Summary of Injecting Explainability and Lightweight Design Into Weakly Supervised Video Anomaly Detection Systems, by Wen-dong Jiang et al.
Injecting Explainability and Lightweight Design into Weakly Supervised Video Anomaly Detection Systems
by Wen-Dong Jiang, Chih-Yung Chang, Hsiang-Chuan Chang, Ji-Yuan Chen, Diptendu Sinha Roy
First submitted to arxiv on: 28 Dec 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 Medium Difficulty summary: The Weakly Supervised Monitoring Anomaly Detection (WSMAD) paper presents a novel approach to identifying anomalies in smart city monitoring. Existing multimodal methods often fail to meet real-time and interpretability requirements on edge devices due to their complexity. TCVADS, a Two-stage Cross-modal Video Anomaly Detection System, leverages knowledge distillation and cross-modal contrastive learning to achieve efficient, accurate, and interpretable anomaly detection. The system operates in two stages: coarse-grained rapid classification using time series analysis and fine-grained detailed analysis with convolutional networks. Experimental results show that TCVADS outperforms existing methods in model performance, detection efficiency, and interpretability, making it a valuable contribution to smart city monitoring applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This paper is about finding unusual events in cities using cameras and computers. The problem is that most solutions are too complicated and slow for devices that need to make decisions quickly. To solve this, the researchers created a new system called TCVADS that can quickly identify unusual events and explain why it found them. The system uses two steps: first, it looks at short periods of time to see if anything strange is happening, and then it takes a closer look at any suspicious activity. This system works better than other methods and could be used in cities to make decisions about what’s happening. |
Keywords
» Artificial intelligence » Anomaly detection » Classification » Knowledge distillation » Supervised » Time series
