self-attention is widely applied in language inference tasks. Motivated by these observations, we propose a self-attention traffic matrix prediction (SATMP) model for long-term network TM prediction in IIoT scenarios. SATMP consists of three components: (a) a spatial–temporal encoding for obtaini...
Self-Attention Memory Module 作者对Self-Attention基础模型加以改进,以捕捉时域和空域上的全局特征依赖,提出了Self-Attention Memory(SAM)模块,结构如上图所示。SAM模块接受两个输入:当前时间步的输入特征H_t和上个时间步的记忆单元M_{t-1},结构可分为三部分:用以获取全局上下文信息的特征聚合(Feature Aggregation)...
self-attention没有捕获target ad跟辅助广告之间的关系,也就是每个辅助广告序列内部算出来的权重是跟target ad无关的。还有个问题就是每个辅助广告序列权重是内部做归一化的,导致就算一个辅助广告序列中广告都跟target ad不相关,但是因为做了归一化,权重还是会很大的。 接下来论文提出了Interactive Attention,其实就是...
Although, they have proven their great potential in this field, they fail to model long range temporal information in very long video sequences. We have hence thought of using Transformer networks to propose a new pose-guided self-attention mechanism combined to 3D convolutional neural networks (...
Spatio-Temporal Embedding Layer 以每小时和每一百米作为基本单位,对时空关系矩阵进行嵌入,映射到一个欧氏空间。 此外,论文也提出了一种插值嵌入的方法: 经过求和得到最终的嵌入: Self-Attention Aggregation Layer 首先是第一个 Attention,主要用用来考虑轨迹中有不同距离和时间间隔的两次 check-in 的关联程度,对轨迹...
simultaneously from spatial and temporal cues have been shown crucial for video processing, with the shortage of temporal information of soft assignment, the vector of locally aggregated descriptor (VLAD) should be considered as a suboptimal framework for learning the spatio-temporal video representation...
[骨架动作识别]STA-LSTM: Spatio-Temporal Attention Model for Human Action Recognition from Skeleton Data,程序员大本营,技术文章内容聚合第一站。
temporal deformable attention network (STDANet) for video delurring, which extracts the information of sharp pixels by considering the pixel-wise blur levels of the video frames. Specifically, STDANet is an encoder-decoder network combined with the motion estimator and spatio-temporal deformable ...
Diversity Regularized Spatiotemporal Attention for Video-based Person Re-identification 主旨:用空间注意力和时间注意力来解决遮挡和不对齐问题 自动地发现不同部位的注意力(即训练多个注意力模块) 不会受遮挡和不对齐影响(这里是因为时间上的注意力模块会自动择优(权值大的))...
To extract spatial features with both global and local dependencies, we introduce the self-attention mechanism into ConvLSTM. Specifically, a novel self-attention memory (SAM) is proposed to memorize features with long-range dependencies in terms of spatial and temporal domains. Based on the self-...