Reviews: Learning Conditioned Graph Structures for Interpretable Visual Question Answering

The predicted graph connectivity (at least in these few examples) looks quite intuitive and interpretable, even when the model predicts the incorrect answer. Weaknesses -- Figure 2 caption says "[insert quick recap here]":) -- The paper emphasizes multiple times that the proposed approach achieves state of the art accuracies on VQA v2, but that does not seem to be the case. The best published result so far -- the counting module by Zhang et al., ICLR 2018 -- performs 3% better than the proposed approach (as shown in Table 1 as well). This claim needs to be sufficiently toned down. Also, the proposed approach is marginally better than the base Bottom-Up architecture.