In this paper, researchers explore zero-shot video QA using GPT-3, outperforming supervised models, leveraging narrative summaries and visual matching.
This paper is available on arxiv under CC 4.0 license. Authors: Jiwan Chung, MIR Lab Yonsei University ; Youngjae Yu, MIR Lab Yonsei University . Table of Links Abstract and Intro Method Experiments Related Work Conclusion Limitations and References A. Experiment Details B. Prompt Samples 2. Method 2.1. Plot Generation 2.2. Narrative Search Given the summarized narrative and the question, we wish to retrieve the relatively short clip relevant to the question from the long video.
Authors: Authors: Jiwan Chung, MIR Lab Yonsei University ; https://jiwanchung.github.io/ Youngjae Yu, MIR Lab Yonsei University . https://jiwanchung.github.io/ Table of Links Abstract and Intro Method Experiments Related Work Conclusion Limitations and References A. Experiment Details B. Prompt Samples Abstract and Intro Abstract and Intro Method Method Experiments Experiments Related Work Related Work Conclusion Conclusion Limitations and References Limitations and References A.
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