Attribute Diversity Determines the Systematicity Gap in VQA
November 15, 2023 ยท Declared Dead ยท ๐ Conference on Empirical Methods in Natural Language Processing
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Authors
Ian Berlot-Attwell, Kumar Krishna Agrawal, A. Michael Carrell, Yash Sharma, Naomi Saphra
arXiv ID
2311.08695
Category
cs.LG: Machine Learning
Cross-listed
cs.CL,
cs.CV
Citations
2
Venue
Conference on Empirical Methods in Natural Language Processing
Last Checked
5 months ago
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 model to be.
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