STA-LSTM
Spatio-Temporal Attention LSTM
In human action recognition, 
each type of action  generally only depends 
on a few specific kinematic joints. Furthermore, over time, multiple actions may be performed.
Motivated by these observations, Song et al. proposed 
a joint spatial and temporal attention network based on LSTM, to adaptively find discriminative features and keyframes. 
Its main attention-related components are a spatial attention sub-network, to select important regions, and a temporal attention sub-network, to select key frames. The spatial attention sub-network can be written as:
\begin{align}
    s_{t} &= U_{s}\tanh(W_{xs}X_{t} + W_{hs}h_{t-1}^{s} + b_{si}) + b_{so}
\end{align}
\begin{align}
    \alpha_{t} &= \text{Softmax}(s_{t})
\end{align}
\begin{align}
    Y_{t} &= \alpha_{t}  X_{t} 
\end{align}
where $X_{t}$ is the input feature at time $t$, $U_{s}$, $W_{hs}$, $b_{si}$, and $b_{so}$ are learnable parameters, and $h_{t-1}^{s}$ is the hidden state at step $t-1$. Note that use of the hidden state $h$ means  the attention process takes  temporal relationships into consideration.

The temporal attention sub-network is similar to the spatial branch and produces its attention map using:
\begin{align}
    \beta_{t} = \delta(W_{xp}X_{t} + W_{hp}h_{t-1}^{p} + b_{p}). 
\end{align}
It adopts a ReLU function instead of a normalization function for ease of optimization. It also uses a regularized objective function to improve  convergence.

Overall, this paper presents a joint spatiotemporal attention method
to focus on important joints and keyframes, 
with excellent results on the action recognition task.
An End-to-End Spatio-Temporal Attention Model for Human Action Recognition from Skeleton Data
https://paperswithcode.com/paper/an-end-to-end-spatio-temporal-attention-model
An End-to-End Spatio-Temporal Attention Model for Human Action Recognition from Skeleton Data