UniADet

Official PyTorch Implementation of One Language-Free Foundation Model Is Enough for Universal Vision Anomaly Detection, 2026.

• “我爱计算机视觉”解读 in Chinese
• "木迅"解读 in Chinese
• alphaxiv blog in English

📢 News

• [2026-02-02]: ✅ UniADet has been independently reproduced by a third-party team, confirming our state-of-the-art results.
• [2026-01-13]: 🚀 Initialized the official UniADet code repository.
• [2026-01-09]: 📄 The UniADet paper is now available on arXiv.

Contents

• Introduction
• UniADet Framework
• Language-Free UniADet with Different Foundation Models
• Comparison with State-of-the-Arts
• Complexity and Efficiency Comparisons
• Ablation Studies
• Comparison with Language-Dependent AnomalyCLIP
• ToDo List
• Citation

📝 Introduction

UniADet is a language-free universal (Zero- and Few-shot) visual anomaly detection framework. It achieves superior performance outperforming state-of-the-art language-dependent Zero- and Few-shot AD models while also exhibiting remarkable simplicity and efficiency.

• We rethink vision-language ADs and find that language prompts and encoders are unnecessary. This insight leads to an embarrassingly simple(language-free+dual-decoupling), efficient(0.015M or 0.02M learnable params), effective (SOTA zero-/few-shot) and general (support VLMs and Pure VMs) framework for universal anomaly detection.
• We fully decouple global anomaly classification and local anomaly segmentation across multi-scale hierarchical features, i.e., learning layer-wise cls/seg weights, effectively mitigating the learning conflict between different feature manifolds and substantially improving AD performance.
• Comprehensive experiments conclusively validate that our approach achieves state-of-the-art zero-shot and few-shot performance. Notably, our few-shot UniADet is the first to outperform full-shot state-of-the-art.

💎 UniADet Framework

📊 Language-Free UniADet with Different Foundation Models

Backbone	Shot	MVTec-AD	VisA	Real-IAD
CLIP (ViT-L/14@336px)	0	92.4 / 42.8	88.0 / 28.0	78.6 / 33.6
CLIP (ViT-L/14@336px)	4	97.7 / 58.8	93.3 / 36.7	84.3 / 37.2
DINOv2 (Register ViT-L/14)	0	93.5 / 50.9	91.3 / 32.7	82.5 / 43.1
DINOv2 (Register ViT-L/14)	4	98.7 / 65.4	96.9 / 45.2	90.3 / 48.5
DINOv3 (ViT-L/16)	0	94.0 / 52.7	91.9 / 32.5	81.2 / 41.6
DINOv3 (ViT-L/16)	4	98.2 / 69.0	97.1 / 45.5	88.5 / 49.8

Note: The performance is mesured by Image-AUROC / Pixel-AUPR, and the same below.

🏆 Comparison with State-of-the-Arts

Methods	Venue	Language-Free	Shots	MVTec	VisA	Real-IAD
UniADet $^‡$	ours	$\color{red}{\checkmark}$	0	93.5 / 50.9	91.3 / 32.7	82.5 / 43.1
WinCLIP	CVPR 23	$\color{green}{✘}$	0	90.4 / 18.2	75.5 / 5.4	67.0 / 3.3
APRIL-GAN	CVPRW 23	$\color{green}{✘}$	0	86.1 / -	78.0 / -	-
AnomalyCLIP	ICLR 24	$\color{green}{✘}$	0	91.6 / 34.5	82.0 / 21.3	69.5 / 26.7
AdaCLIP	ECCV 24	$\color{green}{✘}$	0	90.7 / 39.1	81.7 / 31.0	73.3 / 30.5
VCPCLIP	ECCV 24	$\color{green}{✘}$	0	92.1 / 49.4	83.8 / 30.1	-
Bayes-PFL	CVPR 25	$\color{green}{✘}$	0	92.5 / 48.3	87.0 / 29.8	70.0 / 27.6
AA-CLIP	CVPR 25	$\color{green}{✘}$	0	90.5 / -	84.6 / -	-
FE-CLIP	ICCV 25	$\color{green}{✘}$	0	91.9 / -	84.6 / -	-
FAPrompt	ICCV 25	$\color{green}{✘}$	0	91.9 / -	84.6 / -	-
RareCLIP	ICCV 25	$\color{green}{✘}$	0	91.5 / 46.1	86.1 / 27.0	-
AdaptCLIP	AAAI 26	$\color{green}{✘}$	0	93.5 / 38.3	84.8 / 26.1	74.2 / 28.2

