# VTG-GPT: Tuning-Free Zero-Shot Video Temporal Grounding with GPT
## 4 Experiments  
### Experimental Settings  
**Datasets. ** To demonstrate the superiority and effectiveness of our proposed tuning-free VTG-GPT framework, we conduct extensive experiments on three publicly available datasets: QVHighlights [1], Charades-STA [37], and ActivityNet-Captions [38], as these datasets encompass diverse types of videos. **QVHighlights** consists of 10,148 distinct YouTube videos, each accompanied by human annotations that include a textual query, a temporal segment, and frame-level saliency scores. Here, the saliency scores serve as the output for the highlight detection (HD) task, quantifying the relevance between a query and its corresponding frames. QVHighlights encompasses a wide array of themes, ranging from daily activities and travel in everyday vlogs to social and political events in news videos. For evaluation, Moment-DETR [1] allocates 15% of the data for validation and another 15% for testing, with consistent data distribution across both sets. Due to limitations on the online test server ([[https://codalab.lisn.upsaclay.fr/competitions/6937](https://codalab.lisn.upsaclay.fr/competitions/6937)]([https://codalab.lisn.upsaclay.fr/competitions/6937](https://codalab.lisn.upsaclay.fr/competitions/6937)), accessed on 1 September 2023) allowing a maximum of five submissions, all our ablation studies are conducted on the validation split. **Charades-STA**, derived from the original Charades [39] dataset, includes 9848 videos of human indoor activities, accompanied by 16,128 annotations. For this dataset, a standard split of 3720 annotations is specifically designated for testing. **ActivityNet-Captions**, built upon the raw ActivityNet [40] dataset, comprises 19,994 long YouTube videos from various domains. Since the test split is reserved for competitive evaluation, we follow the setup used in 2D-TAN [16], utilizing 17,031 annotations for testing. **Metrics. ** To effectively evaluate performance on VTG, we employ several metrics, including Recall-1 at Intersection over Union (IoU) thresholds (R1@\(m\)), mean average precision (mAP), and mean IoU (mIoU). R1@\(m\) measures the percentage of queries in the dataset where the highest-scoring predicted segment has an IoU greater than \(m\) with the ground truth. mIoU calculates the average IoU across all test samples. For a fair comparison, our results on the QVHighlights dataset report R1@\(m\) with \(m\) values of 0.5 and 0.7, mAP at IoU thresholds of 0.5 and 0.75, and the average mAP across multiple IoU thresholds [0.5:0.05:0.95]. For the Charades-STA dataset, we report R1@\(m\) for \(m\) values of 0.3, 0.5, and 0.7, along with mIoU. Finally, we employ mAP and HIT@1 to evaluate the results of HD, thereby measuring the query-frame relevance. Here, HIT@1 represents the accuracy of the highest-scoring frame. **Implementation details. ** To mitigate video information redundancy, we downsample QVHighlights and Charades-STA datasets to a frame rate of 0.5 per second. Considering the extended duration of videos in the ActivityNet-Captions, we extract one frame every three seconds. In the image captioning stage, we utilize MiniGPT-v2 [6] based on the LLaMa-2-Chat-7B [4]. For query debiasing, we employ Baichuan2-7B-Chat [5], also based on LLaMa-2 [5], generating five debiased queries (\(N_{q}=5\)) per instance. The temperature coefficients for MiniGPT-v2 and Baichuan2 are set at 0.1 and 0.2, respectively. Drawing from the preliminary experiments in Section 3.4, we select Sentence-BERT [35] as our similarity model to evaluate query-frame correlations using cosine similarity. The histogram in our proposal generator is configured with ten bins (\(N_{b}\)), with a selection of the top eight values (\(k=8\)) and a continuity threshold \(\lambda=6\). During the post-processing phase, the balance coefficient (\(\alpha\)) in the proposal scorer is set to 0.5, and the IoU threshold (\(\mu\)) for non-maximum suppression (NMS) is determined at 0.75. All pre-processing and experiments are conducted on eight NVIDIA RTX 3090 GPUs.