This ignores the inherent graph structure of the knowledge base, and performs reasoning from facts to answer one at a time, which is computationally inefficient. Two entities have a connecting edge if they belong to the same fact. Strengths -- The proposed approach is intuitive, sufficiently novel, and outperforms prior work by a large margin -- 10% better than the previous best approach, which is an impressive result. Weaknesses -- Given that the fact retrieval step is still the bottleneck in terms of accuracy (Table 4), it would be useful to check how sensitive downstream accuracy is to the choice of retrieving 100 facts. What is the answering accuracy if 50 facts are retrieved?

Accurately answering a question about a given image requires combining observations with general knowledge. While this is effortless for humans, reasoning with general knowledge remains an algorithmic challenge. To advance research in this direction a novel fact-based' visual question answering (FVQA) task has been introduced recently along with a large set of curated facts which link two entities, i.e., two possible answers, via a relation. Given a question-image pair, deep network techniques have been employed to successively reduce the large set of facts until one of the two entities of the final remaining fact is predicted as the answer. We observe that a successive process which considers one fact at a time to form a local decision is sub-optimal.