[ICCV 2021] Counterfactual Attention Learning for Fine-Grained Visual Categorization and Re-identification

Last update: Dec 31, 2022

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Overview

Counterfactual Attention Learning

Created by Yongming Rao*, Guangyi Chen*, Jiwen Lu, Jie Zhou

This repository contains PyTorch implementation for ICCV 2021 paper Counterfactual Attention Learning for Fine-Grained Visual Categorization and Re-identification [arXiv]

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.

CAL for Fine-Grained Visual Categorization

See CAL-FGVC.

CAL for Person Re-Identification

See CAL-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}
}

Comments

Pre-trained weights

Hello everyone :) thanks for you nice work! Do you provide pre-trained weights for the person re-identification datasets somewhere? Thanks in advance! :)

opened by JennySeidenschwarz 5
cannot reproduce the accuracy on CUB

I am trying to reproduce the result of the CUB dataset, which is 90.6 acc ( table 1 in the paper). However, I use the same config and startup script as the code repo, but only get 90.03 acc for the last epoch. I notice that the total training epoch for fgvc task is not reported in the paper. So what is the proper epoch to get the 90.6 acc? Are there any other reasons that could affect reproducing the acc?

Please see attachment for my training log. Thanks train.log !

opened by yifanpu001 4
about feature matrix which is the input of the last fc layer

Sorry to bother you, I have a question about the fgvc part. Why the normalized feature_matrix and feature_matrix_hat need to multiply 100 before the fc layer?

opened by lynlindasy 4
Output Feature During Inference Stage

I quickly checked the model script baseline.py and found that you used the cls_score as output when doing inference. I am wondering if your published results were generated by this instead of features before classifier (which is regularly applied in popular reid framework).

opened by morgenzhang 4
Reproduce Result for MSMT dataset

I am trying to reproduce the result as shown in the paper for the MSMT which is mAP@64% and [email protected]%; however, I could not do it. May I ask about the backbone you are using to get these results? Is it the same with the code in your repository or do you use different approach? And do you take the best case during training or the result after training for the whole 160 epoch? I am sorry if these questions bother you. Thank in advance!

opened by petertran1811 2

Owner

Yongming Rao

GitHub

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