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

Takanori Ugai, Kensho Hara, Shusaku Egami, Ken Fukuda

TL;DR

The question answering (QA) dataset measures the extent to which a robot can understand human behavior and the environment in a home setting and is useful in measuring AI's comprehension of daily life.

Abstract

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}

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

TL;DR

The question answering (QA) dataset measures the extent to which a robot can understand human behavior and the environment in a home setting and is useful in measuring AI's comprehension of daily life.

Abstract

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}
Paper Structure (5 sections, 1 figure, 1 table)

This paper contains 5 sections, 1 figure, 1 table.

Figures (1)

  • Figure 1: Example of video snapshot and action script