# Video Temporal Grounding with Multi-Model Collaborative Learning
## 4 Experiments
### Comparison with State-of-the-Arts
#### 4.5.2 Quantitative Analysis  
The outcomes on the three benchmarks, as reported in Tables 1-3, indicate that our MMCL framework significantly enhances the baseline models' performance. Summarizing the results in Tables 1-3, we see that on the Charades-STA dataset, 2D-TAN underperforms VSLNet mainly because its 2D-MAP mechanism struggles with short segments and high-overlap video scenarios, whereas VSLNet's boundary-classification approach allows more precise modeling of short-segment boundaries. After incorporating the MMCL framework, 2D-TAN learns the semantic modeling capability of VSLNet's boundary-classification approach, improving its mIoU to 42.17 and achieving a relative improvement of 7.5%, demonstrating that the MMCL framework effectively compensates for 2D-TAN's shortcomings in modeling short segments. By contrast, on the TACoS dataset, VSLNet underperforms 2D-TAN because its span-based structure finds it difficult to handle the dense and uneven segment distribution of TACoS, while 2D-TAN's 2D-MAP mechanism is better suited for complex temporal-relationship modeling. After integrating the MMCL framework, VSLNet acquires long-segment relational modeling and temporal context-capturing abilities from 2D-TAN, raising its mIoU to 27.83 and yielding a 15.4% relative improvement. These results demonstrate that the MMCL framework, through knowledge transfer and collaborative learning, significantly improves both models' performance on the datasets where they originally fared poorly. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**Publication**} & \multicolumn{4}{c}{**Rank@1**} & \multirow{2}{*}{**mIoU**} \\ \cline{3-4}  & & **IoU = 0.3** & **IoU = 0.5** & **IoU = 0.7** & \\ \hline CTRL [1] & ICCV2017 & – & 23.63 & 8.9 & – \\ ROLE [40] & MM2018 & 25.26 & 12.12 & – & – \\ ACL [41] & WACV2019 & – & 30.48 & 12.20 & – \\ SAP [52] & AAAI2019 & – & 27.42 & 13.36 & – \\ SM-RL [42] & CVPR2019 & – & 24.36 & 11.17 & – \\ QSPN [53] & AAAI2019 & 54.70 & 35.60 & 15.80 & – \\ DEBUG [54] & EMNLP2019 & 54.95 & 37.39 & 17.92 & 36.34 \\ ExCL [43] & ACL2019 & – & 44.10 & 22.40 & – \\ MAN [55] & CVPR2019 & – & 46.53 & 22.72 & – \\ GDP [56] & AAAI2020 & 54.54 & 39.47 & 18.49 & – \\ DRN [44] & CVPR2020 & – & 42.90 & 23.68 & – \\ CBLN [45] & CVPR2021 & – & 43.67 & 24.44 & – \\ CI-MHA [57] & SIGIR2021 & 69.87 & 54.68 & 35.27 & – \\ MMN [59] & AAAI2022 & – & 47.31 & 27.28 & – \\ PS-VTG [28] & ITM2022 & – & 39.22 & 20.17 & – \\ D3G [46] & ICCV2023 & – & 41.64 & 19.60 & – \\ \hline
2D-TAN [23] & AAAI2020 & 57.31 & 39.70 & 23.31 & 39.23 \\ MRNTNet (2D-TAN) [51] & ICASSP2024 & 59.23 & 44.27 & 25.88 & 40.59 \\ MMCL (2D-TAN) & – & 60.58 & 46.95 & 28.26 & 42.17 \\ \hline VSLNet [22] & ACL2020 & 70.46 & 54.19 & 35.22 & 50.02 \\ MRNTNet (VSLNet) [51] & ICASSP2024 & 70.88 & 56.19 & 36.37 & 50.74 \\ MMCL (VSLNet) & – & **72.31** & **58.86** & **38.52** & **51.96** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance comparison with the state-of-the-art models on Charades-STA dataset.