text: 88,4,1400827497646751747,1400815831018213376,Meng-Hao Guo,Our new paper uses two linear layers to replace self-attention and builds an all-MLP architecture name EAMLP. EAMLP achieved 79.4% accuracy on ImageNet and surpassed Performer in some settings! Results and visualizations are as follows.  ArXiv: <LINK>. <LINK>,https://arxiv.org/abs/2105.02358,"Attention mechanisms, especially self-attention, have played an increasingly important role in deep feature representation for visual tasks. Self-attention updates the feature at each position by computing a weighted sum of features using pair-wise affinities across all positions to capture the long-range dependency within a single sample. However, self-attention has quadratic complexity and ignores potential correlation between different samples. This paper proposes a novel attention mechanism which we call external attention, based on two external, small, learnable, shared memories, which can be implemented easily by simply using two cascaded linear layers and two normalization layers; it conveniently replaces self-attention in existing popular architectures. External attention has linear complexity and implicitly considers the correlations between all data samples. We further incorporate the multi-head mechanism into external attention to provide an all-MLP architecture, external attention MLP (EAMLP), for image classification. Extensive experiments on image classification, object detection, semantic segmentation, instance segmentation, image generation, and point cloud analysis reveal that our method provides results comparable or superior to the self-attention mechanism and some of its variants, with much lower computational and memory costs. ","Beyond Self-attention: External Attention using Two Linear Layers for