Multimodal Datasets and Benchmarks for Reasoning about Dynamic Spatio-Temporality in Everyday Environments

Ken Fukuda; Kensho Hara; Shusaku Egami; Takanori Ugai

Multimodal Datasets and Benchmarks for Reasoning about Dynamic Spatio-Temporality in Everyday Environments

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arxiv 2408.11347 v2 pith:SIUNZ6ID submitted 2024-08-21 cs.AI

Takanori Ugai , Kensho Hara , Shusaku Egami , Ken Fukuda This is my paper

classification cs.AI

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We used a 3D simulator to create artificial video data with standardized annotations, aiming to aid in the development of Embodied AI. Our question answering (QA) dataset measures the extent to which a robot can understand human behavior and the environment in a home setting. Preliminary experiments suggest our dataset is useful in measuring AI's comprehension of daily life. \end{abstract}

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VUDG: A Dataset for Video Understanding Domain Generalization
cs.CV 2025-05 conditional novelty 6.0 of 10

VUDG is a domain-generalization benchmark for video understanding with 11 domains and 36,388 QA pairs, and it shows that current large video-language models lose accuracy across visual domains.

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