  We propose a novel approach to identify the difficulty of visual questions
for Visual Question Answering (VQA) without direct supervision or annotations
to the difficulty. Prior works have considered the diversity of ground-truth
answers of human annotators. In contrast, we analyze the difficulty of visual
questions based on the behavior of multiple different VQA models. We propose to
cluster the entropy values of the predicted answer distributions obtained by
three different models: a baseline method that takes as input images and
questions, and two variants that take as input images only and questions only.
We use a simple k-means to cluster the visual questions of the VQA v2
validation set. Then we use state-of-the-art methods to determine the accuracy
and the entropy of the answer distributions for each cluster. A benefit of the
proposed method is that no annotation of the difficulty is required, because
the accuracy of each cluster reflects the difficulty of visual questions that
belong to it. Our approach can identify clusters of difficult visual questions
that are not answered correctly by state-of-the-art methods. Detailed analysis
on the VQA v2 dataset reveals that 1) all methods show poor performances on the
most difficult cluster (about 10\% accuracy), 2) as the cluster difficulty
increases, the answers predicted by the different methods begin to differ, and
3) the values of cluster entropy are highly correlated with the cluster
accuracy. We show that our approach has the advantage of being able to assess
the difficulty of visual questions without ground-truth (\ie, the test set of
VQA v2) by assigning them to one of the clusters. We expect that this can
stimulate the development of novel directions of research and new algorithms.
