Self-Promoted Prototype Refinement for Few-Shot Class-Incremental Learning

This is the implementation of the paper "Self-Promoted Prototype Refinement for Few-Shot Class-Incremental Learning" (accepted to CVPR2021).

For more information, check out the paper on [arXiv].

Requirements

• Python 3.8
• PyTorch 1.8.1 (>1.1.0)
• cuda 11.2

Preparing Few-Shot Class-Incremental Learning Datasets

Download following datasets:

1. CIFAR-100

Automatically downloaded on torchvision.

2. MiniImageNet

(1) Download MiniImageNet train/test images[github], and prepare related datasets according to [TOPIC].

(2) or Download processed data from our Google Drive: [mini-imagenet.zip], (and locate the entire folder under datasets/ directory).

3. CUB200

(1) Download CUB200 train/test images, and prepare related datasets according to [TOPIC]:

wget http://www.vision.caltech.edu/visipedia-data/CUB-200-2011/CUB_200_2011.tgz

(2) or Download processed data from our Google Drive: [cub.zip], (and locate the entire folder under datasets/ directory).

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

../                                                        # parent directory
├── ./                                           # current (project) directory
│   ├── log/                              # (dir.) running log
│   ├── pre/                              # (dir.) trained models for test.
│   ├── utils/                            # (dir.) implementation of paper 
│   ├── README.md                          # intstruction for reproduction
│   ├── test.sh                          # bash for testing.
│   ├── train.py                        # code for training model
│   └── train.sh                        # bash for training model
└── datasets/
    ├── CIFAR100/                      # CIFAR100 devkit
    ├── mini-imagenet/           
    │   ├── train/                         # (dir.) training images (from Google Drive)
    │   ├── test/                           # (dir.) testing images (from Google Drive)
    │   └── ..some csv files..
    └── cub/                                   # (dir.) contains 200 object classes
        ├── train/                             # (dir.) training images (from Google Drive)
        └── test/                               # (dir.) testing images (from Google Drive)

Training

Choose apporopriate lines in train.sh file.

sh train.sh

• '--base_epochs' can be modified to control the initial accuracy ('Our' vs 'Our*' in our paper).
• Training takes approx. several hours until convergence (trained with one 2080 Ti or 3090 GPUs).

Testing

1. Download pretrained models to the 'pre' folder.

Pretrained models are available on our [Google Drive].

2. Test

Choose apporopriate lines in test.sh file.

sh test.sh 

Main Results

The experimental results with 'test.sh 'for three datasets are shown below.

1. CIFAR-100

Model	1	2	3	4	5	6	7	8	9
iCaRL	64.10	53.28	41.69	34.13	27.93	25.06	20.41	15.48	13.73
TOPIC	64.10	56.03	47.89	42.99	38.02	34.60	31.67	28.35	25.86
Ours	63.97	65.86	61.31	57.6	53.39	50.93	48.27	45.36	43.32

2. MiniImageNet

Model	1	2	3	4	5	6	7	8	9
iCaRL	61.31	46.32	42.94	37.63	30.49	24.00	20.89	18.80	17.21
TOPIC	61.31	45.58	43.77	37.19	32.38	29.67	26.44	25.18	21.80
Ours	61.45	63.80	59.53	55.53	52.50	49.60	46.69	43.79	41.92

3. CUB200

Model	1	2	3	4	5	6	7	8	9	10	11
iCaRL	68.68	52.65	48.61	44.16	36.62	29.52	27.83	26.26	24.01	23.89	21.16
TOPIC	68.68	61.01	55.35	50.01	42.42	39.07	35.47	32.87	30.04	25.91	24.85
Ours	68.05	62.01	57.61	53.67	50.77	46.76	45.43	44.53	41.74	39.93	38.45

The presented results are slightly different from those in the paper, which are the average results of multiple tests.

BibTeX

If you use this code for your research, please consider citing:

@inproceedings{zhu2021self,
  title={Self-Promoted Prototype Refinement for Few-Shot Class-Incremental Learning},
  author={Zhu, Kai and Cao, Yang and Zhai, Wei and Cheng, Jie and Zha, Zheng-Jun},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={6801--6810},
  year={2021}
}