论文《STAS: Spatial-Temporal Return Decomposition for Multi-agent Reinforcement Learning》来自 Arxiv 2024。这篇论文讨论情景多智能体强化学习(Episodic Multi-agent Reinforcement Learning)中的信用分配问题。情景强化学习是指只有当智能体序列终止时才能获得非零奖励,也就是奖励稀疏场景。因此信用分配问题就需要考虑,...
Temporal attention, that is, the process of anticipating the occurrence of a stimulus at a given time point, has been shown to improve perceptual processing of visual stimuli. In the present study, we investigated whether and how temporal attention interacts with spatial attention and feature-based...
Spatial-Temporal Attention Model 时空注意力模型如下图所示 输入视频经过基础网络的feature maps 为 fn{n=1:N} , 然后经过l2正则化处理得到对应的attention map g_n . g_n(h,w)=\dfrac{||\sum_{d=1}^{d=D}f_n(h,w,d)^2||_2}{\sum_{h,w}^{H,W}||\sum_{d=1}^{d=D}f_n(h,w,...
Introduction 本文主要提出了高效且容易实现的STA框架(Spatial-Temporal Attention)来解决大规模video Reid问题。框架中融合了一些创新元素:帧选取、判别力局部挖掘、不带参特征融合、视频内正则化项。 Proposed Method (1)总体思路: 先通过骨干网络提取特征映射,再将特征映射通过STA框架生成2D的注意力得分矩阵。为了降低视...
3.3.3 Temporal attention Different from spatial attention considering a single frame, temporal attention is used to compute the relevance of all locations in a sequence. As shown in Fig. 3.7, each frame in the sequence is concatenated and denoted as FS(n), and then FS(n) is turned into ...
ASTGCN 4.1 整体框架 4.2 Spatial-Temporal Attention 4.3 Spatial-Temporal Convolution 4.3.1 Graph convolution in spatial dimension ...Age Progression and Regression with Spatial Attention Modules(AAAI20) Method Problem Formulation 定义young face image为Iy\mathbf{I}_yIy,对应的age为αy\bm{\alpha}...
Spatial-temporal Attention Net 对于时间切片 对于空间切片 使用注意力机制,X是3维特征矩阵,对于每一个交易记录 使用时间注意力机制,更新时间切片,在此基础上在使用空间注意力机制 3D Convolutional Layers W是要训练的权重 Experiments 数据集 从一家大型商业银行收集了欺诈交易,其中包括2016年1月1日至12月31日的十...
Temporal Latent Auto-Encoder: A Method for Probabilistic Multivariate Time Series Forecasting(AAAI 2021)设计了一种端到端的学习方法。原始的矩阵分解可以表示为Y=FX,进而可以表示为Y=FF'Y,该方法将原来的矩阵分解部分改为AutoEncoder,即通过Encoder隐式得到base序列,再通过Decoder还原原始序列。对于第二项时序约束...
official implementation of the spatial-temporal attention neural network (STANet) for remote sensing image change detection - justchenhao/STANet
1.模型结构 1.如何构建图,并基于self-attention学习node feature和edge feature 2.spatial mask & temporal mask 总结 贡献: 构建全连接图 spatial-temporal position embedding node features 和edges通过spatial and temporal domain的self-attention mechanism 学习 使用spatial-temporal mask,降低了99%的复杂度。