Papers with Clip Accuracy

2 papers
Event Causality Is Key to Computational Story Understanding (2024.naacl-long)

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Challenge: Cognitive science and symbolic AI research suggest that event causality provides vital information for story understanding.
Approach: They propose a method for event causality identification that leads to material improvements in story understanding.
Outcome: The proposed method improves story understanding on the COPES dataset . it achieves 4.1-10.9% increase on Clip Accuracy and 4.2-13.5% increase on Sentence IoU .
Multilingual Synopses of Movie Narratives: A Dataset for Vision-Language Story Understanding (2024.findings-emnlp)

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Challenge: Story video-text alignment is a core task in computational story understanding, but its progress has been held back by the scarcity of manually annotated video- text correspondences and the heavy concentration on English narrations of Hollywood movies.
Approach: They construct a multilingual video story dataset with 13,166 movie summary videos from 7 languages and manual annotations of fine-grained video-text correspondences.
Outcome: The proposed approach outperforms the SOTA methods on clip accuracy and Sentence IoU scores.

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