Focus and Supplement: Dual-Enhanced Vision Transformer for Multi-Class Anomaly Classification

Xurui Li; Enjie Xu; Chenzhou Li; Shilei Zeng; Dayou Huang; Tianyi Ma; Yu Zhou

cs.CVSep 27, 2026

Focus and Supplement: Dual-Enhanced Vision Transformer for Multi-Class Anomaly Classification

Authors: Xurui Li, Enjie Xu, Chenzhou Li, Shilei Zeng, Dayou Huang, Tianyi Ma, Yu Zhou

Organizations: School of Electronic Information and Communications, Huazhong University of Science and Technology

Abstract

Multi-class anomaly classification in industrial vision remains challenging due to noisy/incomplete anomaly representations and the unknown number of anomaly classes. To overcome this, we propose MACO, a novel multi-class anomaly classification framework that learns comprehensive representations and dynamically estimates class number without prior knowledge. First, a soft-focus attention uses anomaly maps to concentrate on relevant abnormal regions, while suppressing background noise. Second, auxiliary classification ([A-CLS]) tokens complement the [CLS] token. They collectively attend to diverse anomaly sub-regions, yielding more holistic and discriminative features. These [A-CLS] tokens are also effective across more tasks and domains. To infer the class number, we propose Correlation-based Number Estimation strategy. It computes the average correlation among labeled classes and transfers its separability cue to the unlabeled set. Experiments on MVTec AD and MTD datasets demonstrate our superiority. Under known class number, MACO improves ARI by 6.5% and $\textbf{16.3%}$ on both datasets, respectively. In the more challenging unknown number scenario, it achieves an $\textbf{11.2%}$ NMI gain on MTD and outperforms existing number estimation strategies by $\textbf{24.1%}$ UPS on MVTec AD. Code will be released at https://github.com/HUST-SLOW/MACO.

Figures & tables

Figure 1: (a) Clustering - based methods focus on the highest - probability region. (b) NCD - based methods capture only a partial region. (c) Our MACO attends to diverse abnormal sub - regions, yielding a complete feature representation.

Figure 2: Our pipeline. Anomaly features are extracted by our proposed DE-ViT, which incorporates Soft-focused Attention and Auxiliary Classification Tokens. These features are classified using a linear classifier regularized by an Orthogonal Weight Loss. A Correlation-based Number Estimation strategy dynamically estimates the unknown class number if it is unavailable.

Figure 3: Attention maps of the [CLS] token of the ViT last layer. We visualize them using: (a) the original design, (b) mask-guided attention, and (c) soft-focused attention.

Figure 4: Attention maps of the [CLS] token, two [ac1] tokens ( red boxes ), and two [ac2] tokens ( blue boxes ) from the ViT last layer, illustrated using one natural scene example (i) and two industrial scene examples (ii) and (iii).

Datasets	Metric	IIC	GATCluster	SCAN	UNO	GCD	SimGCD	AMEND	AC	MuSc	MuSc
Datasets	Metric	IIC	GATCluster	SCAN	UNO	GCD	SimGCD	AMEND	(Unsup.)	+AnomalyNCD	+MACO (Ours)
MVTec AD	NMI	0.093	0.136	0.210	0.146	0.417	0.452	0.431	0.525	0.613	0.657
( Bergmann et al. 2019 )	ARI	0.020	0.053	0.103	0.052	0.302	0.346	0.333	0.431	0.526	0.591
( Bergmann et al. 2019 )	$F_{1}$	0.285	0.264	0.335	0.342	0.553	0.569	0.542	0.604	0.712	0.749
MTD	NMI	0.064	0.028	0.041	0.034	0.211	0.105	0.138	0.179	0.268	0.422
( Huang et al. 2020 )	ARI	0.020	0.009	0.029	0.011	0.115	0.048	0.067	0.120	0.228	0.391

Table 1: Quantitative results on the MVTec AD and MTD datasets if the unlabeled class number

\mathcal{C}^{\mathbf{u}}

is known .

Figure 5: t-SNE visualization of anomaly features $\overline{\mathbf{F}}$ with 6 classes and classifier prototypes $\mathbf{W}^{\textbf{u}}$ , where stars are all prototypes and circled stars denote selected ones.

