  The collaborative reasoning for understanding each image-question pair is
very critical but under-explored for an interpretable Visual Question Answering
(VQA) system. Although very recent works also tried the explicit compositional
processes to assemble multiple sub-tasks embedded in the questions, their
models heavily rely on the annotations or hand-crafted rules to obtain valid
reasoning layout, leading to either heavy labor or poor performance on
composition reasoning. In this paper, to enable global context reasoning for
better aligning image and language domains in diverse and unrestricted cases,
we propose a novel reasoning network called Adversarial Composition Modular
Network (ACMN). This network comprises of two collaborative modules: i) an
adversarial attention module to exploit the local visual evidence for each word
parsed from the question; ii) a residual composition module to compose the
previously mined evidence. Given a dependency parse tree for each question, the
adversarial attention module progressively discovers salient regions of one
word by densely combining regions of child word nodes in an adversarial manner.
Then residual composition module merges the hidden representations of an
arbitrary number of children through sum pooling and residual connection. Our
ACMN is thus capable of building an interpretable VQA system that gradually
dives the image cues following a question-driven reasoning route and makes
global reasoning by incorporating the learned knowledge of all attention
modules in a principled manner. Experiments on relational datasets demonstrate
the superiority of our ACMN and visualization results show the explainable
capability of our reasoning system.
