Too Long; Didn’t Read
In this paper, researchers explore zero-shot video QA using GPT-3, outperforming supervised models, leveraging narrative summaries and visual matching.
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. Language models generate open-ended text which is irregular and often noisy. To retrieve the exact part of the video, we drive the model to output indices of the plot rather than the text form.
The generated indices might still be noisy due to the open-ended nature of language models. When the model outputs an answer in text form, we use rouge-l (19) score to find plot piece candidates whose similarity with the generated sentence are above the specified threshold α ≥ 0.5.
2.3. Visual Checking
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