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One of the most challenging question types in VQA is when answering thequestion requires outside knowledge not present in the image. In this work westudy open-domain knowledge, the setting when the knowledge required to answera question is not given/annotated, neither at training nor test time. We tapinto two types of knowledge representations and reasoning. First, implicitknowledge which can be learned effectively from unsupervised languagepre-training and supervised training data with transformer-based models.Second, explicit, symbolic knowledge encoded in knowledge bases. Our approachcombines both - exploiting the powerful implicit reasoning of transformermodels for answer prediction, and integrating symbolic representations from aknowledge graph, while never losing their explicit semantics to an implicitembedding. We combine diverse sources of knowledge to cover the wide variety ofknowledge needed to solve knowledge-based questions. We show our approach,KRISP (Knowledge Reasoning with Implicit and Symbolic rePresentations),significantly outperforms state-of-the-art on OK-VQA, the largest availabledataset for open-domain knowledge-based VQA. We show with extensive ablationsthat while our model successfully exploits implicit knowledge reasoning, thesymbolic answer module which explicitly connects the knowledge graph to theanswer vocabulary is critical to the performance of our method and generalizesto rare answers.

KRISP: Integrating Implicit and Symbolic Knowledge for Open-Domain Knowledge-Based VQA