Constructing Hierarchical Q&A Datasets for Video Story Understanding

Understand

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.