Awesome

Category Adaptation Meets Projected Distillation in Generalized Continual Category Discovery

This repository contains code for ECCV 2024 paper: https://arxiv.org/abs/2308.12112
Introduction video: https://www.youtube.com/watch?v=NfgT1qpOb4U

Setup:

pip install requirements.txt

To run pretrained ViT download a model from https://github.com/facebookresearch/dino:

mkdir pretrained && cd pretrained
wget https://dl.fbaipublicfiles.com/dino/dino_deitsmall16_pretrain/dino_deitsmall16_pretrain.pth

Download datasets

CUB200

cd data
wget https://data.caltech.edu/records/65de6-vp158/files/CUB_200_2011.tgz?download=1
tar zxvf 'CUB_200_2011.tgz?download=1'

DomainNet

wget http://csr.bu.edu/ftp/visda/2019/multi-source/groundtruth/clipart.zip &&
wget http://csr.bu.edu/ftp/visda/2019/multi-source/infograph.zip &&
wget http://csr.bu.edu/ftp/visda/2019/multi-source/groundtruth/painting.zip &&
wget http://csr.bu.edu/ftp/visda/2019/multi-source/quickdraw.zip &&
wget http://csr.bu.edu/ftp/visda/2019/multi-source/real.zip &&
wget http://csr.bu.edu/ftp/visda/2019/multi-source/sketch.zip

Run CAMP method on 5 datasets:

bash scripts/camp.sh

Run GCD method on 5 datasets:

bash scripts/gcd.sh

Run GCD+FD method on 5 datasets:

bash scripts/gcd_fd.sh

Run GCD+EWC method on 5 datasets:

bash scripts/gcd_ewc.sh

Run Proxy Anchors method on 5 datasets:

bash scripts/proxy_anchors.sh

If you find this code or paper useful we will appreciate if you cite us:

@inproceedings{rypesc2024category,
    title   = {Category Adaptation Meets Projected Distillation in Generalized Continual Category Discovery},
    author={Rype{\'s}{\'c}, Grzegorz and Marczak, Daniel and Cygert, Sebastian  and Trzci{\'n}ski, Tomasz and Twardowski, Bart{\l}omiej},
    booktitle = {European Conference on Computer Vision (ECCV)},
    year    = {2024}
}