Transcription of SA-NET: SHUFFLE ATTENTION FOR DEEP CONVOLUTIONAL ...
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SA-NET: SHUFFLE ATTENTION FOR DEEP CONVOLUTIONAL ...
SA-NET: SHUFFLE ATTENTION FOR DEEP CONVOLUTIONAL NEURAL NETWORKSQing-Long Zhang, Yu-Bin Yang State Key Laboratory for Novel Software Technology at Nanjing UniversityABSTRACTA ttention mechanisms, which enable a neural network to ac-curately focus on all the relevant elements of the input, havebecome an essential component to improve the performance ofdeep neural networks. There are mainly two ATTENTION mecha-nisms widely used in computer vision studies,spatial attentionandchannel ATTENTION , which aim to capture the pixel-levelpairwise relationship and channel dependency, fusing them together may achieve better performancethan their individual implementations, it will inevitably in-crease the computational overhead.
classification, object detection, and instance segmentation. There are mainly two types of attention mechanisms most commonly used in computer vision: channel attention and spa- ... branch networks, in which one branch is the identity mapping. SKNets [2] and ShuffleNet families [13] both followed the
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