImageSets/
    │   ├── ...
    │   └── SegmentationClassAug/
    ├── COCO2014/           
    │   ├── annotations/
    │   │   ├── train2014/  # (dir.) training masks (from Google Drive) 
    │   │   ├── val2014/    # (dir.) validation masks (from Google Drive)
    │   │   └── ..some json files..
    │   ├── train2014/
    │   └── val2014/
    └── FSS-1000/           # (dir.) contains 1000 object classes
        ├── abacus/   
        ├── ...
        └── zucchini/

Training

Training on PASCAL-5ⁱ:

  python train.py --config "config/pascal_resnet{50, 101}/pascal_resnet{50, 101}_fold{0, 1, 2, 3}/config.yaml"

Training on COCO-20ⁱ:

  python train.py --config "config/coco_resnet50/coco_resnet50_fold{0, 1, 2, 3}/config.yaml"

Training on FSS-1000:

  python train.py --config "config/fss_resnet{50, 101}/config.yaml"

Evaluation

• Download pre-trained weights on Link

Result on PASCAL-5ⁱ:

  python test.py --load "/path_to_pretrained_model/pascal_resnet{50, 101}/pascal_resnet{50, 101}_fold{0, 1, 2, 3}/"

Result on COCO-20ⁱ:

  python test.py --load "/path_to_pretrained_model/coco_resnet50/coco_resnet50_fold{0, 1, 2, 3}/"

Results on FSS-1000:

  python test.py --load "/path_to_pretrained_model/fss_resnet{50, 101}/"

Acknowledgement

We borrow code from public projects (huge thanks to all the projects). We mainly borrow code from HSNet.

BibTeX

If you find this research useful, please consider citing:

@article{hong2021cost,
  title={Cost Aggregation Is All You Need for Few-Shot Segmentation},
  author={Hong, Sunghwan and Cho, Seokju and Nam, Jisu and Kim, Seungryong},
  journal={arXiv preprint arXiv:2112.11685},
  year={2021}
}