The classifier judged this item not relevant to AI security, so it is left out of lists, feeds, the API and dataset releases. If that is wrong, report a correction on this page.
InfoResearchPeer-reviewed
ZUMA: Training-Free Zero-Shot Unified Multimodal Anomaly Detection
- Published
- Record updated
Summary
ZUMA is a training-free framework for zero-shot multimodal anomaly detection that uses CLIP to identify anomalies in 2D images, 3D point clouds, or both. It bridges the domain gap between CLIP's pretraining space and point clouds through cross-domain calibration, then applies dynamic semantic interaction with natural language anchors to separate anomaly regions. The authors report state-of-the-art results on MVTec 3D-AD and Eyecandies, and a fine-tuned variant, ZUMA-FT, adds improvements with 6.75 M learnable parameters.