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We propose Encyclopedic-VQA, a large scale visual question answering (VQA)dataset featuring visual questions about detailed properties of fine-grainedcategories and instances. It contains 221k unique question+answer pairs eachmatched with (up to) 5 images, resulting in a total of 1M VQA samples.Moreover, our dataset comes with a controlled knowledge base derived fromWikipedia, marking the evidence to support each answer. Empirically, we showthat our dataset poses a hard challenge for large vision+language models asthey perform poorly on our dataset: PaLI [14] is state-of-the-art on OK-VQA[37], yet it only achieves 13.0% accuracy on our dataset. Moreover, weexperimentally show that progress on answering our encyclopedic questions canbe achieved by augmenting large models with a mechanism that retrieves relevantinformation from the knowledge base. An oracle experiment with perfectretrieval achieves 87.0% accuracy on the single-hop portion of our dataset, andan automatic retrieval-augmented prototype yields 48.8%. We believe that ourdataset enables future research on retrieval-augmented vision+language models.It is available athttps://github.com/google-research/google-research/tree/master/encyclopedic_vqa .

Encyclopedic VQA: Visual questions about detailed properties of fine-grained categories