# VTG-GPT: Tuning-Free Zero-Shot Video Temporal Grounding with GPT
## 6 Discussion  
**Limitations.** In our study, constrained by computational resources, we downsample frames in the long-video dataset ActivityNet-Captions, which adversely affected perfor  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(\mu\) & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]** & **mAP Avg.** \\ \hline
0.6 & 53.71 & 28.48 & 30.06 \\
0.7 & 54.12 & 29.60 & 30.54 \\
**0.75** & **54.17** & 29.73 & **30.91** \\
0.8 & 54.02 & **29.87** & 30.68 \\
0.9 & 53.81 & 29.63 & 30.41 \\
1.0 & 53.96 & 29.25 & 30.38 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of different IoU thresholds (\(\mu\)) in NMS on QVHighlights _val_ split.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Debiasing** & **Captioning** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]** & **mAP Avg.** \\ \hline LLaMA-v2 [4] & MiniGPT-4 [31] & 50.78 & 30.56 & 27.20 \\ LLaMA-v2 & MiniGPT-v2 [6] & **54.65** & 34.08 & 30.15 \\ MiniGPT-v2 & MiniGPT-v2 & 50.46 & 29.87 & 27.48 \\ Baichuan2 [5] & MiniGPT-4 & 52.78 & 33.84 & 28.54 \\
**Baichuan2** & **MiniGPT-v2** & 54.26 & **38.45** & **30.91** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ablation study of different LLMs and LMMs (Large Multi-modal Models) on QVHighlights _val_ split.  
Figure 7: Ablation experiments for top-\(k\) and continuity threshold (\(\lambda\)) in proposal generator on QVHighlights _val_ split. When \(k=8\) and \(\lambda=6\), the model achieves the best performance (red triangle).  
mance. Future work should focus on developing a more efficient and rapid GPT model to address this challenge. Moreover, due to the limitations imposed by the context length in video-based GPT, our framework relies solely on image-based GPT, thus needing more temporal information modeling.  
**In future work**, we will explore applying video-based GPT (such as VideoChat-GPT [45]) to enhance the capabilities of zero-shot VTG. In addition, crafting a more efficient module for query debiasing and proposal generation is paramount. Finally, leveraging GPT to implement a zero-shot framework on other data-driven tasks (such as video summarization [13], depth estimation [46; 47] and transformer diagnosis [48]) is very promising.  
**Ethical considerations.** Our work is based on open-source LLMs and LMMs which require direct inference without training, thereby reducing the carbon footprint. Additionally, we utilize common and safe prompts, and have not observed the generation of harmful or offensive content by the model.  
Conceptualization, Y.X. and Y.S.; methodology, Y.X.; software, Y.X. and Y.S.; validation, Z.X. and B.Z.; formal analysis, Y.X.; investigation, Y.X.; resources, Y.S.; data curation, Y.X.; writing--original draft preparation, Y.X. and Y.S.; writing--review and editing, Z.X. and B.Z.; visualization, Z.X. and B.Z.; supervision, S.D.; project administration, S.D.; funding acquisition, S.D. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
**Data Availability Statement:** Publicly available datasets were analyzed in this study. This data can be found here: [[https://github.com/YoucanBaby/VTG-GPT](https://github.com/YoucanBaby/VTG-GPT)]([https://github.com/YoucanBaby/VTG-GPT](https://github.com/YoucanBaby/VTG-GPT)).  
Many thanks to [PERSON] for his discussion and help in polishing this paper.  
The authors declare no conflicts of interest.