Summary of Ytcommentqa: Video Question Answerability in Instructional Videos, by Saelyne Yang et al.
YTCommentQA: Video Question Answerability in Instructional Videos
by Saelyne Yang, Sunghyun Park, Yunseok Jang, Moontae Lee
First submitted to arxiv on: 30 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research paper tackles the challenge of answering questions that go beyond the information provided in instructional videos. While previous models have focused on generating answers within the video content, this study recognizes the need to determine whether a question can be answered by the video at all. To address this issue, the authors present the YTCommentQA dataset, which contains naturally-generated questions from YouTube categorized by their answerability and required modality (visual, script, or both). The dataset is designed to help computational models understand the complex interplay between visual and textual information in videos. By developing a better understanding of video reasoning, this research aims to improve Video Question Answering (Video QA) tasks and provide more accurate answers to users’ questions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study explores how we can answer questions that are not just about what’s happening in an instructional video, but also ask about things that aren’t shown. To help computers better understand these types of questions, the researchers created a big dataset called YTCommentQA. This dataset contains lots of questions that people have asked on YouTube, and it tells us whether each question can be answered by looking at the video or if we need to look somewhere else (like reading the script). The goal is to make computers better at answering questions about videos, which will help us learn more from these types of resources. |
Keywords
* Artificial intelligence * Question answering
