Attentional Feature Fusion

November 21, 2020 · View on GitHub

Attentional Feature Fusion

MXNet/Gluon code for "Attentional Feature Fusion" https://arxiv.org/abs/2009.14082

What's in this repo so far:

Code, trained models, and training logs for ImageNet

PS:

If you are the reviewers of our submitted paper, please note that the accuracy of current implementation is a bit higher than the accuracy in the paper because it is a new implementation with a bag of tricks.
如果您是我的学位论文评审专家，发现论文与这个 repo 的数字有些出入，那是因为在论文提交后我又将代码重新实现了一遍，添加了 AutoAugment、Label Smooting 这些技巧，所以目前这个 repo 中的分类准确率会比论文中的数字高一些，还请见谅。

Change Logs:

2020-10-08: Re-implement the image classification code with a bag of tricks
2020-09-29: Upload the image classification codes and trained models for the submitted paper

To Do:

Running AFF-ResNeXt-50 and AFF-ResNet-50 on ImageNet
Update Grad-CAM results on new trained models
Re-implement the segmentation code
Convert to PyTorch

In Progress:

Running iAFF-ResNeXt-50 on ImageNet

Done:

Re-implement the image classification code with a bag of tricks

Requirements

Install MXNet and Gluon-CV:

pip install --upgrade mxnet-cu101 gluoncv

If you are going to use autoaugment:

python3 -m pip install --upgrade "mxnet_cu101<2.0.0"
python3 -m pip install autogluon

Experiments

All trained model params and training logs are in ./params

The training commands / shell scripts are in cmd_scripts.txt

ImageNet

Architecture	Params	top-1 err.
ResNet-101 [1]	42.5M	23.2
Efficient-Channel-Attention-Net-101 [2]	42.5M	21.4
Attention-Augmented-ResNet-101 [3]	45.4M	21.3
SENet-101 [4]	49.4M	20.9
Gather-Excite- $\theta^{+}$ -ResNet-101 [5]	58.4M	20.7
Local-Importance-Pooling-ResNet-101 [6]	42.9M	20.7
AFF-ResNet-50 (ours)	30.3M	20.3
iAFF-ResNet-50 (ours)	35.1M	20.2
iAFF-ResNeXt-50-32x4d (ours)	34.7M	19.78

PyTorch Version

@bobo0810 has contributed the PyTorch version. Please check the aff_pytorch directory for details.

Many thanks for @bobo0810 for his contribution.

Citation

Please cite our paper in your publications if our work helps your research. BibTeX reference is as follows.

@inproceedings{dai21aff,
  title   =  {Attentional Feature Fusion},
  author  =  {Yimian Dai and Fabian Gieseke and Stefan Oehmcke and Yiquan Wu and Kobus Barnard},
  booktitle =  {{IEEE} Winter Conference on Applications of Computer Vision, {WACV} 2021}
  year    =  {2021}
}

References

[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun: Deep Residual Learning for Image Recognition. CVPR 2016: 770-778

[2] Qilong Wang, Banggu Wu, Pengfei Zhu, Peihua Li, Wangmeng Zuo, Qinghua Hu: ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks. CVPR 2020: 11531-11539

[3] Irwan Bello, Barret Zoph, Quoc Le, Ashish Vaswani, Jonathon Shlens: Attention Augmented Convolutional Networks. ICCV 2019: 3285-3294

[4] Jie Hu, Li Shen, Gang Sun: Squeeze-and-Excitation Networks. CVPR 2018: 7132-7141

[5] Jie Hu, Li Shen, Samuel Albanie, Gang Sun, Andrea Vedaldi: Gather-Excite: Exploiting Feature Context in Convolutional Neural Networks. NeurIPS 2018: 9423-9433

[6] Ziteng Gao, Limin Wang, Gangshan Wu: LIP: Local Importance-Based Pooling. ICCV 2019: 3354-3363

[7] Xiang Li, Wenhai Wang, Xiaolin Hu, Jian Yang: Selective Kernel Networks. CVPR 2019: 510-519

[8] Dongyoon Han, Jiwhan Kim, Junmo Kim: Deep Pyramidal Residual Networks. CVPR 2017: 6307-6315

[9] Zhichao Lu, Gautam Sreekumar, Erik D. Goodman, Wolfgang Banzhaf, Kalyanmoy Deb, Vishnu Naresh Boddeti: Neural Architecture Transfer. CoRR abs/2005.05859 (2020)

[10] Ekin D. Cubuk, Barret Zoph, Dandelion Mane, Vijay Vasudevan, Quoc V. Le: AutoAugment: Learning Augmentation Strategies From Data. CVPR 2019: 113-123