The novel attention module named spatio-temporal-channel (STC) attention is designed to jointly learn weights in spatial, temporal, and channel dimensions in a more efficient way. Extensive experiments have been conducted on two uncompressed datasets and one compressed dataset. Results show that AST...
参考SENet(Squeeze-and-Excitation, attention相关论文的一个网络),第n层在第t个时间步长的输入张量为X^{t,n-1}\in R^{L \times B \times C}(L和B是图像的height和width,C是channel size),squeeze步骤计算出一个统计向量s^{n-1}\in R^{T},其中在t时间步长的值为: 在excitation步骤中,S^{n-1}通...
先使用通道注意力再使用空间注意力(R2plus1D + channel + spatial)的准确率高于先使用空间注意力再使用通道注意力的组合(R2plus1D + spatial + channel)。 同时使用通道和空间注意力模块(R2plus1D + channel & spatial in parallel)得到的准确率与先使用通道后使用空间的组合相当。 仅使用平均池化(avg)的模型在 ...
[TNNLS 2024] Implementation of "TCJA-SNN: Temporal-Channel Joint Attention for Spiking Neural Networks" - ridgerchu/TCJA
For example, SE-TCN, which combines a temporal convolutional network with a channel attention mechanism. This method is primarily used to address the issue of the unequal importance of each feature across multivariate time series. SE-TCNeXt, which uses depth-wise convolution to reduce the number ...
Secondly, we model the channel features obtained by spatial context to enhance the ability to extract useful spatial semantic features at different levels. Thirdly, a temporal attention module which can model the temporal information makes the extracted temporal features more representative. A large ...
In this work, we propose a novel Spatial-Temporal Attention (STA) approach to tackle the large-scale person re-identification task in videos. Different from the most existing methods, which simply compute representations of video clips using frame-level aggregation (e.g. average pooling), the pr...
To better fuse the information of the two temporal-recurrent propagation units, we use channel attention mechanisms. Additionally, we recommend a progressive up-sampling method instead of one-step up-sampling. We find that progressive up-sampling gets better experimental results than one-stage up-...
To improve the accuracy of human activity recognition (HAR) based on body area network (BAN), a novel spatio-temporal network combining multi-channel convolutional neural network (CNN) with graph convolutional neural network (GCN) is proposed in this paper. Based on BAN including multi-sensors, ...
However, these methods primarily fasten more attention on its spatial domain information, and the dynamics in temporal domain are attached less significance. Consequently, this might lead to the performance bottleneck, and scores of training techniques shall be additionally required. Another underlying ...