# VTG-GPT: Tuning-Free Zero-Shot Video Temporal Grounding with GPT
## 4 Experiments
### Comparisons to the State-of-the-Art  
In this section, we present a comprehensive comparison of our VTG-GPT with state-of-the-art (SOTA) methods in VTG. Firstly, we disclose results on the QVHighlights validation and test splits, as shown in Table 2. The approaches are categorized into fully supervised (FS), weakly supervised (WS), unsupervised (US), and zero-shot (ZS) methods. Notably,VTG-GPT significantly outperforms the previous SOTA zero-shot model ([PERSON] et al. [2]), demonstrating substantial improvements across five metrics. Specifically, [EMAIL_ADDRESS] saw an increase of +7.49 and [EMAIL_ADDRESS] improved by +7.23. Remarkably, VTG-GPT also vastly exceeds all WS methods. Most impressively, our approach surpasses the FS baseline (Moment-DETR [1]) in most metrics, even achieving competitive performance compared with FS methods. Unlike these methods, VTG-GPT requires only a single inference pass, eliminating the need for training data and resources. Subsequently, we report the performance on the Charades-STA test set and ActivityNet-Captions test set in Table 3. In Charades-STA, VTG-GPT surpasses the SOTA zero-shot method ([PERSON] et al. [23]) with a +5.81 increase in R1@0.7 and a +1.89 improvement in mIoU. Furthermore, VTG-GPT significantly outperforms the best US method ([PERSON] et al. [21]) across all metrics. However, on the ActivityNet-Captions dataset, our method falls slightly behind [PERSON] et al. in two metrics, which we attribute to the high downsampling rate used for this dataset. Moreover, VTG-GPT approaches the performance of the fully supervised Moment-DETR, validating its capacity to handle diverse and complex video contexts without any training or fine-tuning. This underscores the robustness and adaptability of VTG-GPT in zero-shot VTG scenarios, demonstrating its potential as a versatile and efficient tool for video understanding. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Setup**} & \multicolumn{4}{c}{**Charades-STA**} & \multicolumn{4}{c}{**ActivityNet-Captions**} \\ \cline{3-11}  & & & **R100.3** & **R100.5** & **[EMAIL_ADDRESS]** & **mIoU** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]** & **mIoU** \\ \hline
2D-TAN [16] & 2020 & PS & 57.31 & 45.75 & 27.88 & 41.05 & 60.32 & 43.41 & 25.04 & 42.45 \\ Moment-DETR [1] & 2021 & PS & 65.83 & 52.07 & 30.59 & 45.54 & - & - & - & - \\ VDI [12] & 2023 & PS & - & 52.32 & 31.37 & - & - & 48.09 & 28.76 & - \\ \hline CNM [41] & 2022 & WS & 60.04 & 35.15 & 14.95 & 38.11 & 55.68 & 33.33 & 13.29 & 37.55 \\ CPL [42] & 2022 & WS & 65.99 & 49.05 & 22.61 & 43.23 & 55.73 & 31.37 & 13.68 & 36.65 \\ [PERSON] et al. [44] & 2023 & WS & 69.16 & 5.218 & 23.94 & 45.20 & 58.07 & 36.91 & - & 41.02 \\ \hline PSVL [17] & 2021 & US & 46.47 & 31.29 & 14.17 & 31.24 & 44.74 & 30.08 & 14.74 & 29.62 \\ [PERSON] et al. [19] & 2021 & US & 46.69 & 20.14 & 8.27 & - & 46.15 & 26.38 & 11.64 & - \\ DSCNet [18] & 2022 & US & 44.15 & 28.73 & 14.67 & - & 47.29 & 28.16 & - & - \\ PZVMR [20] & 2022 & US & 46.83 & 33.21 & 18.51 & 32.62 & 45.73 & 31.26 & 17.84 & 30.35 \\ [PERSON] et al. [21] & 2023 & US & 52.95 & 37.24 & 19.33 & 36.05 & 47.61 & 32.59 & 15.42 & 31.85 \\ \hline UniVTG [13] & 2023 & PS & 44.09 & 25.22 & 10.03 & 27.12 & - & - & - & - \\ [PERSON] et al. [23] & 2023 & PS & 56.77 & 42.93 & 20.13 & 37.92 & **48.28** & 27.90 & 11.57 & **32.37** \\
**VTG-GPT** (Ours) & 2023 & PS & **59.48** & **43.68** & **25.94** & **39.81** & 47.13 & **28.25** & **12.84** & 30.49 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance comparison on Charades-STA _test_ split and ActivityNet-Captions _test_ split. Where FS means fully-supervised setting, WS means weakly-supervised, US means unsupervised, and ZS means zero-shot.