Reviews: Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understanding

This paper uses neural networks to parse visual scenes and language queries, transforming them into a logical representation that can be used to compute the output of the query on the scene. The logical representation is learned via a combination of direct supervision via a small number of traces and fine-tuning using end-to-end reinforcement learning. Advantages of the approach over existing approaches include: Reduction in the number of training examples, a more interpretable inference process and substantially increased accuracy. The overall approach shows great promise in increasing the performance of neural architectures by incorporating a symbolic component, as well as making them more robust, interpretable and debuggable. So I think this is a good direction for AI research to go in.