Transcription of Residual Attention Network for Image Classification
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Residual Attention Network for Image ClassificationFei Wang1, Mengqing Jiang2, Chen Qian1, Shuo Yang3, Cheng Li1,Honggang Zhang4, Xiaogang Wang3, Xiaoou Tang31 SenseTime Group Limited,2 Tsinghua University,3 The Chinese University of Hong Kong,4 Beijing University of Posts and Telecommunications1{wangfei, qianchen, this work, we propose Residual Attention Network ,a convolutional neural Network using Attention mechanismwhich can incorporate with state-of-art feed forward net-work architecture in an end-to-end training fashion. OurResidual Attention Network is built by stacking AttentionModules which generate Attention -aware features. Theattention-aware features from different modules changeadaptively as layers going deeper. Inside each AttentionModule, bottom-up top-down feedforward structure is usedto unfold the feedforward and feedback Attention processinto a single feedforward process.}
Residual Attention Network for Image Classification Fei Wang1, Mengqing Jiang2, Chen Qian1, Shuo Yang3, Cheng Li1, Honggang Zhang4, Xiaogang Wang3, Xiaoou Tang3 1SenseTime Group Limited, 2Tsinghua University, 3The Chinese University of Hong Kong, 4Beijing University of Posts and Telecommunications 1{wangfei, qianchen, chengli}@sensetime.com, …
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