StructCore: Structure-Aware Image-Level Scoring for Training-Free Unsupervised Anomaly Detection

Joongwon Chae; Lihui Luo; Yang Liu; Runming Wang; Dongmei Yu; Zeming Liang; Xi Yuan; Dayan Zhang

cs.CVFeb 19, 2026

StructCore: Structure-Aware Image-Level Scoring for Training-Free Unsupervised Anomaly Detection

Authors: Joongwon Chae, Lihui Luo, Yang Liu, Runming Wang, Dongmei Yu, Zeming Liang, Xi Yuan, Dayan Zhang, +3 more

Organizations: Tsinghua University Shenzhen International Graduate School, Shenzhen, China · Ratel Soft · Affiliated Fifth Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, China · Chinese Medicine Guangdong Laboratory

Abstract

Max pooling is the de facto standard for converting anomaly score maps into image-level decisions in memory-bank-based unsupervised anomaly detection (UAD). However, because it relies on a single extreme response, it discards most information about how anomaly evidence is distributed and structured across the image, often causing normal and anomalous scores to overlap. We propose StructCore, a training-free, structure-aware image-level scoring method that goes beyond max pooling. Given an anomaly score map, StructCore computes a low-dimensional structural descriptor phi(S) that captures distributional and spatial characteristics, and refines image-level scoring via a diagonal Mahalanobis calibration estimated from train-good samples, without modifying pixel-level localization. StructCore achieves image-level AUROC scores of 99.6% on MVTec AD and 98.4% on VisA, demonstrating robust image-level anomaly detection by exploiting structural signatures missed by max pooling.

Figures & tables

Figure 1: Overview of the StructCore framework. Normal images are encoded by a frozen DINOv2 ViT-B/14 backbone using multi-layer skip feature extraction. Patch features from multiple layers are concatenated and compressed via a fixed random projection, followed by greedy coreset selection to construct a category-specific memory bank. At inference time, an optional routing bank selects a relevant memory subset for efficient nearest-neighbor matching, producing an anomaly score map. While the base image-level score is obtained by conventional max pooling, StructCore augments it with a structure-aware score computed from a low-dimensional descriptor of the anomaly map, calibrated using statistics from train-good samples. StructCore refines image-level decisions without altering pixel-level localization.

Component	Setting
Backbone	DINOv2 ViT-B/14 (frozen)
Input	resize $448\times 448$ , center-crop $392\times 392$
Layers	skip5: $[-1,-3,-6,-9,-12]$ (concat fusion)
Projection	fixed random projection to $D{=}512$ (RP-512)
Memory bank	coreset ratio = 1% (default), 10% (robustness)
Patch scoring	kNN $k{=}5$ , mean over neighbors (squared $\ell_{2}$ )

Table 1: Paper-default configuration. We use a 1% coreset by default and report 10% as a robustness check.

Panel of Figure 2 — Figure 2: Base vs. hybrid image scores illustrating where StructCore changes the image-level decision. The x-axis is the base score $S_{\mathrm{base}}=\max(S)$ and the y-axis is the final score $S_{\mathrm{hyb}}=S_{\mathrm{base}}+\lambda_{\mathrm{auto}}\,D_{\mathrm{struct}}$ . Points are colored by ground-truth label. Thresholds $\tau_{\mathrm{base}}$ and $\tau_{\mathrm{hyb}}$ are set to the 99.5% quantile of train-good scores. Shaded cross-over regions mark samples whose decisions differ between $S_{\mathrm{base}}$ and $S_{\mathrm{hyb}}$ (counts are shown in each panel).

Dataset	Method	Backbone	I-AUROC	P-AUROC
MVTec-AD [ 1 ]	CFA [ 40 ]	WideResNet-50	99.2	98.2
	PaDiM [ 7 ]	WideResNet-50	95.5	97.3
	PatchCore (max pooling) [ 9 ]	WideResNet-50	98.9	98.1
	PatchCore + StructCore (struct only)	WideResNet-50	99.1	98.1
	PatchCore + StructCore (hybrid)	WideResNet-50	99.2	98.1
	Ours (DINOv2, max pooling)	DINOv2 ViT-B/14 (skip-layer)	98.7	98.2

Table 2: Average performance (Mean) on MVTec-AD. Each entry reports I-AUROC / P-AUROC . Results are only comparable within each backbone group. CFA/PaDiM/PatchCore follow their standard WideResNet-50 settings, while our variants use a frozen DINOv2 ViT-B/14 backbone with skip-layer fusion. Within the DINOv2 group, Base denotes max pooling on the same anomaly maps, and Struct/Hybrid modify only the image-level scoring (pixel-level metrics remain unchanged). For PatchCore-based runs in this table, the coreset ratio is set to 1%. Best results are highlighted in bold within each backbone group.

