Constructing Hierarchical Q&A Datasets for Video Story Understanding
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.
- Publication:
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arXiv e-prints
- Pub Date:
- April 2019
- DOI:
- 10.48550/arXiv.1904.00623
- arXiv:
- arXiv:1904.00623
- Bibcode:
- 2019arXiv190400623H
- Keywords:
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- Computer Science - Artificial Intelligence;
- Computer Science - Computer Vision and Pattern Recognition;
- Computer Science - Machine Learning;
- Computer Science - Multimedia
- E-Print:
- Accepted to AAAI 2019 Spring Symposium Series : Story-Enabled Intelligence