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We propose SUMix, which consists of a mix ratio learning module and an uncertain estimation module. The former focuses on computing the proportion of two images and the latter aims to learn the uncertainty information of mixed samples. Firstly, we design a function to compute the semantic distance between the mixed and original samples to determine the ratio lambda. Secondly, we present a method to learn the uncertainty of the mixed. This adapted feature vector effectively mitigates issues related to computing the loss function caused by discrepancies in semantic and uncertainty aspects.

📬 You can contact me by email: 158398730@qq.com or WeChat: xinxinxinxin_j.

If you are interested in palm or finger vein research, please contact us!

🛠 Installation

💥News! ! !💥
2024-07-8: Please Wait for a while, we will release the checkpoint!

🔧How to install?🔧
In fact, you can add our python file in OpenMixup.
There, you can see how to use it and the environment required. What you need to do is add or replace our files by folder inside OpenMixup, and then add the function names of the files in the __init__.py file.
You also can download or find other Mixup methods in OpenMixup("https://github.com/Westlake-AI/openmixup")
Thanks contributors: Siyuan Li(@Lupin1998), Zichen Liu(@pon7) and Zedong Wang(@Jacky1128).

Here are the commands to install OpenMixup

conda create -n openmixup python=3.8 pytorch=1.12 cudatoolkit=11.3 torchvision -c pytorch -y
conda activate openmixup
pip install openmim
mim install mmcv-full
git clone https://github.com/Westlake-AI/openmixup.git
cd openmixup
python setup.py develop

Here are the commands to git clone SUMix

git clone https://github.com/JinXins/SUMix.git

📊 Experiments

CIFAR-100

Name alpha Conference ResNet18 ResNeXt50 Wide-ResNet28 DeiT-Small Swin-Tiny
CutMix 0.2 ICCV2019 78.17 78.32 84.45 74.12 80.64
SaliencyMix 0.2 ICLR2021 79.12 78.77 84.35 69.78 80.40
FMix 0.2 ArXiv 79.69 79.02 84.21 70.41 80.72
ResizeMix 1.0 CVMJ2023 80.01 80.35 84.87 68.45 80.16
CutMix+SUMix 0.2 - 79.78 79.91 84.56 75.26 80.32
SaliencyMix+SUMix 0.2 - 79.91 79.32 84.58 70.32 84.71
FMix+SUMix 0.2 - 80.20 80.79 84.32 70.69 80.73
ResizeMix+SUMix 1.0 - 80.38 80.72 84.91 68.78 80.59

Tiny-ImageNet & ImageNet-1K(denote *)

Name alpha Conference ResNet18 ResNeXt50 ResNet18
CutMix 0.2 ICCV2019 65.53 66.47 68.95
SaliencyMix 0.2 ICLR2021 64.60 66.55 69.16
FMix 0.2 ArXiv 63.47 65.08 69.96
ResizeMix 1.0 CVMJ2023 63.74 65.87 69.50
CutMix+SUMix 0.2 - 65.71 68.74 69.71
SaliencyMix+SUMix 0.2 - 65.68 68.92 69.52
FMix+SUMix 0.2 - 63.69 67.12 70.48
ResizeMix+SUMix 1.0 - 65.30 67.49 69.76

CUB-200, FGVC-Aircraft and Standford Cars

Name alpha Confrence CUB R18 CUB RX50 FGVC R18 FGVC RX50
CutMix 0.2 ICCV2019 77.70 83.67 78.84 84.55
SaliencyMix 0.2 ICLR2021 75.77 82.83 79.78 84.31
FMix 0.2 ArXiv 77.28 84.06 79.36 84.10
ResizeMix 1.0 CVMJ2023 78.50 84.16 78.10 84.08
CutMix+SUMix 0.2 - 78.20 83.71 79.72 85.84
SaliencyMix+SUMix 0.2 - 76.98 82.84 79.90 84.49
FMix+SUMix 0.2 - 79.24 84.33 79.48 84.64
ResizeMix+SUMix 1.0 - 78.56 84.23 80.29 85.12

😉 Citation

If you feel that our work has contributed to your research, please cite it, 🥰 and please don`t forget to cite OpenMixup if you use this project ! 🤗 Thanks.

@article{qin2024sumix,
  title={SUMix: Mixup with Semantic and Uncertain Information},
  author={Qin, Huafeng and Jin, Xin and Zhu, Hongyu and Liao, Hongchao and El-Yacoubi, Moun{\^\i}m A and Gao, Xinbo},
  journal={arXiv preprint arXiv:2407.07805},
  year={2024}
}