Summary of Exploring What Why and How: a Multifaceted Benchmark For Causation Understanding Of Video Anomaly, by Hang Du et al.
Exploring What Why and How: A Multifaceted Benchmark for Causation Understanding of Video Anomaly
by Hang Du, Guoshun Nan, Jiawen Qian, Wangchenhui Wu, Wendi Deng, Hanqing Mu, Zhenyan Chen, Pengxuan Mao, Xiaofeng Tao, Jun Liu
First submitted to arxiv on: 10 Dec 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 research delves into the practical aspects of Video Anomaly Understanding (VAU) by introducing a comprehensive benchmark, Exploring the Causation of Video Anomalies (ECVA). The ECVA benchmark is meticulously designed, with each video accompanied by detailed human annotations. Specifically, it involves three sets of human annotations to indicate “what”, “why” and “how” of an anomaly. Building upon this foundation, a novel prompt-based methodology is proposed as a baseline for tackling the intricate challenges posed by ECVA. The approach utilizes “hard prompts” to guide the model to focus on critical parts related to video anomaly segments and “soft prompts” to establish temporal and spatial relationships within these anomaly segments. Additionally, AnomEval, a specialized evaluation metric, is proposed to align closely with human judgment criteria for ECVA. This metric leverages the unique features of the ECVA dataset to provide a more comprehensive and reliable assessment of various video large language models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers created a new way to understand videos that have unusual things in them, like a car accident or a factory malfunction. They made a special set of rules for what makes something unusual, and they tested their ideas using a big collection of videos with human explanations. They also came up with a new way to measure how well computers can understand these unusual events, which is important because it could help us make better decisions in places like traffic control or manufacturing. |
Keywords
» Artificial intelligence » Prompt
