Reviews: Out of the Box: Reasoning with Graph Convolution Nets for Factual Visual Question Answering

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?