Constructing Hierarchical Q A Datasets for Video Story Understanding
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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.
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