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,...
为了减小背景干扰,关注局部的信息区域,采用了Relation-Guided Spatial Attention Module(RGSA),由特征和相关性向量来决定关注的区域; 为提取视频级特征,采用了Relation-Guided Temporal Refinement Module(RGTR),通过帧之间的关系信息融合为视频特征。 Method (1)框架概述: 假定输入的视频片段为 ,采用CNN提取得到单帧的...
贡献: 0.MultiHeadedAttention recap 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-temp...
框架: Spatial-Temporal Attention 它包含两种注意,即空间注意和时间注意 1)Spatial attention 2)Temporal attention 在时间维度上,不同时间片上的交通状况之间存在相关性,且在不同情况下其相关性也不同。 Spatial-Temporal Convolution 时空关注模块让网络自动对有价值的信息给予相对更多的关注。本文提出的时空卷积模块包...
In this paper, we propose a model based on weighted spatial-temporal attention for action recognition. This model selects the key parts in each video frame and important frames in each video sequence. Then the model focuses on analyzing these key parts and frames. Therefore, the most important...
Introduction 在视频序列中,有些帧由于被严重遮挡,需要被尽可能的“忽略”掉,因此本文提出了时间注意力模型(temporal attention model,TAM),注重于更有相关性的帧。 常规的矩阵学习通常用特征的距离来进行计算,但忽视了帧之间的差异,上图可以看出,本文的方法考虑
Recently, researchers have generally adopted deep networks to capture the static and motion information separately, which has two main limitations. First, the coexistence relationship between spatial and temporal attention is ignored, although they should be jointly modeled as the spatial and temporal ...
A spatial cueing paradigm was used to (a) investigate the effects of attentional orienting on spatial and temporal parameters of saccadic eye movements and (b) examine hypotheses regarding the hierarchical programming of saccade direction and amplitude. On a given trial, the subjects were presented ...
a spatial-temporal attention-based convolutional network (STACN) that can leverage the advantages of an attention mechanism, a convolutional neural network and long short-term memory to extract text and numerical information for stock price prediction. Benefiting from the utilisation of an attention ...
所谓的spatial attention和temporal attention,其实都是常规的attention机制,用LSTM的隐状态作为query,来对Value特征集做加权求和。区别在于两者Value特征集不一样。前者是在每一帧检测出来的不同objects之间做attention。后者是在从不同帧各自得到的全局特征之间做attention。