# VTG-GPT: Tuning-Free Zero-Shot Video Temporal Grounding with GPT
## 4 Experiments
### Ablation Studies  
To demonstrate the effectiveness of each module within our VTG-GPT framework, we perform in-depth ablation studies on the QVHighlights dataset. **Effect of debiased query. ** Firstly, we report saliency scores used to evaluate query-frame correlation. As delineated in Table 4, row three corresponds to VTG-GPT without debiasing, where we directly employ the similarity generated by Sentence-BERT as the saliency scores. Conversely, row four is VTG-GPT with debiasing, wherein we average the similarity of five debiased queries as saliency scores. The comparison reveals that row four significantly outperforms row three, demonstrating the efficacy of our debiasing strategy in mitigating human biases inherent in the original queries. Furthermore, comparing row two (UMT [9]) and row four, our VTG-GPT achieves a notable increase in HIT@1, recording a score of 62.29 (+2.3). This enhancement underscores VTG-GPT's superior reasoning capabilities in discerning challenging cases, affirming the value of our debiasing approach in refining model performance. Then, we investigate the impact of different numbers of debiased queries (\(N_{q}\)) generated by Baichuan2 on the performance of the VTG-GPT model. Our findings, as depicted in Figure 6a, indicate that the model achieves optimal results when utilizing five debiased queries (\(N_{q}=5\)). Compared to using solely the original biased query, implementing five debiased queries resulted in a notable increase in [EMAIL_ADDRESS] to 54.26 (+3.87) and an improvement in mAP Avg. to 30.91 (+2.59). This evidence suggests that removing bias from queries significantly enhances the model's accuracy. However, an intriguing observation emerged: the performance metrics decline when \(N_{q}\) exceeds 5. This pattern suggests that excessive rephrasing does not continually yield improvements, likely due to the finite number of synonymous rewrites and syntactic variations available to maintain the original intent of the query. Over-rephrasing can introduce irrelevant content, deviating from the semantic intent of the raw query, and potentially diminishing model performance. This finding underscores the critical need to balance the number of query rewrites, ensuring that debiased queries capture a spectrum of semantic nuances while retaining the essence of the original \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**Setup**} & \multirow{2}{*}{**Debiasing**} & \multicolumn{2}{c}{**HD (\(\geq\)VG)**} \\ \cline{3-5}  & & & **mAP** & **HIT@1** \\ \hline Moment-DETR [1] & FS & ✗ & 35.69 & 55.60 \\ UMT [9] & FS & ✗ & **38.18** & 59.99 \\ \hline \multirow{2}{*}{**VTG-GPT**} & \multirow{2}{*}{ZS} & ✗ & 34.84 & 60.48 \\  & & ✓ & 36.08 & **62.29** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of video highlight detection (HD) on QVHighlights _val_ split. VG is the abbreviation of very good. ✓ and ✗ respectively represent the use and non-use of debiased queries. query. Future research should focus on developing advanced query debiasing techniques to enhance this equilibrium. **LLMs and LMMs. ** In Table 5, we evaluate the capabilities of LLMs (LLaMA-v2 [4] and Baichuan2 [5]), alongside LMMs (MiniGPT-4 [31] and MiniGPT-v2 [6]) in handling biased queries and generating image captions. A comparison between rows two and five reveals that Baichuan2 outperforms LLaMa-v2, since it is trained on a more diverse dataset and tasks based on LLaMa-v2, enhancing its sentence rewriting capabilities. As illustrated in row three, MiniGPT-v2, also developed on the foundations of LLaMa-v2, shows moderate results in text dialogue. Comparing rows four and five, we observe an improvement in the performance of MiniGPT-v2 over MiniGPT-4. Overall, the results suggest that the integration of Baichuan2 for query debiasing combined with MiniGPT-v2 for image captioning emerges as the most effective strategy.