Channel Attention方面,大致结构还是和SE相似,不过作者提出AvgPool和MaxPool有不同的表示效果,所以作者对原来的特征在Spatial维度分别进行了AvgPool和MaxPool,然后用SE的结构提取channel attention,注意这里是参数共享的,然后将两个特征相加后做归一化,就得到了注意力矩阵。 Spatial Attention和Channel Attention类似,先在cha...
In response to the above, we propose a novel neural network pruning method based on the channel attention mechanism. In this paper, we firstly utilise the principal component analysis algorithm to reduce the influence of noisy data on feature maps. Then, we propose an improved Leaky-Squeeze-and...
(2)在上述分析的基础上,我们尝试开发一种用于深度cnn的极轻量级通道注意模块,提出了一种高效通道注意(Efficient channel attention, ECA)模型,该模型的复杂性几乎没有增加,但有明显的改进。 (3)在ImageNet-1K和MS COCO上的实验结果表明,该方法具有较低的模型复杂度,同时具有较好的性能。 Method 上图说是ECA模块的...
This paper proposed a channel-attention mechanism inspired by beamforming for speech enhancement of multichannel recordings. 多通道语音增强也在逐渐尝试用DL,根据compare the performance of our method to the following three state-of-the-art methods on CHiME-3 dataset, 这比较的方法中有传统的NMF方式,效...
论文阅读——ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks,程序员大本营,技术文章内容聚合第一站。
内容提示: ECA-Net: Eff i cient Channel Attention for Deep Convolutional Neural NetworksQilong Wang 1 , Banggu Wu 1 , Pengfei Zhu 1 , Peihua Li 2 , Wangmeng Zuo 3 , Qinghua Hu 1,∗1Tianjin Key Lab of Machine Learning, College of Intelligence and Computing, Tianjin University, China2...
Channel Attention Based on the intuition described in the previous section, let’s go in-depth into why channel attention is a crucial component for improving generalization capabilities of a deep convolutional neural network architecture. To recap, in a convolutional neural network, there are two ma...
In this paper, a convolutional neural network (CNN) based framework is proposed for protein-DNA binding residues prediction, named Se-Residual-Inception network, which includes stacked Inception blocks with short-cut connection and channel-attention mechanism. We examine the performance of proposed Se-...
attention mechanism. This mechanism enables the network to capture deep spatio-temporal characteristics in a hierarchical manner and distinguish between different human movements in everyday life. Our investigations, using the UCI-HAR, WISDM, and IM-WSHA datasets, demonstrated that our proposed model,...
5. **ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks**（ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks）- **简介**：ECA-Net提出了一个更加高效的通道注意力机制，通过使用一个感受野为k的1x1卷积，替代了SE结构中的两个全连接层，减少了...