## 7 Conclusions  
In this paper, we introduced GLFormer for temporal action detection (TAD). This network takes advantage of multi-scale 1D convolution, global attention, and window  
Figure 3: Visualization of four proposals generated by GLFormer on the THUMOS14 dataset, which include short, medium, and long action instances.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Levels** & **Channels** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]** & **mAP@avg** & **GFLOPs** \\ \hline
4 & 1024 & 74.1 & 69.7 & 61.3 & 50.2 & 36.5 & 58.4 & 37.5 \\
5 & 1024 & 74.2 & 71.2 & 63.9 & 53.8 & 38.8 & 60.4 & 38.3 \\
6 & 1024 & 75.9 & 72.6 & 67.2 & 57.2 & 41.8 & 62.9 & 38.8 \\
7 & 1024 & 75.4 & 72.0 & 65.9 & 55.3 & 41.9 & 62.1 & 39.0 \\
6 & 256 & 75.2 & 72.4 & 65.0 & 55.1 & 42.7 & 60.1 & 33.9 \\
6 & 512 & 75.1 & 71.6 & 65.6 & 56.1 & 42.6 & 62.2 & 35.2 \\
6 & 2048 & 75.7 & 72.4 & 66.0 & 56.1 & 42.5 & 62.5 & 50.2 \\
6 & 4096 & 75.1 & 72.3 & 65.6 & 55.0 & 42.5 & 62.1 & 90.2 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Ablation study of different channel settings on THUMOS14 in terms of mAP(%)@toU.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]** & **mAP@avg** \\ \hline Max pooling & 75.7 & 72.3 & 66.3 & 57.3 & 43.1 & 62.9 \\ Avg pooling & 75.5 & 72.0 & 66.0 & 55.6 & 42.1 & 62.3 \\ stride = 2 & 75.9 & 72.6 & 67.2 & 57.2 & 41.8 & 62.9 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Ablation study of different downsampling operations on THUMOS14 in terms of mAP(%)@toU.  
self-attention to learn rich contexts for action classification and boundary prediction. Furthermore, taking into account action instances with various durations, an important component of our network is the temporal feature pyramid, which is achieved by using a 2\(\times\) downsampling between successive stages. The experimental evaluation shows that our model achieves state-of-the-art performance on the human activity datasets THUMOS14 and ActivityNet 1.3. Overall, this work highlights the importance of aggregating features with different ranges of attention and shows that window self-attention is an effective means to model longer-range temporal context in complex activity videos.  
Discussion: Although great achievements have been made in temporal action detection, there still remain many shortcomings that need further improvement: (i) manually labeling data results in excessive human and material investment, combining supervised learning with semi-supervised or even unsupervised learning; (ii) exploring effective post-processing methods to improve boundary positioning accuracy; (iii) the traditional TV-1 algorithm is inefficient and occupies storage space, which is replaced by the deep learning algorithm to realize the end-to-end processing process; (iv) real-world action instances are often dynamic and random (exploring effective methods to model nonlinear video sequence features); (v) adding data preprocessing steps to improve the quality of the raw data, such as the clutter effect, illumination effect, and complex scenarios.  
Conceptualization, Y.H. and Y.Z.; methodology, Y.H. and L.W.; software, Y.H. and L.W.; validation, Y.H.; formal analysis, Y.H. and Y.Z.; investigation, Y.H.; resources, Y.H.; data curation, Y.H.; writing--original draft preparation, Y.H.; writing--review and editing, Y.H.; visualization, Y.H. and J.D.; supervision, Y.Z.; project administration, Y.Z.; funding acquisition, Y.Z. All authors have read and agreed to the published version of the manuscript.  
Not applicable.  
The authors declare no conflict of interest.