Methods	Venue	Language-Free	Shots	MVTec	VisA	Real-IAD
UniADet $^‡$	ours	$\color{red}{\checkmark}$	1	97.6 / 63.1	95.2 / 42.1	88.7 / 48.4
UniADet $^‡$	ours	$\color{red}{\checkmark}$	2	98.0 / 64.1	96.1 / 44.2	89.0 / 46.7
UniADet $^‡$	ours	$\color{red}{\checkmark}$	4	98.7 / 65.4	96.9 / 45.2	90.3 / 48.5
MetaUAS	NeurIPS 24	$\color{red}{\checkmark}$	1	90.7 / 59.3	81.2 / 42.7	80.0 / 36.6
APRIL-GAN	CVPRW 23	$\color{green}{✘}$	4	92.8 / 54.5	92.6 / 32.2	-
PromptAD	CVPR 24	$\color{green}{✘}$	4	96.6 / 52.9	89.1 / 31.5	-
UniVAD	CVPR 25	$\color{green}{✘}$	1	97.8 / 55.6	93.5 / 42.8	85.1 / 37.6
AdaptCLIP	AAAI 26	$\color{green}{✘}$	1	94.5 / 53.7	90.5 / 38.9	81.8 / 36.6
AdaptCLIP	AAAI 26	$\color{green}{✘}$	2	95.7 / 55.1	92.2 / 40.7	82.9 / 37.8
AdaptCLIP	AAAI 26	$\color{green}{✘}$	4	96.6 / 57.2	93.1 / 41.8	83.9 / 39.1

Methods	Venue	Language-Free	Setting	MVTec	VisA	Real-IAD
Dinomaly	CVPR 25	$\color{red}{\checkmark}$	multi-class (full train set)	99.6 / 69.3	98.7 / 53.2	89.3 / 42.8
UniAD	NeurIPS 24	$\color{red}{\checkmark}$	multi-class (full train set)	96.5 / 44.7	90.8 / 33.6	83.0 / 21.1
MuSc	ICLR 24	$\color{red}{\checkmark}$	online (full test set)	97.8 / 62.7	92.8 / 45.1	-

Important

Note1: If you find that any existing zero-shot/few-shot AD methods are missing from the table above, please feel free to open an issue so we can add them.

Note2: Dinomaly and UniAD are multi-class unsupervised AD algorithms, and they require dataset-specific training with full normal images.

Note3: MuSc is an Online algorithm that requires access to statistics from the entire test dataset to evaluate the current image. Therefore, it is not a strictly zero-shot AD.

🚀 Complexity and Efficiency Comparisons

Shots	Methods	Models	Input Size	# Params (M)	Inf. Time (ms)
0	AdaCLIP	CLIP ViT-L/14@336px	518×518	428.8 + 1.1e+1	107.4
0	AnomalyCLIP	CLIP ViT-L/14@336px	518×518	427.9 + 5.6e+0	70.7
0	Bayes-PFL	CLIP ViT-L/14@336px	518×518	427.9 + 2.7e+1	154.9
0	AdaptCLIP-Zero	CLIP ViT-L/14@336px	518×518	427.9 + 6.0e-1	57.5
0	UniADet $^†$	CLIP ViT-L/14@336px	518×518	342.9 + 1.5e-2	15.7
0	UniADet $^‡$	DINOv2 ViT-L/14	518×518	303.2 + 2.0e-2	41.9
1	InCtrl	CLIP ViT-B-16+240	240×240	208.4 + 3.0e-1	59.0
1	AnomalyCLIP+	CLIP ViT-L/14@336px	518×518	427.9 + 5.6e+0	76.2
1	AdaptCLIP	CLIP ViT-L/14@336px	518×518	342.9 + 1.8e+0	58.7
1	UniADet $^†$	CLIP ViT-L/14@336px	518×518	342.9 + 1.5e-2	22.4
1	UniADet $^‡$	DINOv2 ViT-L/14	518×518	303.2 + 2.0e-2	48.4

