  Visual Question Answering (VQA) models have achieved significant success in
recent times. Despite the success of VQA models, they are mostly black-box
models providing no reasoning about the predicted answer, thus raising
questions for their applicability in safety-critical such as autonomous systems
and cyber-security. Current state of the art fail to better complex questions
and thus are unable to exploit compositionality. To minimize the black-box
effect of these models and also to make them better exploit compositionality,
we propose a Dynamic Neural Network (DMN), which can understand a particular
question and then dynamically assemble various relatively shallow deep learning
modules from a pool of modules to form a network. We incorporate compositional
temporal attention to these deep learning based modules to increase
compositionality exploitation. This results in achieving better understanding
of complex questions and also provides reasoning as to why the module predicts
a particular answer. Experimental analysis on the two benchmark datasets,
VQA2.0 and CLEVR, depicts that our model outperforms the previous approaches
for Visual Question Answering task as well as provides better reasoning, thus
making it reliable for mission critical applications like safety and security.
