Convolutional neural networkMulti-scale attention mechanismWith the rapid increase of data availability, time series classification (TSC) has arisen in a wide range of fields and drawn great attention of researchers. Recently, hundreds of TSC approaches have been developed, which can be classified ...
Therefore, we investigate a novel end-to-end model based on deep learning named as Multi-scale Attention Convolutional Neural Network (MACNN) to solve the TSC problem. We first apply the multi-scale convolution to capture different scales of information along the time axis by generating different...
提出了 Mss-AGCN,一种能够高效学习骨架图局部和全局特征的多尺度采样注意力图卷积网络,能够在多个骨架动作识别任务中达到最先进的性能。提出了局部优先采样(LFS)和全局优先采样(GFS)策略,用于构建骨架的多尺度图窗口,通过引入特定的骨架归纳偏置,降低了自注意力模型的计算复杂度。通过将自注意力机制与图卷积相结合,保...
Transformer最近在CV领域展现出了不错的效果,Vision Transformer(ViT)的大致流程可分为两步: 1)因为Self-Attention(SA)的计算复杂度是和输入特征的大小呈平方关系的,所以如果直接将224x224的图片输入到Transformer中,会导致计算量的“爆炸”。因此,ViT的第一步是将图片转换成更小的token(比如16x16),然后将这些token...
这篇文章介绍了一种名为Hybrid Convolutional and Attention Network (HCANet)的模型,用于高光谱图像去噪。该模型结合了卷积神经网络和Transformer的优势,以有效地去除高光谱图像中的噪声。文章提出了注意力机制,用于捕获远程依赖性和邻域光谱相关性,以增强全局和局部特征建模。通过设计卷积和注意力融合模块以及多尺度前馈网...
CA-MCNN is a multi-scale convolutional neural network with the combination of pooling layers, efficient channel attention block and parallel feature fusion mechanism. We use the bearing dataset to find suitable pooling parameters and mini-batch size for the model, and verify the effectiveness of CA...
model was ultimately obtained by combining the idea of large convolutional kernels with the attention fusion module. The detailed process of image segmentation using the MSFANet model is illustrated in Fig.3. Firstly, ResNet-50 was used as the backbone network, and the ResNet layer was adjusted...
Structured Attention Guided Convolutional Neural Fields for Monocular Depth Estimation Recent works have shown the benefit of integrating Conditional Random Fields (CRFs) models into deep architectures for improving pixel-level prediction tasks. Following this line of research, in this paper we introduce ...
此外,作者实际上还提出了一个卷积前馈网络MixCFN (Mixed-scale Convolutional Feedforward Network),但没有说很清楚到底是到底用在哪个位置的 (T ^ T)。可能是在每个 Attention 之前或之后再提取一下多尺度特征?总之模块本身不难理解,看图就行了,是分通道经过两个核大小不同的 DWConv: ...
②Mixed-scale convolutional feedforward network. 受MiT中MixFFN和HR-NAS中multibranch inverted residual blocks 的启发,作者通过在2个线性层之间插入2条多尺度深度卷积路径,设计了一种混合尺度卷积FFN(MixCFN)。 在LayerNorm之后,将通道以r的比例展开,然后将其分成2个分支。利用3×3和5×5深度卷积(DWConv)增强HR...