  Video temporal grounding (VTG) aims to locate specific temporal segments from
an untrimmed video based on a linguistic query. Most existing VTG models are
trained on extensive annotated video-text pairs, a process that not only
introduces human biases from the queries but also incurs significant
computational costs. To tackle these challenges, we propose VTG-GPT, a
GPT-based method for zero-shot VTG without training or fine-tuning. To reduce
prejudice in the original query, we employ Baichuan2 to generate debiased
queries. To lessen redundant information in videos, we apply MiniGPT-v2 to
transform visual content into more precise captions. Finally, we devise the
proposal generator and post-processing to produce accurate segments from
debiased queries and image captions. Extensive experiments demonstrate that
VTG-GPT significantly outperforms SOTA methods in zero-shot settings and
surpasses unsupervised approaches. More notably, it achieves competitive
performance comparable to supervised methods. The code is available on
https://github.com/YoucanBaby/VTG-GPT
