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Multimodal reasoning for video question answering

Published on:

29 February 2024

Primary Category:

Computation and Language

Paper Authors:

Kate Sanders,

Nathaniel Weir,

Benjamin Van Durme

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Key Details

Proposes TV-TREES, an interpretable video QA system producing multimodal reasoning trees

Introduces new task of multimodal entailment tree generation

Achieves SOTA zero-shot results on challenging TVQA benchmark

Provides transparent and reliable reasoning unlike black-box models

AI generated summary

Multimodal reasoning for video question answering

The authors propose TV-TREES, the first system to generate interpretable trees showing chains of reasoning across both language and visual content from videos. They evaluate it on a video QA dataset, where it achieves state-of-the-art zero-shot performance using full length clips as input while also providing transparent reasoning.

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