Abstract
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.
Abstract (translated)
视频理解正在成为研究人工智能的新范式。问答（Q&A）作为衡量视频理解智能水平的通用基准。虽然之前的几项研究已经提出了视频问答任务的数据集，但这些数据集并没有真正包含故事层面的理解，因此在问题难度方面存在高度偏差和缺乏差异。本文提出了一种建立Q&A数据集的层次化方法，即层次难度水平。我们介绍了视频故事理解的三个标准，即存储容量、逻辑复杂性和数据信息知识智慧金字塔。我们讨论了如何用这些标准构建三维地图，作为评估与视频故事理解相关的智力水平的指标。
URL
https://arxiv.org/abs/1904.00623
