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VTG-GPT: Tuning-Free Zero-Shot Video Temporal Grounding with GPT

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Pubblicato in:Applied Sciences vol. 14, no. 5 (2024), p. 1894
Autore principale: Xu, Yifang
Altri autori: Sun, Yunzhuo, Xie, Zien, Zhai, Benxiang, Du, Sidan
Pubblicazione:
MDPI AG
Soggetti:
Language
Design
Methods
Linguistics
Annotations
Queries
Proposals
Natural language
Bias
Prejudice
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Abstract: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 GitHub.
ISSN:2076-3417
DOI:10.3390/app14051894
Fonte:Publicly Available Content Database