Awesome

Meta-Adapter: An Online Few-shot Learner for Vision-Language Model

Cheng Cheng, Lin Song, Ruoyi Xue, Hang Wang, Hongbin Sun, Yixiao Ge, Ying Shan

Meta-Adapter, a new few-shot learning method for CLIP, targets to overcome the limitations of previous methods in terms of poor generalization ability and low efficiency. The Meta-Adapter, employing a meta-testing mechanism and a lightweight residual-style network, extracts knowledge from few-shot samples without the need for additional fine-tuning, thus alleviating the over-fitting issue while maintaining high efficiency.

Arxiv Paper

Installation

1. This code is built on top of the toolbox Dassl.pytorch so you need to install the dassl environment first. Simply follow the instructions described here to install dassl as well as PyTorch.

2. Follow guidelines to install the datasets.

3. torch>=2.0.0, cuda==11.8

Model Zoo

1. The pre-trained weights of Meta-Adapter on ImageNet based on RN50, RN101, ViT-B/16 and ViT-B/32 can be downloaded altogether via this link. The weights can be used to reproduce the results in Table 3 of Meta-Adapter's paper.

2. Quantitative results on other datasets are as follows:

Getting Start

Validate

1. change root_path in $DATA.yaml, the default configurations are shots=16 and backbone=RN50;
2. run python main.py --config ./configs/$DATA.yaml;

Ref

[1]: Zhang, Renrui, Wei Zhang, Rongyao Fang, Peng Gao, Kunchang Li, Jifeng Dai, Yu Qiao, and Hongsheng Li. "Tip-adapter: Training-free adaption of clip for few-shot classification." In European Conference on Computer Vision, pp. 493-510. Cham: Springer Nature Switzerland, 2022. https://arxiv.org/pdf/2207.09519.pdf

[2]: Gao, Peng, Shijie Geng, Renrui Zhang, Teli Ma, Rongyao Fang, Yongfeng Zhang, Hongsheng Li, and Yu Qiao. "Clip-adapter: Better vision-language models with feature adapters." International Journal of Computer Vision (2023): 1-15. https://arxiv.org/pdf/2110.04544.pdf

[3]: Zhou, Kaiyang, Jingkang Yang, Chen Change Loy, and Ziwei Liu. "Learning to prompt for vision-language models." International Journal of Computer Vision 130, no. 9 (2022): 2337-2348. https://arxiv.org/pdf/2109.01134

Acknowledgement

If you find Meta-Adapter helpful, please cite:

@inproceedings{cheng2023meta,
  title={Meta-Adapter: An Online Few-shot Learner for Vision-Language Model},
  author={Cheng, Cheng and Song, Lin and Xue, Ruoyi and Wang, Hang and Sun, Hongbin and Ge, Yixiao and Shan, Ying},
  booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
  year={2023}
}