Residual Attention Network for Image Classification
Refereed conference paper presented and published in conference proceedings


Other information
AbstractIn this work, we propose "Residual Attention Network", a convolutional neural network using attention mechanism which can incorporate with state-of-art feed forward network architecture in an end-to-end training fashion. Our Residual Attention Network is built by stacking Attention Modules which generate attention-aware features. The attention-aware features from different modules change adaptively as layers going deeper. Inside each Attention Module, bottom-up top-down feedforward structure is used to unfold the feedforward and feedback attention process into a single feedforward process. Importantly, we propose attention residual learning to train very deep Residual Attention Networks which can be easily scaled up to hundreds of layers. Extensive analyses are conducted on CIFAR-10 and CIFAR-100 datasets to verify the effectiveness of every module mentioned above. Our Residual Attention Network achieves state-of-the-art object recognition performance on three benchmark datasets including CIFAR-10 (3.90% error), CIFAR-100 (20.45% error) and ImageNet (4.8% single model and single crop, top-5 error). Note that, our method achieves 0.6% top-1 accuracy improvement with 46% trunk depth and 69% forward FLOPs comparing to ResNet-200. The experiment also demonstrates that our network is robust against noisy labels.
Acceptance Date21/07/2017
All Author(s) ListFei Wang, Mengqing Jiang, Chen Qian, Shuo Yang, Cheng Li, Honggang Zhang, Xiaogang Wang, Xiaoou Tang
Name of Conference2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Start Date of Conference21/07/2017
End Date of Conference26/07/2017
Place of ConferenceHonolulu, Hawaii
Country/Region of ConferenceUnited States of America
Proceedings TitleProceedings: 30th IEEE Conference on Computer Vision and Pattern Recognition CVPR 2017
Year2017
Pages6450 - 6458
ISBN978-1-5386-0457-1
LanguagesEnglish-United States

Last updated on 2018-04-05 at 15:04