Toward a Human-Level Video Understanding Intelligence
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Abstract (arXiv)
We aim to develop an AI agent that can watch video clips and have a conversation with human about the video story. Developing video understanding intelligence is a significantly challenging task, and evaluation methods for adequately measuring and analyzing the progress of AI agent are lacking as well. In this paper, we propose the Video Turing Test to provide effective and practical assessments of video understanding intelligence as well as human-likeness evaluation of AI agents. We define a general format and procedure of the Video Turing Test and present a case study to confirm the effectiveness and usefulness of the proposed test.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 8 of its 21 references that are in the map. First posted on arXiv 8 Oct 2021.
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References · 8
- 2014CIDEr: Consensus-based Image Description Evaluationcited by 2,046
- 2020Towards a Human-like Open-Domain Chatbotcited by 470
- 2018TVQA: Localized, Compositional Video Question Answeringcited by 467
- 2020TVR: A Large-Scale Dataset for Video-Subtitle Moment Retrievalcited by 215
- 2019Audio-Visual Scene-Aware Dialogcited by 123
- 2020DramaQA: Character-Centered Video Story Understanding with Hierarchical QAcited by 45
- 2019Constructing Hierarchical Q&A Datasets for Video Story Understandingcited by 3
- 2021Mounting Video Metadata on Transformer-based Language Model for Open-ended Video Question Answeringcited by 1
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