  Fact-based Visual Question Answering (FVQA), a challenging variant of VQA,
requires a QA-system to include facts from a diverse knowledge graph (KG) in
its reasoning process to produce an answer. Large KGs, especially common-sense
KGs, are known to be incomplete, i.e., not all non-existent facts are always
incorrect. Therefore, being able to reason over incomplete KGs for QA is a
critical requirement in real-world applications that has not been addressed
extensively in the literature. We develop a novel QA architecture that allows
us to reason over incomplete KGs, something current FVQA state-of-the-art
(SOTA) approaches lack due to their critical reliance on fact retrieval. We use
KG Embeddings, a technique widely used for KG completion, for the downstream
task of FVQA. We also employ a new image representation technique we call
'Image-as-Knowledge' to enable this capability, alongside a simple one-step
CoAttention mechanism to attend to text and image during QA. Our FVQA
architecture is faster during inference time, being O(m), as opposed to
existing FVQA SOTA methods which are O(N log N), where m = number of vertices,
N = number of edges = O(m^2). KG embeddings are shown to hold complementary
information to word embeddings: a combination of both metrics permits
performance comparable to SOTA methods in the standard answer retrieval task,
and significantly better (26% absolute) in the proposed missing-edge reasoning
task.
