# Topology-Embedded Temporal Attention for Fine-Grained Skeleton-Based Action Recognition
## 4 Experiments
### Ablation Study  
In this section, we explore the influences of different settings in the proposed TE-TAM module on the performance through experiments. Note: all experiments in this section were performed on \(TrainSet_{1}\) with CTRGCN [5] + TE-TAM. We randomly and evenly divided 15% of the sample data of all categories in \(TrainSet_{1}\) into the test set (named \(TestSet_{3}\)), and the rest into the training set (\(TrainSet_{2}\)); all of the models were trained on \(TrainSet_{2}\) and tested on \(TestSet_{3}\); they share the same training settings as mentioned in Section 4.2. In addition, the baseline is the result of CTRGCN. As shown in Table 1, if we number the ten blocks in the model from one to nine: 0123456789, then, the insertion positions of TE-TAM in the ABCDEFH experiments are: 01, 09, 0123, 0189, 012345, 012789, and 479. We conducted relevant experimental explorations on the effectiveness of the TE-TAM module. By comparing the experimental results from inserting different numbers of TE-TAM modules and inserting the TE-TAM in different positions, we proved the effectiveness of the TE-TAM and drew the following conclusions: (1) only when the TE-TAM is placed in an appropriate position can it play an effective role; (2) when the temporal dimensions of the input features decrease, the effect of the TE-TAM will weaken, accordingly. Specifically, by comparing A and B, we change the position of the A module, and the accuracy of experiment B improves compared with that of experiment A. Meanwhile, comparing their results with that of experiment H, we find that the position of the TE-TAM has a relatively great influence on the performance. Through the comparisons of E and F, and E and G, combined with the experimental results of A, B, C, and D, it can be concluded that when the temporal dimensions of the feature are low, the effects of TE-TAM will be reduced. Through the horizontal and vertical comparative analyses of the experimental results as shown in Table 2, it can be found that: (1) only modeling the topological relations of the inner skeleton data in the single frame can have limited attention, which also conforms to the fact that the action features of adjacent and similar frames are similar; (2) it is effective to model the topology relationships of local bone points adjacent to the temporal and spatial dimensions, simultaneously. In addition, the effect is most obvious when \(K_{M}=(3,3)\). We explored the influences of the different settings of the embedding functions, activation functions, and parameter \(r\) on the results. As shown in Table 3, I and J show that different embedding functions have a great impact on the results, which also indirectly illustrates the importance of topology embedding. Experiments K, L, M, N, O, and P illustrate that the different configurations of the MLP can affect the conversion of the excitation \begin{table}
\begin{tabular}{c c} \hline \hline
**Methods** & **Top-1 Acc (\%)** \\ \hline
**Baseline** & **59.8** \\ \hline +2 _TE-TAM blocks_\(A\) & 56.6\({}^{\text{1,3,2}}\) \\ +2 _TE-TAM blocks_\({}^{\text{1}}\)\(B\) & 58.9\({}^{\text{1,0,9}}\) \\ +4 _TE-TAM blocks_\({}^{\text{C}}\) & 59.6\({}^{\text{1,0,2}}\) \\ +4 _TE-TAM blocks_\({}^{\text{1}}\)\(D\) & 58.9\({}^{\text{1,0,9}}\) \\ +6 _TE-TAM blocks_\({}^{\text{2}}\)\(E\) & 60.7\({}^{\text{1,0,9}}\) \\ +6 _TE-TAM blocks_\({}^{\text{1}}\)\(F\) & **61.4\({}^{\text{1,1,6}}\)** \\ +10 _TE-TAM blocks_\({}^{\text{G}}\) & **61.4\({}^{\text{1,1,6}}\)** \\ \hline \hline \multicolumn{2}{l}{\(+3\)_TE-TAM blocks_\({}^{\text{2}}\)\(H\) & **60.2\({}^{\text{1,0,4}}\)**} \\ \hline \hline \end{tabular} \({}^{\text{\emph{blocks}}}\): TE-TAMs are stacked from top to bottom. _blocks_\({}^{\text{1}}\): TE-TAMs are inserted from both sides of the model. _blocks_\({}^{\text{2}}\): TE-TAMs are inserted in special places. Note: Other parameters are set: \(K_{M}=(1,1)\), \(\theta=\theta_{1}\), \(r=2\), \(\sigma=\emph{sigmoid}\) and \(\sigma_{1}=\emph{relu}\).  
\end{table}