Dataset	Method	Image-level			Pixel-level			AUPRO
		I-AUROC	I-AP	I-F1-max	P-AUROC	P-AP	P-F1-max
MVTec-AD [ 1 ]	RD4AD [ 3 ]	94.6	96.5	95.2	96.1	48.6	53.8	91.1
	SimpleNet [ 41 ]	95.3	98.4	95.8	96.9	45.9	49.7	86.5
	DeSTSeg [ 42 ]	89.2	95.5	91.6	93.1	54.3	50.9	64.8
	UniAD [ 43 ]	96.5	98.8	96.2	96.8	43.4	49.5	90.7
	ReContrast [ 44 ]	98.3	99.4	97.6	97.1	60.2	61.5	93.2

Table 3: Performance (%) on MVTec-AD and VisA. Our StructCore results are reported with a 1% coreset. Best results are in bold.

Coreset	Extract (ms)	kNN (ms)	Post (ms)	Total (ms)	FPS	GPU peak (MB)
1%	9.60	0.20	0.32	10.12	98.85	2333
5%	9.68	0.68	0.31	10.67	93.71	2459
10%	9.73	1.29	0.27	11.30	88.53	2619

Table 4: Scalability with memory-bank size on MVTec AD (RTX 3090, batch=16). “Extract” includes DINOv2 feature extraction and random projection; “kNN” is FAISS search; “Post” includes Gaussian blur and StructCore scoring. Coreset ratio controls the memory-bank size.

$\phi(S)$	Base	Hybrid	$\Delta$
– (Base only)	98.68	–	–
$\{\sigma_{S}\}$	98.68	99.57	+0.89
$\{\mathrm{topk\_mean}_{r}\}$	98.68	99.57	+0.88
$\{\mathrm{TV}\}$	98.68	99.07	+0.38
$\{\sigma_{S},\mathrm{topk\_mean}_{r}\}$	98.68	99.66	+0.97
$\{\sigma_{S},\mathrm{TV}\}$	98.68	99.55	+0.87

Table 5: Ablation of

\phi(S)

components on MVTec AD (mean I-AUROC, %). Base uses max pooling; Hybrid uses

S_{\mathrm{hyb}}=S_{\mathrm{base}}+\lambda_{\mathrm{auto}}D_{\mathrm{struct}}

\Delta

denotes the improvement over Base.

Distance	MVTec AD (15)	VisA (12)
$\ell_{1}$ (std)	0.9965 $\pm$ 0.0057	0.9743 $\pm$ 0.0347
Diagonal Mahalanobis	0.9962 $\pm$ 0.0059	0.9837 $\pm$ 0.0319
Chebyshev (std)	0.9960 $\pm$ 0.0060	0.9842 $\pm$ 0.0293
Manhattan	0.9934 $\pm$ 0.0108	0.9789 $\pm$ 0.0339
Euclidean	0.9927 $\pm$ 0.0117	0.9767 $\pm$ 0.0349
Chebyshev	0.9922 $\pm$ 0.0123	0.9761 $\pm$ 0.0351

Table 6: Ablation of structural distance for computing

D_{\mathrm{struct}}

(mean image-level AUROC, mean

\pm

std over categories). Standardized variants (suffix _std ) normalize each dimension of

\phi(S)

by the train-good standard deviation.

Appendix figures & tables8 assets

Supplementary material from the paper’s appendix.

Appendix

Category	Base	$\{\sigma_{S}\}$	$\{\mathrm{topk\_mean}_{r}\}$	$\{\mathrm{TV}\}$	$\{\sigma_{S},\mathrm{topk\_mean}_{r}\}$	$\{\sigma_{S},\mathrm{TV}\}$	$\{\mathrm{topk\_mean}_{r},\mathrm{TV}\}$	$\{\sigma_{S},\mathrm{topk\_mean}_{r},\mathrm{TV}\}$
Bottle	100.00	100.00	100.00	100.00	100.00	100.00	100.00	100.00
Cable	92.71	99.04	98.41	94.77	99.23	98.97	98.35	99.10
Capsule	95.81	98.52	98.21	98.88	98.28	98.60	98.32	98.40
Hazelnut	100.00	100.00	100.00	100.00	100.00	100.00	100.00	100.00
Metal Nut	100.00	100.00	100.00	99.76	100.00	99.90	100.00	100.00
Pill	98.06	99.15	99.59	98.28	99.32	99.35	99.59	99.56

Appendix

Table 7: Per-category structural feature ablation on MVTec AD (image-level AUROC, %). Columns show image-level scoring variants: Base is max pooling; others are Hybrid scores with the indicated

\phi(\cdot)

