# CAL

**Repository Path**: CharlieShark/CAL

## Basic Information

- **Project Name**: CAL
- **Description**: [ICCV 2021] Counterfactual Attention Learning for Fine-Grained Visual Categorization and Re-identification
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-10-19
- **Last Updated**: 2021-10-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Counterfactual Attention Learning

Created by [Yongming Rao](https://raoyongming.github.io/)\*, [Guangyi Chen](https://chengy12.github.io/)\*, [Jiwen Lu](https://scholar.google.com/citations?user=TN8uDQoAAAAJ&hl=en&authuser=1), [Jie Zhou](https://scholar.google.com/citations?user=6a79aPwAAAAJ&hl=en&authuser=1)

This repository contains PyTorch implementation for ICCV 2021 paper __Counterfactual Attention Learning for Fine-Grained Visual Categorization and Re-identification__ [[arXiv]](https://arxiv.org/abs/2108.08728)

We propose to learn the attention with counterfactual causality, which provides a tool to measure the attention quality and a powerful supervisory signal to guide the learning process. 

![intro](figs/intro.png)

## CAL for Fine-Grained Visual Categorization

See [CAL-FGVC](fgvc/).

## CAL for Person Re-Identification

See [CAL-ReID](reid/).

## License
MIT License

## Citation
If you find our work useful in your research, please consider citing:
```
@inproceedings{rao2021counterfactual,
  title={Counterfactual Attention Learning for Fine-Grained Visual Categorization and Re-identification},
  author={Rao, Yongming and Chen, Guangyi and Lu, Jiwen and Zhou, Jie},
  booktitle={ICCV},
  year={2021}
}
```