  Visual Question Answering (VQA) is a complex task requiring large datasets
and expensive training. Neural Module Networks (NMN) first translate the
question to a reasoning path, then follow that path to analyze the image and
provide an answer. We propose an NMN method that relies on predefined
cross-modal embeddings to ``warm start'' learning on the GQA dataset, then
focus on Curriculum Learning (CL) as a way to improve training and make a
better use of the data. Several difficulty criteria are employed for defining
CL methods. We show that by an appropriate selection of the CL method the cost
of training and the amount of training data can be greatly reduced, with a
limited impact on the final VQA accuracy. Furthermore, we introduce
intermediate losses during training and find that this allows to simplify the
CL strategy.
