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Current Visual Question Answering (VQA) systems can answer intelligentquestions about `Known' visual content. However, their performance dropssignificantly when questions about visually and linguistically `Unknown'concepts are presented during inference (`Open-world' scenario). A practicalVQA system should be able to deal with novel concepts in real world settings.To address this problem, we propose an exemplar-based approach that transferslearning (i.e., knowledge) from previously `Known' concepts to answer questionsabout the `Unknown'. We learn a highly discriminative joint embedding space,where visual and semantic features are fused to give a unified representation.Once novel concepts are presented to the model, it looks for the closest matchfrom an exemplar set in the joint embedding space. This auxiliary informationis used alongside the given Image-Question pair to refine visual attention in ahierarchical fashion. Since handling the high dimensional exemplars on largedatasets can be a significant challenge, we introduce an efficient matchingscheme that uses a compact feature description for search and retrieval. Toevaluate our model, we propose a new split for VQA, separating Unknown visualand semantic concepts from the training set. Our approach shows significantimprovements over state-of-the-art VQA models on the proposed Open-World VQAdataset and standard VQA datasets.

From Known to the Unknown: Transferring Knowledge to Answer Questions about Novel Visual and Semantic Concepts