2.attention-block的实现方式:attention-block通常是由多个注意力机制组成的模块,其核心思想是将多个注意力机制的输出进行融合,从而得到更加全面的关注值。在实现过程中,通常会使用一种称为自注意力(self-attention)和门控循环(gatedrecurrentunit,GRU)的机制。自注意力机制能够根据输入序列的特征,计算出每个位置的关注值...
In this study, we propose the development of UNet for brain tumor image segmentation by modifying its contraction and expansion block by adding Attention, adding multiple atrous convolutions, and adding a residual pathway that we call Multiple Atrous convolutions Attention Block (MAAB). The ...
Attention blocks have been used to modify skip connections on the UNet architecture and result in improved performance. In this study, we propose the development of UNet for brain tumor image segmentation by modifying its contraction and expansion block by adding Attention, adding multiple atrous ...
Next, the identified video key frames with the lowest MS-SSIM are forwarded to 3D CNN to extract spatiotemporal features. Furthermore, we exploit the Convolutional Block Attention Module (CBAM) to increase the representational capabilities of the 3D CNN. The results on different benchmark datasets...
In this 3D-CNN (hereafter, FogNet), two parallel branches of feature extraction have been designed, one for spatially auto-correlated features (spatial-wise dense block and attention module), and the other for correlation between input variables (variable-wise dense block and attention mechanism.)...