G2SF: Geometry-Guided Score Fusion for Multimodal Industrial Anomaly Detection | AD Research Hub

AD Research Hub

Dashboard People Papers Workshops Datasets Research Map

⌘K

AD Research Hub — Anomaly Detection in Computer Vision

← Back to Papers

G2SF: Geometry-Guided Score Fusion for Multimodal Industrial Anomaly Detection

Authors

Chengyu Tao, Xuanming Cao, Juan Du

ICCV-2025direct anomaly

Score

24

Tags

anomaly detectionindustrial anomaly

Links

Cite

Related Papers

RareCLIP: Rarity-aware Online Zero-shot Industrial Anomaly Detection

Jianfang He, Min Cao, Silong Peng +1

ICCV-2025direct anomaly24

anomaly detectionindustrial anomaly

CLIPZero-shot

PDF

Real-IAD D3: A Real-World 2D/Pseudo-3D/3D Dataset for Industrial Anomaly Detection

Wenbing Zhu, Lidong Wang, Ziqing Zhou +14

CVPR-2025direct anomaly24

anomaly detectionindustrial anomaly

Real-IAD

PDF

Mixture of Experts Guided by Gaussian Splatters Matters: A new Approach to Weakly-Supervised Video Anomaly Detection

Giacomo D' Amicantonio, Snehashis Majhi, Quan Kong +4

ICCV-2025direct anomaly24

anomaly detectionvideo anomaly

PDF

ReMP-AD: Retrieval-enhanced Multi-modal Prompt Fusion for Few-Shot Industrial Visual Anomaly Detection

Hongchi Ma, Guanglei Yang, Debin Zhao +2

ICCV-2025direct anomaly24

anomaly detectionvisual anomaly

Few-shot

PDF

Sequential keypoint density estimator: an overlooked baseline of skeleton-based video anomaly detection

Anja Delić, Matej Grcic, Siniša Šegvić

ICCV-2025direct anomaly24

anomaly detectionvideo anomaly

PDF

SeaS: Few-shot Industrial Anomaly Image Generation with Separation and Sharing Fine-tuning

Zhewei Dai, Shilei Zeng, Haotian Liu +3

ICCV-2025direct anomaly14

industrial anomaly

U-NetVAEFew-shot

Sign in to access this content