Constructing Hierarchical Q&A Datasets for Video Story Understanding

Abstract

(arXiv.org: https://128.84.21.199/abs/1904.00623) 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 buildingQ&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-dimensionalmap constructed from these criteria can be used as a metric for evaluating the levels of intelligence relating to video story understanding.