Graph-based Heuristic Search for Module Selection Procedure in Neural
  Module Network
  Neural Module Network (NMN) is a machine learning model for solving the
visual question answering tasks. NMN uses programs to encode modules'
structures, and its modularized architecture enables it to solve logical
problems more reasonably. However, because of the non-differentiable procedure
of module selection, NMN is hard to be trained end-to-end. To overcome this
problem, existing work either included ground-truth program into training data
or applied reinforcement learning to explore the program. However, both of
these methods still have weaknesses. In consideration of this, we proposed a
new learning framework for NMN. Graph-based Heuristic Search is the algorithm
we proposed to discover the optimal program through a heuristic search on the
data structure named Program Graph. Our experiments on FigureQA and CLEVR
dataset show that our methods can realize the training of NMN without
ground-truth programs and achieve superior efficiency over existing
reinforcement learning methods in program exploration.
