Official Implementation of HCFormer in PyTorch.
arXiv: https://arxiv.org/abs/2205.09949

• Linux with Python ≥ 3.6
• PyTorch ≥ 1.9 and torchvision that matches the PyTorch installation. Install them together at pytorch.org to make sure of this. Note, please check PyTorch version matches that is required by Detectron2.
• Detectron2: follow Detectron2 installation instructions.
• OpenCV is optional but needed by demo and visualization
• pip install -r requirements.txt

See Preparing Datasets for HCFormer.

See Getting Started with HCFormer.

• COCO (top row: clustering results with random colors and predicted masks, bottom row: cluster boundaries and gt masks)
• ADE20K (red regions indicate undersegmentation errors)

A large part of this project relises on the Mask2Former repository.
The code related to Mask2Former is subject to Mask2Former's licence.

If you use HCFormer in your research or wish to refer to the results, please use the following BibTeX entry.

@article{suzuki2022clustering,
  title={Clustering as Attention: Unified Image Segmentation with Hierarchical Clustering},
  author={Suzuki, Teppei},
  journal={arXiv preprint arXiv:2205.09949},
  year={2022}
}

Please also consider the following BibTeX entry. (This paper is a preliminary work for HCFormer)

@article{suzuki2021implicit,
  title={Implicit Integration of Superpixel Segmentation into Fully Convolutional Networks},
  author={Suzuki, Teppei},
  journal={arXiv preprint arXiv:2103.03435},
  year={2021}
}

Code is largely based on Mask2Former (https://github.com/facebookresearch/Mask2Former).