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CALIP: Zero-Shot Enhancement of CLIP with Parameter-free Attention

Official implementation of 'CALIP: Zero-Shot Enhancement of CLIP with Parameter-free Attention'.

The paper has been accepted by AAAI 2023.

Introduction

CALIP is a free-lunch enhancement method to boost CLIP’s zero-shot performance via a parameter-free Attention module. Specifically, we guide visual and textual representations to interact with each other and explore cross-modal informative features via attention. As the pre-training has largely reduced the embedding distances between two modalities, we discard all learnable parameters in the attention and bidirectionally update the multi-modal features, enabling the whole process to be parameter-free and training-free. In this way, the images are blended with textual-aware signals and the text representations become visual-guided for better adaptive zeroshot alignment. We evaluate CALIP on various benchmarks of 14 datasets for both 2D image and 3D point cloud few-shot classification, showing consistent zero-shot performance improvement over CLIP. Based on that, we further insert a small number of linear layers in CALIP’s attention module and verify our robustness under the few-shot settings, which also achieves leading performance compared to existing methods.

Requirements

Installation

Create a conda environment and install dependencies:

git clone https://github.com/ZiyuGuo99/CALIP.git
cd CALIP

conda create -n calip python=3.7
conda activate calip

# Install the according versions of torch and torchvision
conda install pytorch torchvision cudatoolkit

pip install -r requirements.txt

Dataset

Follow DATASET.md to install ImageNet and other 10 datasets according to CoOp.

Get Started

Configs

The configuration for running on each dataset can be modified in configs/*.yaml. You need to fill in the data_root with your data path. Also, you can edit the settings of backbone and search as your need, and feel free to adjust beta2 and beta3 for a wider or finer search range.

Note that the default load_cache is False for the first running, leading to storing the encoded features and labels. It can be set as True for faster hyperparamters tuning during later running.

Running

For ImageNet dataset:

CUDA_VISIBLE_DEVICES=0 python run_imagenet.py --config configs/imagenet.yaml

For other 10 datasets: TODO...

Acknowledgement

This repo benefits from CLIP, CoOp, CLIP-Adapter and Tip-Adapter. Thanks for their wonderful works.

Citation

@article{guo2022calip,
  title={Calip: Zero-shot enhancement of clip with parameter-free attention},
  author={Guo, Ziyu and Zhang, Renrui and Qiu, Longtian and Ma, Xianzheng and Miao, Xupeng and He, Xuming and Cui, Bin},
  journal={arXiv preprint arXiv:2209.14169},
  year={2022}
}

Contact

If you have any question about this project, please feel free to contact 2101210573@pku.edu.cn.