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

Second, the model is more data-and memory-efficient: it performs well after learning on a small number of training data; it can also encode an image into a compact representation, requiring less storage than existing methods for offline question answering.