MoECLIP: Patch-Specialized Experts for Zero-shot Anomaly Detection
Jun Yeong Park, JunYoung Seo, Minji Kang, Yu Rang Park

TL;DR
MoECLIP introduces a patch-specific Mixture-of-Experts architecture with novel orthogonal and ETF-based regularizations, significantly enhancing zero-shot anomaly detection across diverse datasets.
Contribution
It proposes a patch-level adaptive MoE architecture with innovative regularizations to prevent redundancy, improving zero-shot anomaly detection performance.
Findings
Outperforms state-of-the-art methods on 14 benchmark datasets.
Effective patch-level specialization improves anomaly detection accuracy.
Demonstrates robustness across industrial and medical domains.
Abstract
The CLIP model's outstanding generalization has driven recent success in Zero-Shot Anomaly Detection (ZSAD) for detecting anomalies in unseen categories. The core challenge in ZSAD is to specialize the model for anomaly detection tasks while preserving CLIP's powerful generalization capability. Existing approaches attempting to solve this challenge share the fundamental limitation of a patch-agnostic design that processes all patches monolithically without regard for their unique characteristics. To address this limitation, we propose MoECLIP, a Mixture-of-Experts (MoE) architecture for the ZSAD task, which achieves patch-level adaptation by dynamically routing each image patch to a specialized Low-Rank Adaptation (LoRA) expert based on its unique characteristics. Furthermore, to prevent functional redundancy among the LoRA experts, we introduce (1) Frozen Orthogonal Feature Separation…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAnomaly Detection Techniques and Applications · Domain Adaptation and Few-Shot Learning · Adversarial Robustness in Machine Learning
