Graph-Based Knowledge Driven Approach for Violence Detection
Bibliographic record
Abstract
Automatically identifying violence in videos is critical, and combining visual and audio cues is often the most effective approach that provides complementary information for violence detection. However, existing research on fusing these cues is computationally demanding and limited. To address this issue, we propose a novel fused vision-based graph neural network (FV-GNN) for violence detection using audiovisual information. This approach combines local and global features from both audio and video, leveraging a residual learning strategy to extract the most informative cues. Furthermore, FV-GNN utilizes dynamic graph filtering to analyze the inherent relationships between audio and video samples, enhancing violence recognition. The network consists of three branches: integrated, specialized, and scoring. The integrated branch captures long-range dependencies based on similarity, while the specialized branch focuses on local positional relationships. Finally, the scoring branch assesses the predicted violence likelihood against reality. We extensively explored the use of graphs for modeling temporal context in videos and found FV-GNN to be particularly well-suited for real-time violence detection. Our experiments demonstrate that FV-GNN outperforms current state-of-the-art methods on the XD-Violence datasets.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".