## 4 Experimental Results
### Ablation Studies  
We conducted a series of ablation studies on the Charades-STA dataset to verify the effectiveness of the main components of the proposed method. **Effectiveness of dynamic Gaussian mask:** In Section 3.3, we applied a dynamic Gaussian mask in Equation (5) to rectify the alignment score vector. By this means, dynamic attention values were allocated to different positions of the video segment according to the training state of the framework. To verify the effectiveness of this strategy, we trained a comparison network that eliminated the dynamic Gaussian mask in Equation (5). Comparing models (1) and (5) in Table 3, we observe a severe degradation in performance. This observation confirms the assumption that it is beneficial to focus on the center of the window to learn content information at the beginning of the training stage. As the training progresses, it becomes advantageous to allocate higher attention around the boundaries of the window. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**R@1**} \\ \cline{2-5}  & **IoU = 0.3** & **IoU = 0.5** & **IoU = 0.7** & **Runtime/s** \\ \hline RTBPN [40] & 60.04 & 32.36 & 13.24 & **0.1** s \\ BAR [49] & 44.97 & 27.04 & 12.23 & - \\ WSTAN [15] & 51.61 & 21.64 & 9.01 & 2.1 s \\ FSAN [36] & 55.11 & 29.43 & - & - \\ M2D [16] & - & 30.38 & 17.31 & - \\ CPL [39] & **66.40** & 49.24 & 22.39 & 0.2 s \\ \hline Ours (C3D) & 53.77 & 28.19 & 12.15 & 0.9 s \\ Ours & 66.14 & **51.41** & **23.42** & 1.2 s \\ \hline \hline \end{tabular} Bold mark represent the optimal performance across various methods evaluated under same metric.  
\end{table} 
Table 1: _Cont._ \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**R@1**} \\ \cline{2-5}  & **IoU = 0.1** & **IoU = 0.3** & **IoU = 0.5** & **Runtime/s** \\ \hline SCN [11] & 71.48 & 47.23 & 29.22 & - \\ CCL [41] & - & 50.12 & 31.07 & - \\ RTBPN [40] & 73.73 & 49.77 & 29.63 & **0.1** s \\ BAR [49] & - & 49.03 & 30.73 & - \\ WSTAN [15] & 79.78 & 52.45 & 30.01 & 2.1 s \\ FSAN [36] & 78.45 & 55.11 & 29.43 & - \\ M2D [16] & - & 49.79 & 29.68 & - \\ CPL [39] & 82.55 & 55.73 & 31.37 & 0.2 s \\ \hline Ours (C3D) & 77.19 & 52.36 & 30.12 & 0.9 s \\ Ours & **83.77** & **57.29** & **31.84** & 1.2 s \\ \hline \hline \end{tabular} Bold mark represent the optimal performance across various methods evaluated under same metric.  
\end{table} 
Table 2: Evaluation results on the ActivityNet dataset. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**R@1**} \\ \cline{2-5}  & & **IoU = 0.3** & **IoU = 0.5** & **IoU = 0.7** \\ \hline (1) & w/o dynamic Gaussian mask & 63.76 & 34.11 & 14.76 \\ (2) & w/o guidance loss & 58.54 & 27.55 & 11.08 \\ (3) & w/o reconstruction-based alignment & 64.23 & 43.31 & 16.08 \\ (4) & w/o inner alignment loss & 60.27 & 33.33 & 14.06 \\ (5) & Ours & **66.14** & **51.41** & **23.42** \\ \hline \hline \end{tabular} Bold mark represent the optimal performance across various methods evaluated under same metric.  
\end{table} 
Table 3: Ablation studies of main components on the Charades-STA dataset. **Effectiveness of guidance loss:** In Section 3.3, we proposed a guidance constraint to encourage the middle part of the video clip to have higher alignment scores. In model (2) of Table 3, we removed the guidance loss. It can be seen that the network without this constraint achieved the worst performance, confirming its necessity. **Effectiveness of inner alignment loss:** In Equation (19), we defined an inner alignment loss to encourage allocating higher alignment scores to the center part of the video compared to the left part, right part, and the overall video. In model (4) in Table 3, we removed the inner alignment loss \(\mathcal{L}_{inner}\) in Equation (20).