Despite the great progress of Visual Question Answering (VQA), current VQA
models heavily rely on the superficial correlation between the question type
and its corresponding frequent answers (i.e., language priors) to make
predictions, without really understanding the input. In this work, we define
the training instances with the same question type but different answers as
\textit{superficially similar instances}, and attribute the language priors to
the confusion of VQA model on such instances. To solve this problem, we propose
a novel training framework that explicitly encourages the VQA model to
distinguish between the superficially similar instances. Specifically, for each
training instance, we first construct a set that contains its superficially
similar counterparts. Then we exploit the proposed distinguishing module to
increase the distance between the instance and its counterparts in the answer
space. In this way, the VQA model is forced to further focus on the other parts
of the input beyond the question type, which helps to overcome the language
priors. Experimental results show that our method achieves the state-of-the-art
performance on VQA-CP v2. Codes are available at
\href{https://github.com/wyk-nku/Distinguishing-VQA.git}{Distinguishing-VQA}.Comment: Published in COLING 202