user9000/CLEVR-HOPE
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- Declared licence: cc-by-4.0 (from the tag)
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- Access gated on Hugging Face: no
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Citation text from the card
@inproceedings{berlot-attwell-etal-2024-attribute,
title = "Attribute Diversity Determines the Systematicity Gap in {VQA}",
author = "Berlot-Attwell, Ian and
Agrawal, Kumar Krishna and
Carrell, Annabelle Michael and
Sharma, Yash and
Saphra, Naomi",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-main.537/",
doi = "10.18653/v1/2024.emnlp-main.537",
pages = "9576--9611",
abstract = "Although modern neural networks often generalize to new combinations of familiar concepts, the conditions that enable such compositionality have long been an open question. In this work, we study the systematicity gap in visual question answering: the performance difference between reasoning on previously seen and unseen combinations of object attributes. To test, we introduce a novel diagnostic dataset, CLEVR-HOPE. We find that the systematicity gap is not reduced by increasing the quantity of training data, but is reduced by increasing the diversity of training data. In particular, our experiments suggest that the more distinct attribute type combinations are seen during training, the more systematic we can expect the resulting mode
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