  Video understanding is emerging as a new paradigm for studying human-like AI.
Question-and-Answering (Q&A) is used as a general benchmark to measure the
level of intelligence for video understanding. While several previous studies
have suggested datasets for video Q&A tasks, they did not really incorporate
story-level understanding, resulting in highly-biased and lack of variance in
degree of question difficulty. In this paper, we propose a hierarchical method
for building Q&A datasets, i.e. hierarchical difficulty levels. We introduce
three criteria for video story understanding, i.e. memory capacity, logical
complexity, and DIKW (Data-Information-Knowledge-Wisdom) pyramid. We discuss
how three-dimensional map constructed from these criteria can be used as a
metric for evaluating the levels of intelligence relating to video story
understanding.
