STA:Spatial-Temporal Attention for Large-Scale Video-based Person Re-Identification(AAAI2019) 注意力机制对于视频行人重识别的研究越来越得到很多人的关注,同时因为时序特征也是非常重要的一部分,很多方法开始考虑两部分的结合。但是本文采用一个序列中随机选择4张图片就表示利用了时序信息还是有待商榷,感觉更像是基...
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,...
environment deep-learning attention-mechanism spatio-temporal-prediction Updated Apr 30, 2019 Python NickHan-cs / Spatio-Temporal-Data-Mining-Survey Star 108 Code Issues Pull requests Paper & Code & Dataset Collection of Spatial-Temporal Data Mining. map-matching trajectory-analysis trajectory spati...
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...
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括空间维度上的图卷积,从邻近时间捕获空间相关性,以及沿时间维度上的卷积...
# Multiple Spatial Attention atn = atn.view(atn.size(0), self.spanum, 1, self.atn_height, self.atn_width) atn = atn.expand(atn.size(0), self.spanum, 2048, self.atn_height, self.atn_width) x = x.view(x.size(0), 1, 2048, self.atn_height, self.atn_width) x = x.exp...
(i) predictions and error corrections for current sensory states with internal representations of the environment, and (ii) shifting perspectives and mental reference frames, both of which support endogenous attention and likely facilitate task-related representational search56. The clear involvement of ...
Plant hairs, also known as trichomes, cover the surface of most terrestrial plants, and are often used as one of the key traits in plant taxonomy. In nature, trichomes exhibit an enormous diversity of morphology and size in different species. Trichomes can be unicellular, such asArabidopsistrich...
8. 损失函数 使用了常规的Huber loss。 9. 思考 本文使用的DTW算法应该还可以使用其他的计算时间序列相似的算法,比如shapelets等,并且这里的邻接矩阵中都是1,那么默认了节点的重要程度是一致的,其实也可以参考attention的方法,对节点的重要程度进行加权。
然后,我们让\textbf{F}_{sq}在时间维度上作self-attention,具体为: \textbf{F}_{sq}=\textbf{F}_{sq}+Module_{att}\left(\textbf{F}_{sq}\right)\\ 其中Module_{att}一个L_t层多头注意力模块。然后,通过残差前馈网络对获取的特征进行逐点精炼,得到长期时间特征\textbf{F}_{LT}{\in\...