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。 ...
2016. Spatio- Temporal Attention Models for Grounded Video Captioning. In ACCV.Zanfir M, Marinoiu E, Sminchisescu C. Spatio-Temporal Attention Models for Grounded Video Captioning. In: Lai SH, Lepetit V, Nishino K, Sato Y, eds. Computer Vision - ACCV 2016. Cham, Switzerland: Springer ...
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
为了并行化时域模块,作者提出了时间注意力单元(Temporal Attention Unit, TAU),它将时间注意力分解为帧内静态注意力和帧间动态注意力。该方法的框架如下图所示,包括编码器,TAU,解码器三部分。 TAU 使用注意力机制来并行化的处理时间演变,该模块将时空注意力分解为:帧内静态注意力和帧间动态注意力。帧内静态注意力...
Temporal attentionManifold spaceRecently, skeleton-based action recognition has become increasingly prevalent in computer vision due to its wide range of applications, and many approaches have been proposed to address this task. Among these methods, manifold space is widely used to deal with the ...
[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。
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
2. **multi-node vs multi-head**:相较于multi-head attention,模型提出的注意力模型没有使用scale dot-product操作,而是用累加和平均的方式,也就没有大幅压缩向量维度,保留信息更充分。 标签: lstm, attention mechanism, spatio-temporal graph, autonomous vehicle 好文要顶 关注我 收藏该文 微信分享 youzn...
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...
To address this limitation, this paper proposes a spatiotemporal self-attention mechanism-based LSTNet, which is a multivariate time series forecasting model. The proposed model leverages two self-attention strategies, spatial and temporal self-attention, to focus on the most relevant information among...