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[WACV 2025] Code for Enhancing Vision-Language Few-Shot Adaptation with Negative Learning

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# [WACV 2025] SimNL

[![Website](https://img.shields.io/badge/Project-Website-green)](https://github.com/zhangce01/SimNL)
[![arXiv](https://img.shields.io/badge/arXiv-2403.12964-red)](http://arxiv.org/abs/2403.12964)
[![Conference](https://img.shields.io/badge/WACV-2025-blue)](https://raw.githubusercontent.com/zhangce01/SimNL/main/[https:/neurips.cc/](https:/wacv2025.thecvf.com/))
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

## 👀Introduction

This repository contains the code for our paper `Enhancing Vision-Language Few-Shot Adaptation with Negative Learning`.

![](https://raw.githubusercontent.com/zhangce01/SimNL/main/fig/qualitative.png)

![](https://raw.githubusercontent.com/zhangce01/SimNL/main/fig/overview.png)

## ⏳Setup

#### 1. Environment

We test our codebase with PyTorch 1.12.1 with CUDA 11.6. Please install corresponding PyTorch and CUDA versions according to your computational resources. Then install the rest of required packages by running `pip install -r requirements.txt`.

#### 2. Dataset

Please follow ```DATASET.md``` to download all the 11 datasets we used for experiments. We adapt this from [CoOp](https://github.com/KaiyangZhou/CoOp/blob/main/DATASETS.md).

#### 3. Extracting Few-Shot Features

You can extract the features by running ``` CUDA_VISIBLE_DEVICES=0 python extract_features.py```.

After running, you can get all the image features from tran/val/test set, as well as the positive/negative textual features in ```caches/[dataset_name]```.

## 📦Usage

You can simply run ```CUDA_VISIBLE_DEVICES=0 python main.py --config configs/[dataset_name].yaml --shot [shot_number]``` to train and test the SimNL model.

Here, `dataset_name` should be one of `[caltech101, dtd, eurosat, fgvc, food101, imagenet, oxford_flowers, oxford_pets, stanford_cars, sun397, ucf101]`, and `shot_number` is chosen from 1/2/4/8/16.

## 🙏Acknowledgements

Our codebase is adapted from [Tip-Adapter](https://github.com/gaopengcuhk/Tip-Adapter/), [CLIP](https://github.com/openai/CLIP/tree/main/clip), [APE](https://github.com/yangyangyang127/APE), and [CuPL](https://github.com/sarahpratt/CuPL). We thank the authors for releasing their code!

## 📧Contact

If you have any questions, please  contact at [cezhang@cs.cmu.edu](mailto:cezhang@cs.cmu.edu).

## 📌 BibTeX & Citation

If you find this code useful, please consider citing our work:

```bibtex
@article{zhang2024enhancing,
  title={Enhancing Vision-Language Few-Shot Adaptation with Negative Learning},
  author={Zhang, Ce and Stepputtis, Simon and Sycara, Katia and Xie, Yaqi},
  booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision},
  pages={5905-5915}
  year={2025}
}
```

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