VTG-GPT: Tuning-Free Zero-Shot Video Temporal Grounding with GPT
<|reference_start|>VTG-GPT: Tuning-Free Zero-Shot Video Temporal Grounding with GPT: 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<|reference_end|>
@article{xu2024vtg-gpt:,
    title={VTG-GPT: Tuning-Free Zero-Shot Video Temporal Grounding with GPT},
    author={Yifang Xu, Yunzhuo Sun, Zien Xie, Benxiang Zhai, and Sidan Du},
    journal={arXiv preprint arXiv:2403.02076},
    year={2024},
    doi={10.3390/app14051894},
    archivePrefix={arXiv},
    eprint={2403.02076},
    primaryClass={cs.CV cs.AI}
}