Saved in:
| Main Authors: | Hsieh, Yu-Hsuan, Lai, Shang-Hong |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2408.15628 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Component-aware Unsupervised Logical Anomaly Generation for Industrial Anomaly Detection
by: Tong, Xuan, et al.
Published: (2025)
by: Tong, Xuan, et al.
Published: (2025)
3D-CSAD: Untrained 3D Anomaly Detection for Complex Manufacturing Surfaces
by: Cao, Xuanming, et al.
Published: (2024)
by: Cao, Xuanming, et al.
Published: (2024)
Exploring Plain ViT Reconstruction for Multi-class Unsupervised Anomaly Detection
by: Zhang, Jiangning, et al.
Published: (2023)
by: Zhang, Jiangning, et al.
Published: (2023)
Weakly Supervised Video Anomaly Detection with Anomaly-Connected Components and Intention Reasoning
by: Wang, Yu, et al.
Published: (2026)
by: Wang, Yu, et al.
Published: (2026)
PKU-GoodsAD: A Supermarket Goods Dataset for Unsupervised Anomaly Detection and Segmentation
by: Zhang, Jian, et al.
Published: (2023)
by: Zhang, Jian, et al.
Published: (2023)
SoftPatch+: Fully Unsupervised Anomaly Classification and Segmentation
by: Wang, Chengjie, et al.
Published: (2024)
by: Wang, Chengjie, et al.
Published: (2024)
IEC3D-AD: A 3D Dataset of Industrial Equipment Components for Unsupervised Point Cloud Anomaly Detection
by: Guo, Bingyang, et al.
Published: (2025)
by: Guo, Bingyang, et al.
Published: (2025)
Enhancing Object Discovery for Unsupervised Instance Segmentation and Object Detection
by: Feng, Xingyu, et al.
Published: (2025)
by: Feng, Xingyu, et al.
Published: (2025)
SAM-LAD: Segment Anything Model Meets Zero-Shot Logic Anomaly Detection
by: Peng, Yun, et al.
Published: (2024)
by: Peng, Yun, et al.
Published: (2024)
Unsupervised Anomaly Detection via Masked Diffusion Posterior Sampling
by: Wu, Di, et al.
Published: (2024)
by: Wu, Di, et al.
Published: (2024)
VADER: Towards Causal Video Anomaly Understanding with Relation-Aware Large Language Models
by: Cheng, Ying, et al.
Published: (2025)
by: Cheng, Ying, et al.
Published: (2025)
Dual-Modeling Decouple Distillation for Unsupervised Anomaly Detection
by: Liu, Xinyue, et al.
Published: (2024)
by: Liu, Xinyue, et al.
Published: (2024)
Exploring Multimodal Prompts For Unsupervised Continuous Anomaly Detection
by: Zhou, Mingle, et al.
Published: (2026)
by: Zhou, Mingle, et al.
Published: (2026)
One-Step Diffusion with Inverse Residual Fields for Unsupervised Industrial Anomaly Detection
by: Zhang, Boan, et al.
Published: (2026)
by: Zhang, Boan, et al.
Published: (2026)
FDP: A Frequency-Decomposition Preprocessing Pipeline for Unsupervised Anomaly Detection in Brain MRI
by: Li, Hao, et al.
Published: (2025)
by: Li, Hao, et al.
Published: (2025)
Fence off Anomaly Interference: Cross-Domain Distillation for Fully Unsupervised Anomaly Detection
by: Liu, Xinyue, et al.
Published: (2025)
by: Liu, Xinyue, et al.
Published: (2025)
FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data
by: Im, Jiin, et al.
Published: (2024)
by: Im, Jiin, et al.
Published: (2024)
GLAD: Towards Better Reconstruction with Global and Local Adaptive Diffusion Models for Unsupervised Anomaly Detection
by: Yao, Hang, et al.
Published: (2024)
by: Yao, Hang, et al.
Published: (2024)
Vision-Language Models Assisted Unsupervised Video Anomaly Detection
by: Jiang, Yalong, et al.
Published: (2024)
by: Jiang, Yalong, et al.
Published: (2024)
Context Enhancement with Reconstruction as Sequence for Unified Unsupervised Anomaly Detection
by: Yang, Hui-Yue, et al.
Published: (2024)
by: Yang, Hui-Yue, et al.
Published: (2024)
Pyramid-based Mamba Multi-class Unsupervised Anomaly Detection
by: Iqbal, Nasar, et al.
Published: (2025)
by: Iqbal, Nasar, et al.
