Zero-Shot Video Moment Retrieval Using BLIP-Based Models

Wattasseril, Jobin Idiculla; Shekhar, Sumit; Döllner, Jürgen; Trapp, Matthias

doi:10.1007/978-3-031-47969-4_13

Lecture Notes in Computer Science

2023

DOI: 10.1007/978-3-031-47969-4_13

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Zero-Shot Video Moment Retrieval Using BLIP-Based Models

Jobin Idiculla Wattasseril,

Sumit Shekhar,

Jürgen Döllner

et al.

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2024

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

Xu,

Sun,

Xie

et al. 2024

Applied Sciences

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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.

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

Xu,

Sun,

Xie

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

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Zero-Shot Video Moment Retrieval Using BLIP-Based Models

Cited by 1 publication

References 24 publications

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

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

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