This is the code repository of "Incremental Residual Concept Bottleneck Models".
The paper is accepted by CVPR 2024.
- Setup environments:
pip install -r requirements.txt
- Prepare concept bank:
# ConceptNet concepts:
python conceptnet_concepts.py --save_path ...
# Visual Genome concepts (candidate concept bank):
run generate_vg_concepts.ipynb
# Res-CBM concepts (base concept bank):
run generate_res_concepts.ipynb
- Get image representations:
python get_img_repre.py
- Prepare datasets:
You can implement your Dataset and Dataloader in dataset.
- First, you can run Residual Concept Bottleneck Models by command:
bash scripts/predict.sh
# or
python train.py --task prediction \
--dataset cifar10 \
--res_dim 10 \
--cpt_path cifar10/cifar10_res_num_237_len_7.pkl
- Then, you can run Incremental Concept Discovery by command:
bash scripts/discover.sh
# or
python train.py --task discovery \
--dataset cifar10 \
--cpt_path concept_bank/cifar10/cifar10_res_num_237_len_7.pkl \
--mdl_path results/prediction/cifar10/Cpt_237_Res_1_Acc_0.8785/model.pt \
--candidate_path concept_bank/vg/vg_num_13936_len_9.pkl \
--epochs 20 --init_lr 0.001 --decay_step 3 --decay_rate 0.5 --init_lr_ 0.01 --decay_step_ 3 --decay_rate_ 0.5 \
--res_dim 10 --sim_reg 0.1 --candidate_num 5
If you find this code useful, please consider citing our paper:
@InProceedings{Shang_2024_CVPR,
author = {Shang, Chenming and Zhou, Shiji and Zhang, Hengyuan and Ni, Xinzhe and Yang, Yujiu and Wang, Yuwang},
title = {Incremental Residual Concept Bottleneck Models},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2024},
pages = {11030-11040}
}