【CVPR2023】Temporal Attention Unit: Towards Efficient Spatiotemporal Predictive Learning 这个论文研究时空预测学习(spatiotemporal predictive learning),这是一种通过学习历史视频帧来生成未来视频帧的方法。 作者认为,当前方法基本上都使用相似的框架,即编码器、解码器,然后中间使用时域模块(temproal module)进行学习。
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,对轨迹内的访问分配不同的权重,具体来说: 其中, 其中 为mask 矩阵。 Attention Matching Layer 第二个 Attention 的作用是根据用户轨迹,在候选位置中召回最合适的 POI,并计算概率。 ...
Luo, Yingtao et al. “STAN: Spatio-Temporal Attention Network for Next Location Recommendation.”Proceedings of the Web Conference 2021(2021): n. pag. 关键概念:时空双注意力模型、PIF、线性插值技术
In this paper, a novel three-stream network spatiotemporal attention enhanced features fusion network for action recognition is proposed. Firstly, features fusion stream which includes multi-level features fusion blocks, is designed to train the two streams jointly and complement the two-stream network...
注意这里是有多个mask的(每个部件一个),然后通过加到损失函数里的损失项,使得他们聚焦于不同的位置。 softmax的实现 mask乘上如图特征图:(学名叫spatial gated feature) 注意得到的map还要经过一次增强。 具体细节不清楚 Attention model 的多样性正则
Human action analytics has attracted a lot of attention for decades in computer vision. It is important to extract discriminative spatio-temporal features to model the spatial and temporal evolutions of different actions. In this paper, we propose a spatial and temporal attention model to explore th...
[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。
Spatiotemporal attention learning for video question answering (VideoQA) has always been a challenging task, where existing approaches treat the attention parts and the nonattention parts in isolation. In this work, we propose to enforce the correlation between the attention parts and the nonattention...
本文在此将基于基于图的神经网络资料阅读整理的已有内容着重强调新模型运用Attention机制在原有SRNN模型基础上做出的改进,其他基础型内容请参见链接。 Spatio-Temporal Graph 模型中的时空图有两类点和三类边 Pedestrian Node:行人点 Object Node:静态物品点 Spatial-Edge(两类):同一时刻不同点之间的连边。所有行人之...
This paper proposes a novel framework with spatiotemporal attention networks (STAN) for wind power forecasting. This model captures spatial correlations among wind farms and temporal dependencies of wind power time series. First of all, we employ a multi-head self-attention mechanism to extract ...