Saved in:
| Main Author: | Zhu, Ning |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.23766 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Dual Distillation for Few-Shot Anomaly Detection
by: Dong, Le, et al.
Published: (2026)
by: Dong, Le, et al.
Published: (2026)
AnomalyAgent: Training-Free Agentic Models for Zero-/Few-Shot Anomaly Detection
by: Zhang, Yi, et al.
Published: (2026)
by: Zhang, Yi, et al.
Published: (2026)
PA-CLIP: Enhancing Zero-Shot Anomaly Detection through Pseudo-Anomaly Awareness
by: Pan, Yurui, et al.
Published: (2025)
by: Pan, Yurui, et al.
Published: (2025)
DevPrompt: Deviation-Based Prompt Learning for One-Normal ShotImage Anomaly Detection
by: Poudineh, Morteza, et al.
Published: (2026)
by: Poudineh, Morteza, et al.
Published: (2026)
One Language-Free Foundation Model Is Enough for Universal Vision Anomaly Detection
by: Gao, Bin-Bin, et al.
Published: (2026)
by: Gao, Bin-Bin, et al.
Published: (2026)
AREPAS: Anomaly Detection in Fine-Grained Anatomy with Reconstruction-Based Semantic Patch-Scoring
by: Mitic, Branko, et al.
Published: (2025)
by: Mitic, Branko, et al.
Published: (2025)
SSVP: Synergistic Semantic-Visual Prompting for Industrial Zero-Shot Anomaly Detection
by: Fu, Chenhao, et al.
Published: (2026)
by: Fu, Chenhao, et al.
Published: (2026)
AnyAnomaly: Zero-Shot Customizable Video Anomaly Detection with LVLM
by: Ahn, Sunghyun, et al.
Published: (2025)
by: Ahn, Sunghyun, et al.
Published: (2025)
InCTRLv2: Generalist Residual Models for Few-Shot Anomaly Detection and Segmentation
by: Zhu, Jiawen, et al.
Published: (2026)
by: Zhu, Jiawen, et al.
Published: (2026)
On the Problem of Consistent Anomalies in Zero-Shot Industrial Anomaly Detection
by: Le-Gia, Tai, et al.
Published: (2025)
by: Le-Gia, Tai, et al.
Published: (2025)
On the Problem of Consistent Anomalies in Zero-Shot Anomaly Detection
by: Le-Gia, Tai
Published: (2025)
by: Le-Gia, Tai
Published: (2025)
Beyond Reconstruction: Reconstruction-to-Vector Diffusion for Hyperspectral Anomaly Detection
by: Xiang, Jijun, et al.
Published: (2026)
by: Xiang, Jijun, et al.
Published: (2026)
Efficient Odd-One-Out Anomaly Detection
by: Chito, Silvio, et al.
Published: (2025)
by: Chito, Silvio, et al.
Published: (2025)
FiLo++: Zero-/Few-Shot Anomaly Detection by Fused Fine-Grained Descriptions and Deformable Localization
by: Gu, Zhaopeng, et al.
Published: (2025)
by: Gu, Zhaopeng, et al.
Published: (2025)
AF-CLIP: Zero-Shot Anomaly Detection via Anomaly-Focused CLIP Adaptation
by: Fang, Qingqing, et al.
Published: (2025)
by: Fang, Qingqing, et al.
Published: (2025)
One Polyp Identifies All: One-Shot Polyp Segmentation with SAM via Cascaded Priors and Iterative Prompt Evolution
by: Mao, Xinyu, et al.
Published: (2025)
by: Mao, Xinyu, et al.
Published: (2025)
Semantic One-Dimensional Tokenizer for Image Reconstruction and Generation
by: Qu, Yunpeng, et al.
Published: (2026)
by: Qu, Yunpeng, et al.
Published: (2026)
Beyond Normal References: Discriminative Few-Shot Anomaly Detection
by: Wang, Huan, et al.
Published: (2026)
by: Wang, Huan, et al.
Published: (2026)
MRAD: Zero-Shot Anomaly Detection with Memory-Driven Retrieval
by: Xu, Chaoran, et al.
Published: (2026)
by: Xu, Chaoran, et al.
Published: (2026)
Bayesian Prompt Flow Learning for Zero-Shot Anomaly Detection
by: Qu, Zhen, et al.
Published: (2025)
by: Qu, Zhen, et al.
Published: (2025)
Dual-Image Enhanced CLIP for Zero-Shot Anomaly Detection
by: Zhang, Zhaoxiang, et al.
Published: (2024)
by: Zhang, Zhaoxiang, et al.
