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


其它資訊
摘要In 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.
出版社接受日期21.07.2017
著者Fei Wang, Mengqing Jiang, Chen Qian, Shuo Yang, Cheng Li, Honggang Zhang, Xiaogang Wang, Xiaoou Tang
會議名稱2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
會議開始日21.07.2017
會議完結日26.07.2017
會議地點Honolulu, Hawaii
會議國家/地區美國
會議論文集題名Proceedings: 30th IEEE Conference on Computer Vision and Pattern Recognition CVPR 2017
出版年份2017
頁次6450 - 6458
國際標準書號978-1-5386-0457-1
語言美式英語

上次更新時間 2018-04-05 於 15:04