Align3D-AD improves zero-shot 3D anomaly detection by cross-modal feature alignment from RGB guidance and dual-prompt contrastive alignment to capture complementary semantics.
Winclip: Zero-/few-shot anomaly classification and segmentation
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.CV 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
EAGLE achieves up to 94.4% anomaly detection accuracy on MVTec-AD and 88.1% on VisA by guiding frozen MLLMs with expert-derived thresholds and confidence-aware attention without parameter updates.
citing papers explorer
-
Align3D-AD: Cross-Modal Feature Alignment and Dual-Prompt Learning for Zero-shot 3D Anomaly Detection
Align3D-AD improves zero-shot 3D anomaly detection by cross-modal feature alignment from RGB guidance and dual-prompt contrastive alignment to capture complementary semantics.
-
EAGLE: Expert-Augmented Attention Guidance for Tuning-Free Industrial Anomaly Detection in Multimodal Large Language Models
EAGLE achieves up to 94.4% anomaly detection accuracy on MVTec-AD and 88.1% on VisA by guiding frozen MLLMs with expert-derived thresholds and confidence-aware attention without parameter updates.