Summary of Weakly Supervised Video Anomaly Detection and Localization with Spatio-temporal Prompts, by Peng Wu et al.
Weakly Supervised Video Anomaly Detection and Localization with Spatio-Temporal Prompts
by Peng Wu, Xuerong Zhou, Guansong Pang, Zhiwei Yang, Qingsen Yan, Peng Wang, Yanning Zhang
First submitted to arxiv on: 12 Aug 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 STPrompt method learns spatio-temporal prompt embeddings for weakly supervised video anomaly detection and localization (WSVADL) using pre-trained vision-language models (VLMs). This novel approach employs a two-stream network structure, with one stream focusing on the temporal dimension and the other on the spatial dimension. By leveraging learned knowledge from pre-trained VLMs and incorporating natural motion priors from raw videos, the model identifies specific local regions of anomalies, enabling accurate video anomaly detection while mitigating the influence of background information. The method achieves state-of-the-art performance on three public benchmarks for WSVADL without relying on detailed spatio-temporal annotations or auxiliary object detection/tracking. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to detect unusual events in videos using weak labels, which are only coarse video-level annotations. Current methods focus on the whole frame and can be misled by background information. The proposed STPrompt method uses pre-trained models that understand both pictures and text to find local anomalies in space and time. It does this without needing detailed labels or tracking objects. The results show it’s better than previous methods on three benchmark datasets. |
Keywords
» Artificial intelligence » Anomaly detection » Object detection » Prompt » Supervised » Tracking