Note: The number of learnable parameters (1.5e-3 and 2.0e-3) is not correct for our UniADet $^†$ and UniADet $^‡$, respectively. The correct is 1.5e-2 and 2.0e-2 for UniADet $^†$ and UniADet $^‡$, respectively.

🔍 Ablation Studies

Ablation studies about different components.

No	DCS	DHF	CAA	Shot	MVTec	VisA
0	$\color{green}{✘}$	$\color{green}{✘}$	$\color{green}{✘}$	0	85.4 / 36.4	77.9 / 26.1
1	$\color{red}{\checkmark}$	$\color{green}{✘}$	$\color{green}{✘}$	0	91.8 / 38.3	85.9 / 27.2
2	$\color{red}{\checkmark}$	$\color{red}{\checkmark}$	$\color{green}{✘}$	0	92.2 / 40.7	86.0 / 27.6
3	$\color{red}{\checkmark}$	$\color{red}{\checkmark}$	$\color{red}{\checkmark}$	0	92.4 / 42.8	88.0 / 28.0
4	$\color{red}{\checkmark}$	$\color{red}{\checkmark}$	random	0	91.3 / 41.5	87.5 / 26.6
5	$\color{red}{\checkmark}$	$\color{red}{\checkmark}$	$\color{red}{\checkmark}$	1	95.9 / 54.6	91.3 / 32.5

Note: The ablation studies are conducted by UniADet $^†$ (i.e., using CLIP ViT-L/14@336px).

⚖️ Comparison with Language-Dependent AnomalyCLIP

• Essential Differences

Feature	AnomalyCLIP	UniADet (Ours)
🧠 Paradigm	🔴 Language-Dependent	✅ Language-Free
⚡ Task Decoupling	Shared Cls/Seg Weight $W$	✅ Decoupled Cls/Seg Weights $W_{cls}$, $W_{seg}$
🏗️ Hierarchical Decoupling	Single / Last Layer	✅ Layer-Wise Cls/Seg Weights $W_{cls}^l$, $W_{seg}^l$
🤖 Backbones	CLIP Only	✅ CLIP, DINOv2-R, DINOv3
📉 Params	⚠️ 130M Text-Encoder + 5.6M (Heavy)	🚀 0.015M or 0.020M(Efficient)

Important

Note1: The official AnomalyCLIP team has clarified that their initial multi-layer claim was a code bug; the effective implementation relies only on the last layer.

Note2: A naive extension of AnomalyCLIP to multiple blocks leads to significant performance degradation.

• Commonality

Feature	AnomalyCLIP & UniADet
Core Objective	Learning Normal / Anomaly Weights
Loss Function	CE for Cls and Focal + Dice for Seg
Training Data	Auxiliary Data (e.g., VisA or MVTec)

• Performace on VisA

Shots	Backbones	AnomalyCLIP	UniADet (Ours)
0-Shot	CLIP	82.0 / 21.3	88.0 / 28.0
0-Shot	DINOv2-R	Not supported	91.3 / 32.7
0-Shot	DINOv3	Not supported	91.9 / 32.5
4-Shot	CLIP	Not supported	93.3 / 36.7
4-Shot	DINOv2-R	Not supported	96.9 / 45.2
4-Shot	DINOv3	Not supported	97.1 / 45.5

📌 ToDo List

• release pre-trained UniADet models
• deploy online UniADet Demo on huggingface
• open training and testing code

📖 Citation

If you find this work useful in your research, please consider citing:

@inproceedings{uniadet,
  title={One Language-Free Foundation Model Is Enough for Universal Vision Anomaly Detection},
  author={Gao, Bin-Bin and Wang, Chengjie},
  booktitle={arXiv:2601.05552},
  year={2026}
}

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