Summary of A Survey Of Video Datasets For Grounded Event Understanding, by Kate Sanders and Benjamin Van Durme
A Survey of Video Datasets for Grounded Event Understanding
by Kate Sanders, Benjamin Van Durme
First submitted to arxiv on: 14 Jun 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 A novel multimodal AI system must excel at common-sense reasoning, akin to human visual understanding. Existing benchmarks primarily focus on specialized tasks like retrieval or question-answering (QA), neglecting the ability to identify and model “things happening” or events. Recent work has explored video analogues to textual event extraction but lacks a unified task definition and dataset. This paper surveys 105 video datasets requiring event understanding, analyzing their contributions to robust event understanding in video. We propose suggestions for dataset curation and task framing, emphasizing the temporal nature of video events and visual ambiguity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how computers can understand videos better. Right now, computer vision systems are not very good at understanding what’s happening in a video, like people talking or cars moving. Researchers have been working on this problem for over 10 years, but they haven’t agreed on the best way to solve it. In this study, we looked at 105 different video datasets that require computers to understand events in videos. We want to figure out how to make computer vision systems better at understanding what’s happening in a video. |
Keywords
» Artificial intelligence » Question answering