Category	Base (Max)			Struct (Ours)			Hybrid (Ours)			Pixel-level Metrics
Category	AUROC	AP	F1-max	AUROC	AP	F1-max	AUROC	AP	F1-max	AUROC	AP	F1-max	AUPRO
Bottle	100.0	100.0	100.0	100.0	100.0	100.0	100.0	100.0	100.0	98.8	86.1	81.5	95.9
Cable	97.7	98.7	93.9	99.4	99.7	97.3	99.5	99.7	97.2	94.5	52.6	62.5	88.0
Capsule	95.6	99.0	96.8	97.8	99.4	98.6	97.8	99.5	98.2	98.3	59.7	56.8	94.5
Carpet	100.0	100.0	100.0	100.0	100.0	100.0	100.0	100.0	100.0	99.3	73.4	70.4	97.8
Grid	99.9	100.0	99.1	99.9	100.0	99.1	100.0	100.0	100.0	99.6	61.9	61.5	98.6

Appendix

Table 8: Detailed per-category performance on MVTec AD (1% coreset) . Best Image-level AUROC is highlighted in bold .

Category	Base (Max)			Struct (Ours)			Hybrid (Ours)			Pixel-level Metrics
Category	AUROC	AP	F1-max	AUROC	AP	F1-max	AUROC	AP	F1-max	AUROC	AP	F1-max	AUPRO
Bottle	100.0	100.0	100.0	100.0	100.0	100.0	100.0	100.0	100.0	99.0	87.1	81.0	96.5
Cable	97.4	98.5	93.1	99.7	99.8	98.4	99.8	99.9	98.4	95.4	53.5	63.3	89.4
Capsule	97.5	99.4	98.6	98.3	99.6	98.2	98.4	99.6	98.6	98.6	61.0	58.1	95.5
Carpet	100.0	100.0	100.0	100.0	100.0	100.0	100.0	100.0	100.0	99.4	74.4	70.9	97.9
Grid	99.2	99.7	99.1	99.9	100.0	99.1	99.8	99.9	99.1	99.6	63.7	63.5	98.7

Appendix

Table 9: Detailed per-category performance on MVTec AD (10% coreset) .

Category	Base (Max)			Struct (Ours)			Hybrid (Ours)			Pixel-level Metrics
Category	AUROC	AP	F1-max	AUROC	AP	F1-max	AUROC	AP	F1-max	AUROC	AP	F1-max	AUPRO
Candle	96.7	97.0	89.7	98.4	98.4	93.8	98.2	98.3	92.9	99.4	39.4	45.6	98.2
Capsules	98.9	99.4	96.5	96.8	97.8	93.8	98.6	99.1	97.5	99.3	62.9	64.0	97.7
Cashew	94.2	97.3	90.8	98.1	99.0	96.6	98.2	99.1	95.2	96.9	58.4	59.3	91.1
Chewinggum	99.4	99.7	98.0	99.3	99.7	99.0	99.6	99.8	99.0	99.4	75.6	69.8	98.1
Fryum	98.6	99.3	95.3	98.1	99.1	94.9	98.6	99.4	95.8	95.2	43.2	49.5	85.2

Appendix

Table 10: Detailed per-category performance on VisA (1% coreset) .

Category	Base (Max)			Struct (Ours)			Hybrid (Ours)			Pixel-level Metrics
Category	AUROC	AP	F1-max	AUROC	AP	F1-max	AUROC	AP	F1-max	AUROC	AP	F1-max	AUPRO
Candle	96.3	96.7	90.9	97.9	98.0	93.5	97.9	98.0	92.8	99.6	40.6	47.4	98.6
Capsules	99.3	99.6	97.5	97.0	98.0	95.0	98.5	98.8	98.0	99.5	63.8	63.9	98.3
Cashew	96.6	98.3	94.0	98.2	99.0	97.6	98.6	99.2	97.5	93.7	52.2	55.6	83.7
Chewinggum	99.4	99.7	98.5	99.1	99.6	98.5	99.4	99.8	99.0	99.3	77.7	71.7	98.0
Fryum	99.1	99.6	96.6	97.8	98.9	96.0	99.1	99.6	96.5	95.7	43.3	48.8	86.1

Appendix

Table 11: Detailed per-category performance on VisA (10% coreset) .

Layer idx	MVTec (15)	VisA (12)
-1	98.58	80.75
-2	98.61	92.97
-3	98.78	95.91
-4	98.94	97.11
-5	98.94	96.65
-6	98.86	97.00

Appendix

Table 12: Single-layer depth sweep on MVTec AD and VisA. All settings are fixed: RP dim=256,

k

=5, with the indicated

\phi(\cdot)

Images are resized to 448 and center-cropped to 392. We report image-level AUROC (%).

Figure 3: Qualitative results on MVTec AD (all categories).

Figure 4: Qualitative results on VisA (all categories).

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