Cover Image

MuSc: Zero-Shot Industrial Anomaly Classification and Segmentation with Mutual Scoring of the Unlabeled Images

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Li, Xurui, Huang, Ziming, Xue, Feng, Zhou, Yu
Format:	Preprint
Published:	2024
Subjects:	Computer Vision and Pattern Recognition
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

MuSc-V2: Zero-Shot Multimodal Industrial Anomaly Classification and Segmentation with Mutual Scoring of Unlabeled Samples
by: Li, Xurui, et al.
Published: (2025)

AnoRefiner: Anomaly-Aware Group-Wise Refinement for Zero-Shot Industrial Anomaly Detection
by: Huang, Dayou, et al.
Published: (2025)

AnomalyNCD: Towards Novel Anomaly Class Discovery in Industrial Scenarios
by: Huang, Ziming, et al.
Published: (2024)

SeaS: Few-shot Industrial Anomaly Image Generation with Separation and Sharing Fine-tuning
by: Dai, Zhewei, et al.
Published: (2024)

RoBiS: Robust Binary Segmentation for High-Resolution Industrial Images
by: Li, Xurui, et al.
Published: (2025)

Zero-Shot Industrial Anomaly Segmentation with Image-Aware Prompt Generation
by: Park, SoYoung, et al.
Published: (2025)

Interpreting and Analysing CLIP's Zero-Shot Image Classification via Mutual Knowledge
by: Sammani, Fawaz, et al.
Published: (2024)

MeshSegmenter: Zero-Shot Mesh Semantic Segmentation via Texture Synthesis
by: Zhong, Ziming, et al.
Published: (2024)

Image to Pseudo-Episode: Boosting Few-Shot Segmentation by Unlabeled Data
by: Zhang, Jie, et al.
Published: (2024)

On the Problem of Consistent Anomalies in Zero-Shot Industrial Anomaly Detection
by: Le-Gia, Tai, et al.
Published: (2025)

AFR-CLIP: Enhancing Zero-Shot Industrial Anomaly Detection with Stateless-to-Stateful Anomaly Feature Rectification
by: Yuan, Jingyi, et al.
Published: (2025)

AnomalyPainter: Vision-Language-Diffusion Synergy for Zero-Shot Realistic and Diverse Industrial Anomaly Synthesis
by: Lai, Zhangyu, et al.
Published: (2025)

StackCLIP: Clustering-Driven Stacked Prompt in Zero-Shot Industrial Anomaly Detection
by: Hou, Yanning, et al.
Published: (2025)

Dual-Image Enhanced CLIP for Zero-Shot Anomaly Detection
by: Zhang, Zhaoxiang, et al.
Published: (2024)

Text and Image Are Mutually Beneficial: Enhancing Training-Free Few-Shot Classification with CLIP
by: Li, Yayuan, et al.
Published: (2024)

ZSG-IAD: A Multimodal Framework for Zero-Shot Grounded Industrial Anomaly Detection
by: Chen, Qiuhui, et al.
Published: (2026)

LGD: Leveraging Generative Descriptions for Zero-Shot Referring Image Segmentation
by: Li, Jiachen, et al.
Published: (2025)

SSVP: Synergistic Semantic-Visual Prompting for Industrial Zero-Shot Anomaly Detection
by: Fu, Chenhao, et al.
Published: (2026)

Few-Shot Anomaly-Driven Generation for Anomaly Classification and Segmentation
by: Gui, Guan, et al.
Published: (2025)

Multi-Granularity Mutual Refinement Network for Zero-Shot Learning
by: Wang, Ning, et al.
Published: (2025)

Referring Industrial Anomaly Segmentation
by: Yue, Pengfei, et al.
Published: (2026)

Will It Zero-Shot?: Predicting Zero-Shot Classification Performance For Arbitrary Queries
by: Robbins, Kevin, et al.
Published: (2026)

MultiADS: Defect-aware Supervision for Multi-type Anomaly Detection and Segmentation in Zero-Shot Learning
by: Sadikaj, Ylli, et al.
Published: (2025)

Prompt-Induced Score Variance in Zero-Shot Binary Vision-Language Safety Classification
by: Weng, Charles, et al.
Published: (2026)

ClipSAM: CLIP and SAM Collaboration for Zero-Shot Anomaly Segmentation
by: Li, Shengze, et al.
Published: (2024)

KAnoCLIP: Zero-Shot Anomaly Detection through Knowledge-Driven Prompt Learning and Enhanced Cross-Modal Integration
by: Li, Chengyuan, et al.
Published: (2025)

MRAD: Zero-Shot Anomaly Detection with Memory-Driven Retrieval
by: Xu, Chaoran, et al.
Published: (2026)

MADS: Multi-Attribute Document Supervision for Zero-Shot Image Classification
by: Qu, Xiangyan, et al.
Published: (2025)

Text as Any-Modality for Zero-Shot Classification by Consistent Prompt Tuning
by: Wu, Xiangyu, et al.
Published: (2025)

Zero-Shot Image Anomaly Detection Using Generative Foundation Models
by: Abdi, Lemar, et al.
Published: (2025)

SAM-LAD: Segment Anything Model Meets Zero-Shot Logic Anomaly Detection
by: Peng, Yun, et al.
Published: (2024)

CLIP meets DINO for Tuning Zero-Shot Classifier using Unlabeled Image Collections
by: Imam, Mohamed Fazli, et al.
Published: (2024)

Generate Aligned Anomaly: Region-Guided Few-Shot Anomaly Image-Mask Pair Synthesis for Industrial Inspection
by: Lu, Yilin, et al.
Published: (2025)

Deep Industrial Image Anomaly Detection: A Survey
by: Liu, Jiaqi, et al.
Published: (2023)

Defect-aware Hybrid Prompt Optimization via Progressive Tuning for Zero-Shot Multi-type Anomaly Detection and Segmentation
by: Nazer, Nadeem, et al.
Published: (2025)

Hard-normal Example-aware Template Mutual Matching for Industrial Anomaly Detection
by: Chen, Zixuan, et al.
Published: (2023)

Exploiting Unlabeled Structures through Task Consistency Training for Versatile Medical Image Segmentation
by: Zhu, Shengqian, et al.
Published: (2025)

Investigating the Semantic Robustness of CLIP-based Zero-Shot Anomaly Segmentation
by: Stangl, Kevin, et al.
Published: (2024)

Multi-Scale Memory Comparison for Zero-/Few-Shot Anomaly Detection
by: Huang, Chaoqin, et al.
Published: (2023)

Prototype-Guided Curriculum Learning for Zero-Shot Learning
by: Wang, Lei, et al.
Published: (2025)