# VTG-GPT: Tuning-Free Zero-Shot Video Temporal Grounding with GPT
## 4 Experiments
### Comparisons to the State-of-the-Art
\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}  & & & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]** & **[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 & ZS & 44.09 & 25.22 & 10.03 & 27.12 & - & - & - & - \\ [PERSON] et al. [23] & 2023 & ZS & 56.77 & 42.93 & 20.13 & 37.92 & **48.28** & 27.90 & 11.57 & **32.37** \\
**VTG-GPT** (Ours) & 2023 & ZS & **59.48** & **43.68** & **25.94** & **39.81** & 47.13 & **28.25** & **12.84** & 30.49 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance comparison on QVHighlights _test_ and _val_ split. FS means fully-supervised method, WS means weakly supervised, and ZS means zero-shot. To qualitatively validate the effectiveness of our VTG-GPT model, we present visual comparisons of grounding results from the Ground-Truth (GT), Moment-DETR, and VTG-GPT in Figure 5. Observations indicate that the tuning-free VTG-GPT achieves more precise localization than the supervised Moment-DETR. The primary reason is that Moment-DETR relies solely on the original queries, which contain human-annotated errors, thus failing to fully align with the video's semantic information. In contrast, VTG-GPT can correct erroneous queries and reduce the bias introduced by human annotations, leading to more accurate grounding. To be more specific, in Figure 5a, our model detects a spelling mistake in the query, where \"_ociture_\" is corrected to \"_image_\" or \"_picture_\". In Figure 5b, the action \"_turns off_\" is refined to terms more congruent with the video context, such as \"_lights off_\", \"_darkened environment_\", and \"_no lights_\". Additionally, the five rephrasings of each original query, in contrast to the original phrasing, exhibit more flexible grammatical structures, enabling the text encoder to comprehensively capture the semantic information of the original query.