Published April 18, 2024 | Version 1.0
Dataset Open

CompAct Dataset for Sequential Compositional Generalization in Multimodal Models

Description

CompAct (Compositional Activities) presents a comprehensive benchmark for assessing the compositional generalization abilities of Sequential Multimodal Models. CompAct is a carefully constructed, perceptually grounded dataset set within a rich backdrop of egocentric kitchen activity videos. Each instance in our dataset is represented with a combination of raw video footage, naturally occurring sound, and crowd-sourced step-by-step descriptions. More importantly, our setup ensures that the individual concepts are consistently distributed across training and evaluation sets, while their compositions are novel in the evaluation set. We conduct a comprehensive assessment of several unimodal and multimodal models.

Files

P01.zip

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Additional details

Identifiers

Related works

Is derived from
Journal article: 10.1007/s11263-021-01531-2 (DOI)
Is part of
Conference paper: arXiv:2404.12013 (arXiv)

Dates

Submitted
2024-04-18
Compact Dataset for Sequential Compositional Generalization in Multimodal Models

Software

Repository URL
https://cyberiada.github.io/CompAct/
Programming language
Python
Development Status
Concept

References

  • @inproceedings{yagcioglu2024compact, title={Sequential Compositional Generalization in Multimodal Models}, author={Semih Yagcioglu and Osman Batur Ince and Aykut Erdem and Erkut Erdem and Desmond Elliott and Deniz Yuret}, year={2024}, booktitle={North American Chapter of the Association for Computational Linguistics (NAACL)}, }