UN
yuanzhao-CVLAB/UniMMAD
[CVPR 2026] Official Implementation of UniMMAD: Unified Multi-Modal and Multi-Class Anomaly Detection via MoE-Driven Feature Decompression
207 21 +2/wk
GitHub
anomaly-detection mixture-of-experts multimodal
Trend
3
Star & Fork Trend (16 data points)
Stars
Forks
Multi-Source Signals
Growth Velocity
yuanzhao-CVLAB/UniMMAD has +2 stars this period . 7-day velocity: 2.5%.
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Signal-backed technical analysis will be available soon.
| Metric | UniMMAD | DiffuseStyleGesture | fcc-ai-engineering-aws | openclaw.net |
|---|---|---|---|---|
| Stars | 207 | 208 | 197 | 196 |
| Forks | 21 | 31 | 120 | 32 |
| Weekly Growth | +2 | +0 | +0 | +1 |
| Language | Python | Python | Jupyter Notebook | C# |
| Sources | 1 | 1 | 1 | 1 |
| License | N/A | MIT | MIT | MIT |
Capability Radar vs DiffuseStyleGesture
UniMMAD
DiffuseStyleGesture
Maintenance Activity 100
Last code push 7 days ago.
Community Engagement 51
Fork-to-star ratio: 10.1%. Active community forking and contributing.
Issue Burden 70
Issue data not yet available.
Growth Momentum 98
+2 stars this period — 0.97% growth rate.
License Clarity 30
No clear license detected — proceed with caution.
Risk scores are computed from real-time repository data. Higher scores indicate healthier metrics.