  In this paper, we propose a method to obtain robust explanations for visual
question answering(VQA) that correlate well with the answers. Our model
explains the answers obtained through a VQA model by providing visual and
textual explanations. The main challenges that we address are i) Answers and
textual explanations obtained by current methods are not well correlated and
ii) Current methods for visual explanation do not focus on the right location
for explaining the answer. We address both these challenges by using a
collaborative correlated module which ensures that even if we do not train for
noise based attacks, the enhanced correlation ensures that the right
explanation and answer can be generated. We further show that this also aids in
improving the generated visual and textual explanations. The use of the
correlated module can be thought of as a robust method to verify if the answer
and explanations are coherent. We evaluate this model using VQA-X dataset. We
observe that the proposed method yields better textual and visual justification
that supports the decision. We showcase the robustness of the model against a
noise-based perturbation attack using corresponding visual and textual
explanations. A detailed empirical analysis is shown. Here we provide source
code link for our model \url{https://github.com/DelTA-Lab-IITK/CCM-WACV}.
