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arXiv · 2311.00807

VQA-GEN: A Visual Question Answering Benchmark for Domain Generalization

Suraj Jyothi Unni·Raha Moraffah·Huan Liu
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Abstract

Visual question answering (VQA) models are designed to demonstrate visual-textual reasoning capabilities. However, their real-world applicability is hindered by a lack of comprehensive benchmark datasets. Existing domain generalization datasets for VQA exhibit a unilateral focus on textual shifts while VQA being a multi-modal task contains shifts across both visual and textual domains. We propose VQA-GEN, the first ever multi-modal benchmark dataset for distribution shift generated through a shift induced pipeline. Experiments demonstrate VQA-GEN dataset exposes the vulnerability of existing methods to joint multi-modal distribution shifts. validating that comprehensive multi-modal shifts are critical for robust VQA generalization. Models trained on VQA-GEN exhibit improved cross-domain and in-domain performance, confirming the value of VQA-GEN. Further, we analyze the importance of each shift technique of our pipeline contributing to the generalization of the model.

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Suraj Jyothi Unni, Raha Moraffah, Huan Liu. 2023-11-01. VQA-GEN: A Visual Question Answering Benchmark for Domain Generalization. https://arxiv.org/abs/2311.00807

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