  Visual Commonsense Reasoning (VCR), deemed as one challenging extension of
the Visual Question Answering (VQA), endeavors to pursue a more high-level
visual comprehension. It is composed of two indispensable processes: question
answering over a given image and rationale inference for answer explanation.
Over the years, a variety of methods tackling VCR have advanced the performance
on the benchmark dataset. Despite significant as these methods are, they often
treat the two processes in a separate manner and hence decompose the VCR into
two irrelevant VQA instances. As a result, the pivotal connection between
question answering and rationale inference is interrupted, rendering existing
efforts less faithful on visual reasoning. To empirically study this issue, we
perform some in-depth explorations in terms of both language shortcuts and
generalization capability to verify the pitfalls of this treatment. Based on
our findings, in this paper, we present a plug-and-play knowledge distillation
enhanced framework to couple the question answering and rationale inference
processes. The key contribution is the introduction of a novel branch, which
serves as the bridge to conduct processes connecting. Given that our framework
is model-agnostic, we apply it to the existing popular baselines and validate
its effectiveness on the benchmark dataset. As detailed in the experimental
results, when equipped with our framework, these baselines achieve consistent
and significant performance improvements, demonstrating the viability of
processes coupling, as well as the superiority of the proposed framework.
