Adapt Before Comparision for Cross-Domain Few-Shot Segmentation (ABCDFSS)

[Paper] accepted for CVPR'24.

Two options:

1. Predict an individual task
2. Predict tasks sampled from a dataset

task = {query image, support image(s), binary support mask(s)}

Predict an individual task

1. Prepare your files for the task.
2. Upload in the DEMO OR git clone the huggingface repo to either (a) call from_model(task) in app.py or (b) run the gradio app locally to let it use your GPU.

Predict tasks sampled from a dataset

1. Prepare the dataset: data/README.md.

2. Call python main.py --benchmark {} --datapath {} --nshot {},
   for example python main.py --benchmark deepglobe --datapath ./datasets/deepglobe/ --nshot 1
   Available benchmark strings: deepglobe,isic,lung,fss,suim.

Default is quick-infer mode.
To change this, pass --adapt-to every-episode.
To turn on post-processing, pass --postprocessing [always|dynamic].
To change other parameters, check the available parameters in core/runner.py makeConfig().
Select --verbosity 1 to get printed what's currently happening while runnning the loop.
Consult eval/README.md for notes on reproducing results.

Limitations

This work might give you inspiration to try some adaption before comparison for CD-FSS. You might be interested in my opinion that

1. It is quite possible that there is a better specific adaption algorithm that you can find in your research.
2. It is also reasonable to replace the part after the comparison with a learned network, this work only demonstrated that even without such, one can get better results than previous works.
3. Lastly, for the latest best performance, you might want to refer to the other concurrent CD-FSS works.

Citation

If this work finds use in your research, please cite:

@article{herzog2024cdfss,
      title={Adapt Before Comparison: A New Perspective on Cross-Domain Few-Shot Segmentation}, 
      author={Jonas Herzog},
      journal={arXiv preprint arXiv:2402.17614},
      year={2024}
}