Datasets	Metric	w/o	DTC	DAEM	GCD	CMS	ProtoGCD	Ours
MVTec AD	MAPE $\downarrow$	1.450	0.490	0.450	0.560	0.610	0.600	0.384
MVTec AD	UPS $\uparrow$	0.491	0.576	0.517	0.323	0.267	0.563	0.817
MTD	MAPE $\downarrow$	0.830	0.500	0.500	0.670	0.670	0.330	0.000
MTD	UPS $\uparrow$	0.546	0.400	0.400	0.200	0.200	0.600	1.000

Table 2: Compared with different number estimate strategies on two industrial datasets. Best results in bold.

	MVTec AD			MTD
Attention	NMI	ARI	$F_{1}$	NMI	ARI	$F_{1}$
(a) w/o guidance	0.587	0.522	0.689	0.305	0.217	0.485
(b) mask-guided	0.647	0.565	0.739	0.254	0.208	0.463
(c) soft-focus (Ours)	0.657	0.591	0.749	0.422	0.391	0.631

Table 3: Compared with different attention mechanisms.

	MVTec AD			MTD
Attention	NMI	ARI	$F_{1}$	NMI	ARI	$F_{1}$
(a) w/o all [A-CLS] tokens	0.651	0.581	0.748	0.390	0.335	0.588
(b) w [ac1] tokens	0.641	0.578	0.730	0.408	0.350	0.588
(c) w [ac2] tokens	0.632	0.569	0.726	0.428	0.403	0.629
(d) Ours	0.657	0.591	0.749	0.422	0.391	0.631

Table 4: Ablation studies on auxiliary classification tokens.

	[CLS] token		+ [A-CLS] tokens
	w/o sf-attn	w sf-attn	only [ca1]	only [ca2]	[ca1] + [ca1]
TP regions	22.05%	35.02%	39.26%	40.55%	42.45%
FN regions	9.86%	10.68%	12.34%	20.79%	21.37%

Table 5: Quantification of anomaly attention alignment on all abnormal images of MVTec AD and MTD datasets.

	MVTec AD			MTD
Loss	NMI	ARI	$F_{1}$	NMI	ARI	$F_{1}$
w/o $\mathcal{L}_{ac}$	0.656	0.588	0.748	0.419	0.387	0.626
w/o $\mathcal{L}_{ow}$	0.659	0.586	0.749	0.416	0.380	0.626
Ours	0.657	0.591	0.749	0.422	0.391	0.631

Table 6: Ablations on the composition of the loss function.

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Jul 27, 2026, cs.CVJ/K move · Enter open · S save

DuoAD: Leveraging [CLS] Dual Characteristics for Training-Free Few-Shot Anomaly Detection

Jyun-Ze Tang, Po-Han Huang, Ming-Ching Chang, Chih-Fan Hsu+1

Inventec Corporation, Taipei, Taiwan · University at Albany, State University of New York, NY, USA

Multimodal Anomaly Detection Vision Transformer Attention-Guided Training Training-Free Duality

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Vision foundation models have enabled strong training-free anomaly detection (AD). However, most existing approaches rely primarily on independent local patch features, leaving the global contextual information encoded by Vision Transformers (ViTs) underexploited. In this work, we identify the dual characteristics of the ViT [CLS] token: its embedding provides anomaly-invariant global semantic representation, while its attention maps implicitly highlight spatially abnormal regions. Building on this observation, we propose a fully automated AD framework leveraging global context to remove manual tunings. Our framework introduces (1) an automatic augmentation selection strategy driven by [CLS]-level semantic consistency, and (2) an attention-guided feature reweighting mechanism that dynamically adjusts patch contributions according to [CLS] attention saliency. By integrating these components over multi-level features, our method achieves stable anomaly scoring and precise localization without training or parameter tuning. Under the one-shot setting, it achieves Image-AUC scores of 97.7%, 93.2%, and 84.5% on MVTec-AD, VisA, and Real-IAD. Using a single fixed configuration across categories, backbones, and datasets, the method establishes a new state-of-the-art for plug-and-play, training-free anomaly detection while maintaining strong robustness and practical scalability.

Abstract

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