  Though beneficial for encouraging the Visual Question Answering (VQA) models
to discover the underlying knowledge by exploiting the input-output correlation
beyond image and text contexts, the existing knowledge VQA datasets are mostly
annotated in a crowdsource way, e.g., collecting questions and external reasons
from different users via the internet. In addition to the challenge of
knowledge reasoning, how to deal with the annotator bias also remains unsolved,
which often leads to superficial over-fitted correlations between questions and
answers. To address this issue, we propose a novel dataset named
Knowledge-Routed Visual Question Reasoning for VQA model evaluation.
Considering that a desirable VQA model should correctly perceive the image
context, understand the question, and incorporate its learned knowledge, our
proposed dataset aims to cutoff the shortcut learning exploited by the current
deep embedding models and push the research boundary of the knowledge-based
visual question reasoning. Specifically, we generate the question-answer pair
based on both the Visual Genome scene graph and an external knowledge base with
controlled programs to disentangle the knowledge from other biases. The
programs can select one or two triplets from the scene graph or knowledge base
to push multi-step reasoning, avoid answer ambiguity, and balanced the answer
distribution. In contrast to the existing VQA datasets, we further imply the
following two major constraints on the programs to incorporate knowledge
reasoning: i) multiple knowledge triplets can be related to the question, but
only one knowledge relates to the image object. This can enforce the VQA model
to correctly perceive the image instead of guessing the knowledge based on the
given question solely; ii) all questions are based on different knowledge, but
the candidate answers are the same for both the training and test sets.
