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.

本文提出了一种分层方法来构建 Q&A 数据集，介绍了视频故事理解的三个标准：记忆容量、逻辑复杂度和 DIKW 金字塔，并讨论了如何从这些标准构建三维图来评估与视频故事理解相关的智能水平。