# DANet: Temporal Action Localization with Double Attention
## 4 Experiments and Results
### Performance Comparison
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Type** & **Model** & **Feature** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]** & **mAP@Avg** \\ \hline \multirow{8}{*}{Two-Stage} & BSN (2018) [4] & TSN [42] & 53.5 & 45.2 & 36.8 & 28.5 & 20.2 & 36.9 \\  & BMN (2019) [7] & TSN [42] & 57.1 & 46.8 & 38.8 & 29.6 & 20.8 & 38.7 \\  & P-GCN (2019) [43] & I3D [37] & 64.1 & 57.4 & 50.1 & — & — & — \\  & DBG (2020) [44] & TSN [42] & 58.1 & 49.4 & 38.7 & 31.1 & 21.6 & 39.5 \\  & G-TAD (2020) [40] & TSN [42] & 54.7 & 48.0 & 40.5 & 31.2 & 24.2 & 40.0 \\  & BC-GNN (2020) [45] & TSN [42] & 57.2 & 49.5 & 40.1 & 31.5 & 23.2 & 40.3 \\  & TAL-MR (2020) [46] & I3D [37] & 54.1 & 51.0 & 45.3 & 38.6 & 28.1 & 43.3 \\  & TSA-Net (2020) [47] & P3D [48] & 61.3 & 56.1 & 47.2 & 36.1 & 25.3 & 45.5 \\  & MUSES (2021) [6] & I3D [37] & 68.7 & 64.3 & 56.4 & 47.2 & 31.1 & 53.6 \\  & TCANet (2021) [17] & TSN [42] & 61.1 & 54.2 & 44.2 & 36.7 & 27.2 & 48.1 \\  & BMN-CSA (2021) [27] & TSN [42] & 65.0 & 58.2 & 50.1 & 37.8 & 27.6 & 47.7 \\  & ContextLoc (2021) [26] & I3D [37] & 68.5 & 64.0 & 53.7 & 41.6 & 26.5 & 51.2 \\  & RTD-Net (2021) [25] & I3D [37] & 68.2 & 63.0 & 51.7 & 38.6 & 24.2 & 49.2 \\ \hline \multirow{8}{*}{One-Stage} & GTAN (2019) [8] & P3D [48] & 57.9 & 47.2 & 39.1 & — & — & — \\  & A\({}^{\rm a}\)Net (2020) [49] & I3D [37] & 58.9 & 54.2 & 46.2 & 33.0 & 17.5 & 41.8 \\ \cline{1-1}  & PBRNet (2020) [24] & I3D [37] & 58.4 & 54.9 & 51.5 & 41.6 & 29.8 & — \\ \cline{1-1}  & A\({}^{\rm r}\)SED (2021) [5] & I3D [37] & 67.1 & 63.2 & 55.1 & 44.2 & 30.9 & 51.9 \\ \cline{1-1}  & TadTR (2022) [9] & I3D [37] & 74.8 & 69.1 & 59.8 & 47.6 & 33.1 & 56.9 \\ \cline{1-1}  & ActionFormer (2022) [10] & I3D [37] & 81.0 & 76.6 & 69.2 & 56.9 & 43.5 & 65.4 \\ \hline \end{tabular}
\end{table} 
Table 1: Performance comparison of different algorithms on THUMOS14. \begin{table}
\begin{tabular}{c c c c c} \hline
**Type** & **Model** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]** & **mAP@Avg** \\ \hline \multirow{8}{*}{Two-Stage} & BSN (2018) [4] & 46.6 & 30.0 & 8.1 & 30.0 \\  & BMN (2019) [7] & 50.2 & 34.9 & 8.5 & 33.9 \\  & P-GCN (2019) [43] & 48.5 & 33.4 & 3.5 & 31.2 \\  & G-TAD (2020) [40] & 50.5 & 34.7 & 9.2 & 34.2 \\  & BC-GNN (2020) [45] & 50.6 & 34.8 & 9.4 & 34.5 \\  & TAL-MR (2020) [46] & 43.6 & 34.1 & 9.4 & 30.4 \\  & TSA-Net (2020) [47] & 48.9 & 32.3 & 9.2 & 32.1 \\  & MUSES (2021) [6] & 50.2 & 34.9 & 6.7 & 34.2 \\  & TCANet (2021) [17] & 52.4 & 36.8 & 7.1 & 35.7 \\  & BMN-CSA (2021) [27] & 52.5 & 36.3 & 5.2 & 35.6 \\  & ContextLoc (2021) [26] & 56.0 & 34.9 & 3.7 & 34.1 \\  & RTD-Net (2021) [25] & 47.3 & 31.0 & 8.7 & 31.0 \\ \hline \multirow{8}{*}{One-Stage} & GTAN (2019) [8] & 52.8 & 34.3 & 8.8 & 34.3 \\  & A\({}^{\rm a}\)Net (2020) [49] & 43.7 & 28.4 & 3.9 & 27.6 \\ \cline{1-1}  & PBRNet (2020) [24] & 54.1 & 35.4 & 8.8 & 35.0 \\ \cline{1-1}  & AFSD (2021) [5] & 52.4 & 35.5 & 6.1 & 34.5 \\ \cline{1-1}  & TadTR (2022) [9] & 49.2 & 32.5 & 8.7 & 32.4 \\ \cline{1-1}  & ActionFormer (2022) [10] & 54.5 & 37.7 & 8.2 & 36.4 \\ \hline \end{tabular}
\end{table} 
Table 2: Performance comparison of different algorithms on ActivityNet1.3. In addition, the computational complexity of DANet is a crucial aspect to consider in assessing its feasibility and scalability. In terms of computational complexity, DANet operates at an acceptable level, given its ability to effectively handle long-term dependencies. The primary computational burden arises from the temporal attention mechanisms, which involve calculating attention weights between each time step in the input sequence. To mitigate this computational overhead, we employ optimization techniques such as parallel computing and efficient data structures. By leveraging parallelization across multiple processors or GPUs, we distribute the attention calculations and reduce the overall runtime. In addition, we utilize optimized data structures, such as sparse matrices, to store and process attention weights efficiently.