  We present a simple method that achieves unexpectedly superior performance
for Complex Reasoning involved Visual Question Answering. Our solution collects
statistical features from high-frequency words of all the questions asked about
an image and use them as accurate knowledge for answering further questions of
the same image. We are fully aware that this setting is not ubiquitously
applicable, and in a more common setting one should assume the questions are
asked separately and they cannot be gathered to obtain a knowledge base.
Nonetheless, we use this method as an evidence to demonstrate our observation
that the bottleneck effect is more severe on the feature extraction part than
it is on the knowledge reasoning part. We show significant gaps when using the
same reasoning model with 1) ground-truth features; 2) statistical features; 3)
detected features from completely learned detectors, and analyze what these
gaps mean to researches on visual reasoning topics. Our model with the
statistical features achieves the 2nd place in the GQA Challenge 2019.