Published: (2024)
Zero-Shot Anomaly Detection with Dual-Branch Prompt Selection
by: Wang, Zihan, et al.
Published: (2025)
by: Wang, Zihan, et al.
Published: (2025)
AnoRefiner: Anomaly-Aware Group-Wise Refinement for Zero-Shot Industrial Anomaly Detection
by: Huang, Dayou, et al.
Published: (2025)
by: Huang, Dayou, et al.
Published: (2025)
RcAE: Recursive Reconstruction Framework for Unsupervised Industrial Anomaly Detection
by: Wu, Rongcheng, et al.
Published: (2025)
by: Wu, Rongcheng, et al.
Published: (2025)
Odd-One-Out: Anomaly Detection by Comparing with Neighbors
by: Bhunia, Ankan, et al.
Published: (2024)
by: Bhunia, Ankan, et al.
Published: (2024)
One-for-More: Continual Diffusion Model for Anomaly Detection
by: Li, Xiaofan, et al.
Published: (2025)
by: Li, Xiaofan, et al.
Published: (2025)
Iterative Definition Refinement for Zero-Shot Classification via LLM-Based Semantic Prototype Optimization
by: Rehmat, Naeem, et al.
Published: (2026)
by: Rehmat, Naeem, et al.
Published: (2026)
One Shot Learning for Edge Detection on Point Clouds
by: Tu, Zhikun, et al.
Published: (2026)
by: Tu, Zhikun, et al.
Published: (2026)
Atlas is Your Perfect Context: One-Shot Customization for Generalizable Foundational Medical Image Segmentation
by: Zhang, Ziyu, et al.
Published: (2025)
by: Zhang, Ziyu, et al.
Published: (2025)
Video Anomaly Detection with Semantics-Aware Information Bottleneck
by: Li, Juntong, et al.
Published: (2025)
by: Li, Juntong, et al.
Published: (2025)
AD-DINOv3: Enhancing DINOv3 for Zero-Shot Anomaly Detection with Anomaly-Aware Calibration
by: Yuan, Jingyi, et al.
Published: (2025)
by: Yuan, Jingyi, 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)
by: Yuan, Jingyi, et al.
Published: (2025)
VETime: Vision Enhanced Zero-Shot Time Series Anomaly Detection
by: Yang, Yingyuan, et al.
Published: (2026)
by: Yang, Yingyuan, et al.
Published: (2026)
Multi-Scale Memory Comparison for Zero-/Few-Shot Anomaly Detection
by: Huang, Chaoqin, et al.
Published: (2023)
by: Huang, Chaoqin, et al.
Published: (2023)
Zero-Shot Image Anomaly Detection Using Generative Foundation Models
by: Abdi, Lemar, et al.
Published: (2025)
by: Abdi, Lemar, et al.
Published: (2025)
Few-Shot Anomaly Detection via Category-Agnostic Registration Learning
by: Huang, Chaoqin, et al.
Published: (2024)
by: Huang, Chaoqin, et al.
Published: (2024)
CoPS: Conditional Prompt Synthesis for Zero-Shot Anomaly Detection
by: Chen, Qiyu, et al.
Published: (2025)
by: Chen, Qiyu, et al.
Published: (2025)
Kernel-Aware Graph Prompt Learning for Few-Shot Anomaly Detection
by: Tao, Fenfang, et al.
Published: (2024)
by: Tao, Fenfang, et al.
Published: (2024)
COFT-AD: COntrastive Fine-Tuning for Few-Shot Anomaly Detection
by: Liao, Jingyi, et al.
Published: (2024)
by: Liao, Jingyi, et al.
Published: (2024)
Investigating the Semantic Robustness of CLIP-based Zero-Shot Anomaly Segmentation
by: Stangl, Kevin, et al.
Published: (2024)
by: Stangl, Kevin, et al.
Published: (2024)
Similar Items
-
Dual Distillation for Few-Shot Anomaly Detection
by: Dong, Le, et al.
Published: (2026) -
AnomalyAgent: Training-Free Agentic Models for Zero-/Few-Shot Anomaly Detection
by: Zhang, Yi, et al.
Published: (2026) -
PA-CLIP: Enhancing Zero-Shot Anomaly Detection through Pseudo-Anomaly Awareness
by: Pan, Yurui, et al.
Published: (2025) -
DevPrompt: Deviation-Based Prompt Learning for One-Normal ShotImage Anomaly Detection
by: Poudineh, Morteza, et al.
Published: (2026) -
One Language-Free Foundation Model Is Enough for Universal Vision Anomaly Detection
by: Gao, Bin-Bin, et al.
Published: (2026)