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
- arXiv
- arXiv:2404.12013
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
-
2024-04-18Compact 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)}, }