Published: (2025)
AnomalyFactory: Regard Anomaly Generation as Unsupervised Anomaly Localization
by: Zhao, Ying
Published: (2024)
by: Zhao, Ying
Published: (2024)
One Dinomaly2 Detect Them All: A Unified Framework for Full-Spectrum Unsupervised Anomaly Detection
by: Guo, Jia, et al.
Published: (2025)
by: Guo, Jia, et al.
Published: (2025)
DINO-AD: Unsupervised Anomaly Detection with Frozen DINO-V3 Features
by: Huo, Jiayu, et al.
Published: (2026)
by: Huo, Jiayu, et al.
Published: (2026)
Falcon: Fractional Alternating Cut with Overcoming Minima in Unsupervised Segmentation
by: Zhang, Xiao, et al.
Published: (2025)
by: Zhang, Xiao, et al.
Published: (2025)
Learning Unified Reference Representation for Unsupervised Multi-class Anomaly Detection
by: He, Liren, et al.
Published: (2024)
by: He, Liren, et al.
Published: (2024)
CKNN: Cleansed k-Nearest Neighbor for Unsupervised Video Anomaly Detection
by: Yi, Jihun, et al.
Published: (2024)
by: Yi, Jihun, et al.
Published: (2024)
Dinomaly: The Less Is More Philosophy in Multi-Class Unsupervised Anomaly Detection
by: Guo, Jia, et al.
Published: (2024)
by: Guo, Jia, et al.
Published: (2024)
Scale-Aware Contrastive Reverse Distillation for Unsupervised Medical Anomaly Detection
by: Li, Chunlei, et al.
Published: (2025)
by: Li, Chunlei, et al.
Published: (2025)
The MVTec AD 2 Dataset: Advanced Scenarios for Unsupervised Anomaly Detection
by: Heckler-Kram, Lars, et al.
Published: (2025)
by: Heckler-Kram, Lars, et al.
Published: (2025)
Spatial Autoregressive Modeling of DINOv3 Embeddings for Unsupervised Anomaly Detection
by: Erdil, Ertunc, et al.
Published: (2026)
by: Erdil, Ertunc, et al.
Published: (2026)
RcAE: Recursive Reconstruction Framework for Unsupervised Industrial Anomaly Detection
by: Wu, Rongcheng, et al.
Published: (2025)
by: Wu, Rongcheng, et al.
Published: (2025)
A Feature Shuffling and Restoration Strategy for Universal Unsupervised Anomaly Detection
by: Luo, Wei, et al.
Published: (2026)
by: Luo, Wei, et al.
Published: (2026)
Few-shot Online Anomaly Detection and Segmentation
by: Wei, Shenxing, et al.
Published: (2024)
by: Wei, Shenxing, et al.
Published: (2024)
Few Shot Part Segmentation Reveals Compositional Logic for Industrial Anomaly Detection
by: Kim, Soopil, et al.
Published: (2023)
by: Kim, Soopil, et al.
Published: (2023)
SALAD -- Semantics-Aware Logical Anomaly Detection
by: Fučka, Matic, et al.
Published: (2025)
by: Fučka, Matic, et al.
Published: (2025)
Unsupervised Anomaly Detection on Implicit Shape representations for Sarcopenia Detection
by: Piecuch, Louise, et al.
Published: (2025)
by: Piecuch, Louise, et al.
Published: (2025)
UMAD: Unsupervised Mask-Level Anomaly Detection for Autonomous Driving
by: Bogdoll, Daniel, et al.
Published: (2024)
by: Bogdoll, Daniel, et al.
Published: (2024)
LR-IAD:Mask-Free Industrial Anomaly Detection with Logical Reasoning
by: Zeng, Peijian, et al.
Published: (2025)
by: Zeng, Peijian, et al.
Published: (2025)
Unsupervised Anomaly Detection in Brain MRI via Disentangled Anatomy Learning
by: Yang, Tao, et al.
Published: (2025)
by: Yang, Tao, et al.
Published: (2025)
Similar Items
-
Component-aware Unsupervised Logical Anomaly Generation for Industrial Anomaly Detection
by: Tong, Xuan, et al.
Published: (2025) -
3D-CSAD: Untrained 3D Anomaly Detection for Complex Manufacturing Surfaces
by: Cao, Xuanming, et al.
Published: (2024) -
Exploring Plain ViT Reconstruction for Multi-class Unsupervised Anomaly Detection
by: Zhang, Jiangning, et al.
Published: (2023) -
Weakly Supervised Video Anomaly Detection with Anomaly-Connected Components and Intention Reasoning
by: Wang, Yu, et al.
Published: (2026) -
PKU-GoodsAD: A Supermarket Goods Dataset for Unsupervised Anomaly Detection and Segmentation
by: Zhang, Jian, et al.
Published: (2023)