# VTG-GPT: Tuning-Free Zero-Shot Video Temporal Grounding with GPT
## 3 Our Method
### Query Debiasing  
Mitigating biases in ground-truth queries represents a crucial and challenging problem for VTG, as these biases often originate from inherent human subjectivity. Such biases often include errors like misspellings and inaccurate descriptions of video content, as shown in Figure 2. Moreover, different annotators may characterize the same video segment in varying ways. A minority might opt for a formal language style, while others might gravitate towards colloquial or slang expressions. This difference in descriptions can inadvertently lead the model to prefer certain types of queries, thus introducing human prejudice and potentially diminishing the model's performance.  
To address the aforementioned challenges, we utilize Baichuan2 to eliminate human biases inherent in original queries, as demonstrated in Figure 4a. In line with human linguistic comprehension [32], our first step is to rectify spelling and grammatical inaccuracies in original query \(T\), thus producing the corrected version \(T_{c}\). We direct GPT with the instruction: _Please correct spelling and grammatical errors in the original query._ Subsequently, we instruct Baichuan2 to rewrite \(T_{c}\) to remove incorrect descriptions. The corresponding command is _Please rephrase the corrected query using different wording while maintaining the same intent and information._ Finally, we generate five semantically similar yet syntactically diverse queries \(Q\) to prevent the model from relying on a specific query type. The command for this is _Provide five different rephrasings._ Although it is generally advisable to issue only one command per message in GPT dialogues to avoid model errors, as noted in [30], we  
Figure 3: An overview of our proposed VTG-GPT, the framework contains four key phases: query debiasing (Section 3.2), image captioning (Section 3.3), proposal generation (Section 3.4), and post-processing (Section 3.5).  
discover in our tests that aggregating all instructions into a single message to GPT proved more effective, as shown in Figure 4a. It is important to note that the red font is not present in the code. A case involving misspelled words is shown in Figure 5a, where the incorrectly spelled word \"_ociture_\" is corrected to \"_image_\" or \"_picture_\". Figure 5b demonstrates a scenario involving a non-existent action, where \"_turn off the lights_\" is optimized to \"_lights off_\" or \"_a darkened environment_\", where \"_a darkened environment_\" is more congruent with the original video segment. In short, this debiasing strategy, featuring variations that differ in structure and word choice, deeply explores semantic information and enables the model to process various real-world queries effectively.  
Figure 4: **(a)** An example of query refinement using Baichuan2. **(b)** An example of image captioning using MiniGPT-v2. The red font employed here is for demonstration purposes only and is not present in the actual code.  
Figure 5: Visualization of predictions on QVHighlights _val_ split. **(a)** misspelled words. **(b)** incorrect descriptions. Our VTG-GPT achieves more precise localization compared to Moment-DETR [1], as it can correct errors in the original queries through rewriting and generate debiased queries, thereby facilitating more accurate grounding.