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Building a Video-and-Language Dataset with Human Actions for Multimodal Logical Inference

2021/06/27 by Riko Suzuki, Hitomi Yanaka, Suzuki, Riko +5
Computer Science · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Multimodal Machine Learning Applications #Natural Language Processing Techniques #Topic Modeling

paper · pdf · doi:10.48550/arxiv.2106.14137

openalex publication_date 2021/06/27 · openalex created_date 2021/07/05 · openalex updated_date 2026/07/28

Abstract

This paper introduces a new video-and-language dataset with human actions for multimodal logical inference, which focuses on intentional and aspectual expressions that describe dynamic human actions. The dataset consists of 200 videos, 5,554 action labels, and 1,942 action triplets of the form that can be translated into logical semantic representations. The dataset is expected to be useful for evaluating multimodal inference systems between videos and semantically complicated sentences including negation and quantification.

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