Cover Image

Dinomaly: The Less Is More Philosophy in Multi-Class Unsupervised Anomaly Detection

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Guo, Jia, Lu, Shuai, Zhang, Weihang, Chen, Fang, Li, Huiqi, Liao, Hongen
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

One Dinomaly2 Detect Them All: A Unified Framework for Full-Spectrum Unsupervised Anomaly Detection
by: Guo, Jia, et al.
Published: (2025)

Absolute-Unified Multi-Class Anomaly Detection via Class-Agnostic Distribution Alignment
by: Guo, Jia, et al.
Published: (2024)

A Feature Shuffling and Restoration Strategy for Universal Unsupervised Anomaly Detection
by: Luo, Wei, et al.
Published: (2026)

CLIPin: A Non-contrastive Plug-in to CLIP for Multimodal Semantic Alignment
by: Yang, Shengzhu, et al.
Published: (2025)

Memory-Augmented Dual-Decoder Networks for Multi-Class Unsupervised Anomaly Detection
by: Xing, Jingyu, et al.
Published: (2025)

Dual Prototype-Conditioned Diffusion Model for Scalable Multi-Class Unsupervised Anomaly Detection in Large Category Spaces
by: Feng, Yaoxuan, et al.
Published: (2026)

RET-CLIP: A Retinal Image Foundation Model Pre-trained with Clinical Diagnostic Reports
by: Du, Jiawei, et al.
Published: (2024)

ViLReF: An Expert Knowledge Enabled Vision-Language Retinal Foundation Model
by: Yang, Shengzhu, et al.
Published: (2024)

RcAE: Recursive Reconstruction Framework for Unsupervised Industrial Anomaly Detection
by: Wu, Rongcheng, et al.
Published: (2025)

Deep Expert Injection for Anchoring Retinal VLMs with Domain-Specific Knowledge
by: Lu, Shuai, et al.
Published: (2026)

One-for-More: Continual Diffusion Model for Anomaly Detection
by: Li, Xiaofan, et al.
Published: (2025)

Exploring Plain ViT Reconstruction for Multi-class Unsupervised Anomaly Detection
by: Zhang, Jiangning, et al.
Published: (2023)

Correcting Deviations from Normality: A Reformulated Diffusion Model for Multi-Class Unsupervised Anomaly Detection
by: Beizaee, Farzad, et al.
Published: (2025)

Achieving More with Less: Additive Prompt Tuning for Rehearsal-Free Class-Incremental Learning
by: Chen, Haoran, et al.
Published: (2025)

Prior Normality Prompt Transformer for Multi-class Industrial Image Anomaly Detection
by: Yao, Haiming, et al.
Published: (2024)

Interleaving One-Class and Weakly-Supervised Models with Adaptive Thresholding for Unsupervised Video Anomaly Detection
by: Nie, Yongwei, et al.
Published: (2024)

MambaAD: Exploring State Space Models for Multi-class Unsupervised Anomaly Detection
by: He, Haoyang, et al.
Published: (2024)

AnomalySD: Few-Shot Multi-Class Anomaly Detection with Stable Diffusion Model
by: Yan, Zhenyu, et al.
Published: (2024)

Anno-incomplete Multi-dataset Detection
by: Xu, Yiran, et al.
Published: (2024)

Pyramid-based Mamba Multi-class Unsupervised Anomaly Detection
by: Iqbal, Nasar, et al.
Published: (2025)

LIME: Less Is More for MLLM Evaluation
by: Zhu, King, et al.
Published: (2024)

Native Intelligence Emerges from Large-Scale Clinical Practice: A Retinal Foundation Model with Deployment Efficiency
by: Guo, Jia, et al.
Published: (2025)

ABounD: Adversarial Boundary-Driven Few-Shot Learning for Multi-Class Anomaly Detection
by: Deng, Runzhi, et al.
Published: (2025)

Collaborative Reconstruction and Repair for Multi-class Industrial Anomaly Detection
by: Wang, Qishan, et al.
Published: (2025)

Structural Teacher-Student Normality Learning for Multi-Class Anomaly Detection and Localization
by: Deng, Hanqiu, et al.
Published: (2024)

Exploring Multimodal Prompts For Unsupervised Continuous Anomaly Detection
by: Zhou, Mingle, et al.
Published: (2026)

Less is More: Efficient Point Cloud Reconstruction via Multi-Head Decoders
by: Alonso, Pedro, et al.
Published: (2025)

Learning Unified Reference Representation for Unsupervised Multi-class Anomaly Detection
by: He, Liren, et al.
Published: (2024)

One-Step Diffusion with Inverse Residual Fields for Unsupervised Industrial Anomaly Detection
by: Zhang, Boan, et al.
Published: (2026)

Component-aware Unsupervised Logical Anomaly Generation for Industrial Anomaly Detection
by: Tong, Xuan, et al.
Published: (2025)

Salvaging the Overlooked: Leveraging Class-Aware Contrastive Learning for Multi-Class Anomaly Detection
by: Fan, Lei, et al.
Published: (2024)

Dual-Modeling Decouple Distillation for Unsupervised Anomaly Detection
by: Liu, Xinyue, et al.
Published: (2024)

UniMMAD: Unified Multi-Modal and Multi-Class Anomaly Detection via MoE-Driven Feature Decompression
by: Zhao, Yuan, et al.
Published: (2025)

Enhancing Multi-Class Anomaly Detection via Diffusion Refinement with Dual Conditioning
by: Zhan, Jiawei, et al.
Published: (2024)

Boosting Global-Local Feature Matching via Anomaly Synthesis for Multi-Class Point Cloud Anomaly Detection
by: Cheng, Yuqi, et al.
Published: (2025)

LLM-RG4: Flexible and Factual Radiology Report Generation across Diverse Input Contexts
by: Wang, Zhuhao, et al.
Published: (2024)

Fence off Anomaly Interference: Cross-Domain Distillation for Fully Unsupervised Anomaly Detection
by: Liu, Xinyue, et al.
Published: (2025)

Context Enhancement with Reconstruction as Sequence for Unified Unsupervised Anomaly Detection
by: Yang, Hui-Yue, et al.
Published: (2024)

SmartHome-Bench: A Comprehensive Benchmark for Video Anomaly Detection in Smart Homes Using Multi-Modal Large Language Models
by: Zhao, Xinyi, et al.
Published: (2025)

SP-Mamba: Spatial-Perception State Space Model for Unsupervised Medical Anomaly Detection
by: Pan, Rui, et al.
Published: (2025)