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
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,...
论文笔记《Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting》 这里写目录标题 1. Abstract 2. Introduction 3. Preliminaries 3.1 交通网络 3.2 交通流预测 4. ASTGCN 4.1 整体框架 4.2 Spatial-Temporal Attention 4.3 Spatial-Temporal Convolution 4.3.1 Graph convolution in...
STA: Spatial-Temporal Attention for Large-Scale Video-based Person Re-Identification,程序员大本营,技术文章内容聚合第一站。
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
7. ST-ABC: Spatio-Temporal Attention-Based Convolutional Network for Multi-Scale Lane-Level Traffic Prediction 作者:Shuhao Li (Fudan University); Yue Cui (The Hong Kong University of Science and Technology); Libin Li (Guangzhou University); Weidong Yang (Fudan University); Fan Zhang (Guangzhou ...
To represent the evolving maritime situation, we establish an Adaptive Graph Spatial-Temporal Attention Network (AGSTAN). In this respect, we develop a dynamic spatial graph module that enable Graph Attention Network (GAT) model to learn adaptive spatial interactions between different latitude-longitude...
进一步开发了基于动态VGAE的框架,该框架生成用于空间可解释性的因果-动态掩膜,并通过因果发明沿时间轴识别动态关系,以实现时间可解释性。在合成和真实世界数据集上进行的全面实验表明,该方法取得了实质性进展,展现了显著的优越性。 DyGNNExplainer与SCM 3. TESTAM: A Time-Enhanced Spatio-Temporal Attention Model ...
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。
temporal dependencies inherent in spike-based processing, leading to suboptimal feature representation and limited performance. To address this limitation, we propose Spiking Transformer with Spatial-Temporal Attention (STAtten), a simple and straightforward architecture that efficiently integrates both spatial...