Summary of Uncovering What, Why and How: a Comprehensive Benchmark For Causation Understanding Of Video Anomaly, by Hang Du et al.
Uncovering What, Why and How: A Comprehensive Benchmark for Causation Understanding of Video Anomaly
by Hang Du, Sicheng Zhang, Binzhu Xie, Guoshun Nan, Jiayang Zhang, Junrui Xu, Hangyu Liu, Sicong Leng, Jiangming Liu, Hehe Fan, Dajiu Huang, Jing Feng, Linli Chen, Can Zhang, Xuhuan Li, Hao Zhang, Jianhang Chen, Qimei Cui, Xiaofeng Tao
First submitted to arxiv on: 30 Apr 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 The proposed Causation Understanding of Video Anomaly (CUVA) benchmark aims to improve the comprehension of unusual occurrences in videos by introducing a comprehensive dataset and evaluation metric. The CUVA benchmark involves human annotations for the “what”, “why”, and “how” of an anomaly, including natural language explanations for the cause and free text describing the effect. A novel prompt-based method is also introduced as a baseline approach for CUVA. Experimental results demonstrate the superiority of the proposed evaluation metric and method. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Video anomaly understanding (VAU) is important for applications like traffic surveillance and industrial manufacturing. Researchers are working on improving VAU by creating new benchmarks and methods. The CUVA benchmark asks three questions: “what anomaly occurred?”, “why did it happen?”, and “how severe is this abnormal event?”. To answer these questions, human annotations were created to describe the anomaly, its cause, and its effect. A special metric called MMEval helps measure how well AI models can understand video anomalies. The researchers also proposed a prompt-based method that can be used as a starting point for developing better VAU models. |
Keywords
» Artificial intelligence » Prompt
