Summary of Vhakg: a Multi-modal Knowledge Graph Based on Synchronized Multi-view Videos Of Daily Activities, by Shusaku Egami et al.
VHAKG: A Multi-modal Knowledge Graph Based on Synchronized Multi-view Videos of Daily Activities
by Shusaku Egami, Takahiro Ugai, Swe Nwe Nwe Htun, Ken Fukuda
First submitted to arxiv on: 27 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 approach to constructing multi-modal knowledge graphs (MMKGs) for videos of daily activities is proposed in this paper. By grounding images and videos into symbols, MMKGs enable knowledge processing and machine learning across modalities. The authors create an MMKG based on simulated videos and represent the content as event-centric knowledge, including fine-grained changes such as bounding boxes within frames. Support tools are also provided for querying the MMKG. As a demonstration of its potential, the paper shows how the MMKG can facilitate benchmarking vision-language models by providing tailored datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research creates a special kind of map that connects pictures and videos to words and symbols. The goal is to make it easier for computers to understand and work with different types of data. The researchers create this “map” using simulated videos of daily activities, like people doing chores or having meals. They also include detailed information about what’s happening in each frame of the video, like where objects are located. This can help machines better understand videos and make decisions based on them. For example, it could be used to test how well computers can recognize what’s happening in a video. |
Keywords
» Artificial intelligence » Grounding » Machine learning » Multi modal
