Summary of Missiongnn: Hierarchical Multimodal Gnn-based Weakly Supervised Video Anomaly Recognition with Mission-specific Knowledge Graph Generation, by Sanggeon Yun et al.
MissionGNN: Hierarchical Multimodal GNN-based Weakly Supervised Video Anomaly Recognition with Mission-Specific Knowledge Graph Generation
by Sanggeon Yun, Ryozo Masukawa, Minhyoung Na, Mohsen Imani
First submitted to arxiv on: 27 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 paper introduces a novel hierarchical graph neural network (GNN) based model called MissionGNN that addresses challenges in Video Anomaly Detection (VAD) and Video Anomaly Recognition (VAR). The tasks involve identifying and classifying deviations from normal behavior in video data, which is crucial for applications like intelligent surveillance and evidence investigation. The paper proposes a weakly supervised learning approach using a state-of-the-art large language model and a comprehensive knowledge graph to overcome imbalanced data and impractical annotation requirements. This approach enables efficient frame-level training without fixed video segmentation, making it practical for real-time analysis. Experimental results on benchmark datasets demonstrate the model’s performance in VAD and VAR, highlighting its potential to redefine anomaly detection and recognition in video surveillance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about a new way to find unusual things in videos that might be important or useful. It’s hard because there are usually very few unusual things and it would take a long time to look at every frame of the video. The researchers made a special computer model called MissionGNN that can help with this problem. They used some really smart language tools and connected ideas together in a special way. This makes their model much faster and more useful than other ways people have tried before. They tested it on some examples and it worked pretty well, which is important because we need better ways to find unusual things in videos for things like keeping us safe. |
Keywords
* Artificial intelligence * Anomaly detection * Gnn * Graph neural network * Knowledge graph * Large language model * Supervised
