## 6 Ablation Experiments
### Effectiveness of WSA Module  
By comparing the first and second rows in Table 4, we found it beneficial to replace the linear projection approach of the traditional Transformer model with a 1D depthwise convolution to project the queries, keys, and values. The results showed that when using the 1D depthwise convolution, the mAP at tIoU 0.5 and the average mAP increased by 0.9% and 0.5%, respectively. This indicates that choosing an appropriate projection approach can help improve the performance of the TAL task. Observed in the third row, we evaluated the effect of the choice of the window size on the results. We can find that increasing the window size from 4 to 9 had an improvement (from 65.7% to 67.2% on [EMAIL_ADDRESS]), while the performance dropped from 67.2% to 65.8% after continuing to increase the window size to the full sequence. The features located in the deep layers of the network are already highly abstracted, and simple linear projection may over-reorganize the channel features, resulting in insufficient feature distinguishability. The 1D group convolution prevents \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Method** & **Year** & **Backbone** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]** & **mAP@avg** \\ \hline SCNN [50] & CVPR16 & C3D & 36.3 & 28.7 & 19.0 & 10.3 & 5.3 & 19.9 \\ RC3D [2] & ICCV17 & C3D & 44.8 & 35.6 & 28.9 & - & - & - \\ SAD [3] & ACM17 & TSN & 43.0 & 35.0 & 24.6 & - & - & - \\ TALNet [29] & CVPR18 & I3D & 53.2 & 48.5 & 42.8 & 33.8 & 20.8 & 39.8 \\ BSN [10] & ECCV18 & TSN & 53.5 & 45.0 & 36.9 & 28.4 & 20.0 & 36.8 \\ MGG [5] & CVPR19 & I3D & 53.9 & 46.8 & 37.4 & 29.5 & 21.3 & 37.8 \\ PGCN [12] & ICCV19 & I3D & 60.1 & 54.3 & 45.5 & 33.5 & 19.8 & 42.6 \\ BMN [11] & ICCV19 & TSN & 56.0 & 47.4 & 38.8 & 29.7 & 20.5 & 36.8 \\ A2 NET [6] & TIP20 & I3D & 58.6 & 54.1 & 45.5 & 32.5 & 17.2 & 41.6 \\ GTAD [6] & CVPR20 & TSN & 54.5 & 47.6 & 40.2 & 30.8 & 23.4 & 39.3 \\ BCGNN [52] & ECCV20 & TSN & 57.1 & 49.1 & 40.4 & 31.2 & 23.1 & 40.2 \\ CSA [53] & ICCV21 & TSN & 64.4 & 58.0 & 49.2 & 38.2 & 27.8 & 47.5 \\ AFSD [33] & CVPR21 & I3D & 67.3 & 62.4 & 55.5 & 43.7 & 31.1 & 52.0 \\ ContextLoc [49] & ICCV21 & I3D & 68.3 & 63.8 & 54.3 & 41.8 & 26.2 & 50.9 \\ TBOS [54] & CVPR21 & C3D & 63.2 & 58.5 & 54.8 & 44.3 & 32.4 & 50.6 \\ RefactorNet [55] & CVPR2022 & I3D & 70.7 & 65.4 & 58.6 & 47.0 & 32.1 & 54.8 \\ ActionFormer [18] & 2022 & I3D & 75.5 & 72.5 & 65.6 & 56.6 & **42.7** & 62.6 \\ BCNet [56] & AAAI22 & I3D & 71.5 & 67.0 & 60.0 & 48.9 & 33.0 & 56.1 \\ RCL [57] & CVPR22 & TSN & 70.1 & 62.3 & 52.9 & 42.7 & 30.7 & 51.7 \\ AES [58] & CVPR22 & SF R50 & 69.4 & 64.3 & 56.0 & 46.4 & 34.9 & 54.2 \\ \hline
**GLFormer(Ours)** & & I3D & **75.9** & **72.6** & **67.2** & **57.2** & 41.8 & **62.9** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison with the state-of-the-art (THUMOS14), measured by the mAP at different tIoU thresholds and the average mAP in [0.3, 0.4, 0.5,0.6,0.7]; the best results are in bold. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Year** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]** & **mAP@avg** \\ \hline PGCN [12] & ICCV19 & 48.3 & 33.2 & 3.3 & 31.1 \\ BMN [11] & ICCV19 & 50.1 & 34.8 & 8.3 & 33.9 \\ PBRNet [31] & AAAI20 & 54.0 & 35.0 & 9.0 & 35.0 \\ GTAD [51] & CVPR20 & 50.4 & 34.6 & 9.0 & 34.1 \\ AFSD [33] & CVPR21 & 52.4 & 35.3 & 6.5 & 34.4 \\ ContextLoc [49] & ICCV21 & 56.0 & 35.2 & 3.6 & 34.2 \\ MUSES [48] & CVPR21 & 50.0 & 35.0 & 6.6 & 34.0 \\ ActionFormer [18] & 2022 & 53.5 & 36.2 & 8.2 & 35.6 \\ BCNet [56] & AAAI22 & 53.2 & 36.2 & **10.6** & 35.5 \\ RCL [57] & CVPR22 & 51.7 & 35.3 & 8.0 & 34.4 \\ AES [58] & CVPR22 & 50.1 & 35.8 & 10.5 & 35.1 \\ \hline
**GLFormer(Ours)** & & **54.5** & **37.7** & 7.6 & **36.3** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison with the state-of-the-art (ActivityNet 1.3), measured by the mAP at different tIoU thresholds, as well as the average mAP in [0.5:0.1:0.95]; the best results are in bold.