AttentionIn 2021, the COVID-19 is still widespread around the world, whichhas a great impact on people's daily lives. However, there is still a lack of researchon the fast segmentation of lung infections caused by COVID-19. The segmentationof the COVID-19- infected region from the lung...
文中提出RADC-Net(residual attention based dense connected convolutional neural network),网络中由三种结构组成,密集连接结构(dense connection structure)、残差注意力块(residual attention block)、增强分类层(enchanced classification layer)。密集连接结构能够提取明显的特征,残差注意力快可以增强局部语义信息,增强分类...
首先应用了一个来自于Transformer-XL的multi-headed self-attention (MHSA),具体来说是一个相对正弦位置编码(relative sinusoidal positional encoding),它能让self-attention模块在变长输入上有更好表现,所得到的编码器对语音长度的变化具有更强的鲁棒性。 图8 Multi-Headed self-attention模块 Convolution Module。对于...
由于这种模型好用,因此很抢手,许多炼丹者们对它进行了五花八门的改造,玩出了各种花样,小有名气的有使用分组卷积(Group Convolution)的 ResNeXt 以及加入了空间注意力(Channel Attention)机制的 SE-ResNet 和 SE-ResNeXt。 另外还有加入混合注意力(Spacial & Channel Attention)机制的 Residual Attention Net 等一批想...
由于这种模型好用,因此很抢手,许多炼丹者们对它进行了五花八门的改造,玩出了各种花样,小有名气的有使用分组卷积(Group Convolution)的 ResNeXt 以及加入了空间注意力(Channel Attention)机制的 SE-ResNet 和 SE-ResNeXt。 另外还有加入混合注意力(Spacial & ...
Relation to Grouped Convolutions. 上面的模块使用分组卷积[24]表示法变得更加简洁。图3(c)说明了这种重新表述。所有的低维嵌入(1×1层)都可以被一个更宽的单层取代(如图3(c)中的1×1,128-d)。当分组卷积层将输入通道分成组时,splitting本质上是由分组卷积层完成的。图3(c)中的分组卷积层执行32组卷积,其...
Seizure Detection Based on Lightweight Inverted Residual Attention Network Timely and accurately seizure detection is of great importance for the diagnosis and treatment of epilepsy patients. Existing seizure detection models are ... H Lv,Y Zhang,T Xiao,... - 《International Journal of Neural System...
conformer模块包含以下几个部分:(1)Feedforward module;(2)Multi-head self attention Module;(3)Convolution Module。其中两个Feedforward输出都乘以了1/2。 Multi-head self attention Module。首先应用了一个来自于Transformer-XL的multi-headed self-attention (MHSA),具体来说是一个相对正弦位置编码(relative sinuso...
In contrast, we formulate light field super-resolution (LFSR) as tensor restoration and develop a learning framework based on a two-stage restoration with 4-dimensional (4D) convolution. This allows our model to learn the features capturing the geometry information encoded in multiple adjacent views...
CNN usually uses a non-linear activation function to extract the non-linear features of images, which naturally arouses people’s attention (Yu et al., 2020). Hu et al. (2015) used the CNN first in HSI classification with only a one-dimensional (1D) convolution kernel and a focus on ...