Summary of Beyond Visual Appearances: Privacy-sensitive Objects Identification Via Hybrid Graph Reasoning, by Zhuohang Jiang et al.
Beyond Visual Appearances: Privacy-sensitive Objects Identification via Hybrid Graph Reasoning
by Zhuohang Jiang, Bingkui Tong, Xia Du, Ahmed Alhammadi, Jizhe Zhou
First submitted to arxiv on: 18 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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 The POI task involves allocating bounding boxes for privacy-sensitive objects in a scene, where an object’s privacy class is determined by its context rather than visual appearance. The key to POI is interpreting the task as a visual reasoning problem aimed at determining each object’s privacy in the scene. A proposed framework, PrivacyGuard, consists of three stages: structuring, data augmentation, and hybrid graph generation & reasoning. These stages aim to balance the distribution of privacy classes, accelerate reasoning, and facilitate capturing subtle context changes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The POI task is like identifying objects with sensitive information in a picture. Instead of just looking at what’s in the picture, you need to consider the whole scene to decide if it’s private or not. The goal is to create a system that can accurately identify these private objects by understanding their context. |
Keywords
» Artificial intelligence » Data augmentation
