RADIOlogy: A Single-Pass Approach to Training-Free Zero-Shot Anomaly Detection

Joaquín Figueira; Giacomo D'Amicantonio; Egor Bondarau

cs.CVOct 29, 2026

RADIOlogy: A Single-Pass Approach to Training-Free Zero-Shot Anomaly Detection

Authors: Joaquín Figueira, Giacomo D'Amicantonio, Egor Bondarau

Organizations: AIMS Group, Department of Electrical Engineering, Eindhoven University of Technology

Abstract

Current zero-shot anomaly detection methods often rely on complex prompt-learning techniques or computationally expensive sliding-window strategies to achieve fine-grained visual alignment to detect small-scale anomalies. This reliance stems from the fundamental limitation of standard CLIP architectures, where [CLS] token attention and spatial patch pooling struggle to capture subtle, localized anomaly features. While prompt-learning approaches aim to bridge this gap, they typically require a large number of ground-truth segmentation masks from auxiliary datasets, which are often unavailable in specialized domains. In this work, we show an emerging property of multi-head attention pooling in vision-language models: remarkable zero-shot anomaly localization and detection right out of the box. We translate this property into a single-pass anomaly detection technique, RADIOlogy, that does not require dense, windowed execution and significantly outperforms other training-free baselines. Instead, the method relies on a simple contrastive scoring approach that compares the average similarity between vision features and a suite of normal and abnormal prompts. Furthermore, we leverage the agglomerative features of RADIO models to enhance pixel-level anomaly segmentation using a dense anomaly refinement pipeline. Finally, we show how the vision-language family of training-free zero-shot anomaly detection techniques can be applied successfully to the medical domain with minimal modifications. Code is available at https://github.com/jacomof/RADIOlogy.

Figures & tables

Figure 1: Performance vs. latency comparison. Anomaly segmentation and classification performance relative to detection latency for RADIOlogy (depicted with a star marker), five published training-free baselines and a naive CLIP approach on a seven-dataset industrial suite. Our method outperforms the baselines by a significant margin, while being at least two times faster than all methods except the naive CLIP approach.

Figure 2: Overview of our proposed method. First, the input image is encoded by the agglomerative encoder and two of the three teacher heads (SigLIP2 and SAM3). Using the language-aligned features and the prompt ensemble a margin score is computed per token-embedding to obtain the image-level anomaly score and the raw patch-level margin scores (1). Then, pixel-level refinement is applied to the patch scores using context pooling (2), affinity refinement (3) and finally object-part pooling (4). The resulting scores are aggregated per patch and the final anomaly mask is obtained by applying the color-based foreground segmentation mask to the patch scores.

Dataset	CLIP	SAA+	WinCLIP	FADE	DIVAD	MRAD-TF	Ours
Industrial
MVTec-AD	88.2 / 90.1 / 94.4	62.8 / 87.0 / 81.3	90.4 / 92.7 / 95.6	90.1 / 92.0 / 95.4	75.9 / 87.3 / 86.2	79.0 / 88.2 / 90.0	96.0 / 95.1 / 98.0
VisA	79.7 / 78.7 / 83.6	70.9 / 76.0 / 77.2	75.5 / 78.2 / 78.7	74.7 / 78.3 / 78.3	69.7 / 74.2 / 75.4	75.0 / 77.6 / 79.1	85.4 / 83.1 / 87.8
BTAD	75.2 / 72.9 / 74.8	43.5 / 64.4 / 53.0	68.2 / 67.8 / 70.9	74.3 / 69.6 / 71.7	82.4 / 88.0 / 91.3	86.3 / 85.2 / 90.3	94.4 / 90.1 / 93.7
MPDD	73.1 / 78.6 / 80.6	47.0 / 73.8 / 61.0	61.5 / 77.5 / 69.2	62.8 / 78.0 / 69.4	63.4 / 75.0 / 66.2	67.9 / 77.8 / 73.8	77.0 / 82.0 / 78.9
KSDD	83.0 / 54.2 / 54.9	68.0 / 31.4 / 25.1	77.0 / 41.2 / 35.2	75.4 / 38.9 / 30.8	67.1 / 34.3 / 20.2	92.5 / 68.1 / 72.6	97.5 / 90.0 / 92.9

Table 1: Training-free zero-shot anomaly classification , industrial and medical benchmarks. The table reports image-level AUROC / max-F1 / AP (%) for all anomaly detection benchmarks under six baseline techniques and our own method. WinCLIP and SAA+ are each run under their own industrial vocabulary in the medical evaluations, which showed comparable or better overall performance than the medical vocabulary (see the supplementary material, Sec. S2.3 ), while our method employs the medical prompts. Bold marks the best training-free result per row; underline marks the second-best. FADE [ 29 ] is excluded from the medical evaluations as explained in Sec. 4.1 .

Dataset	CLIP	SAA+	WinCLIP	FADE	DIVAD	MRAD-TF	Ours
Industrial
MVTec-AD	35.0 / 6.4 / 7.6	73.2 / 37.7 / 53.5	82.3 / 24.8 / 61.9	89.2 / 39.8 / 84.1	88.1 / 35.8 / 72.4	86.7 / 28.1 / 63.5	92.2 / 39.0 / 82.9
VisA	43.9 / 3.5 / 13.0	76.0 / 33.0 / 55.4	73.2 / 9.0 / 51.0	91.1 / 16.8 / 79.1	93.4 / 24.0 / 78.2	91.0 / 15.7 / 71.0	93.7 / 20.4 / 83.1
BTAD	30.3 / 6.2 / 11.8	58.4 / 8.1 / 26.4	72.7 / 18.5 / 27.5	96.3 / 46.4 / 76.7	94.3 / 52.5 / 69.9	80.5 / 15.7 / 36.3	96.2 / 52.9 / 77.8
MPDD	43.6 / 5.0 / 16.9	76.7 / 15.6 / 55.0	71.2 / 15.4 / 40.5	95.3 / 24.6 / 83.9	94.9 / 27.0 / 82.0	92.7 / 13.9 / 76.2	94.4 / 24.7 / 85.5
KSDD	71.1 / 1.5 / 31.4	68.3 / 6.2 / 30.5	79.1 / 7.0 / 34.3	84.4 / 6.4 / 56.1	88.2 / 4.5 / 72.8	90.6 / 19.1 / 48.7	98.5 / 38.5 / 93.8

Table 2: Training-free zero-shot anomaly segmentation , industrial and medical benchmarks. The table contains pixel-level AUROC / max-F1 / AUPRO (%) for a collection of anomaly segmentation benchmarks under our method and six training-free ZSAD techniques. WinCLIP and SAA+ are each run under their own industrial vocabulary in the medical evaluations, which showed comparable or better overall performance than the medical vocabulary (see Sec. S2.3 of the supplementary material), while our method uses the medical prompts. FADE [ 29 ] is not included in the medical evaluations as explained in Sec. 4.1 . Bold marks the best training-free result per row; underline marks the second-best.

Figure 3: Qualitative comparison. Our method compared with the published baselines. All images are generated by resizing the outputs to a common resolution (518). Results from industrial (MVTec and VisA) and medical datasets (TN3K and ClinicDB) are included. GT depicts the ground truth, and Ours the output of RADIOlogy. FADE is excluded from the medical datasets ( Sec. 4.1 ).

Context	Affinity	Regions	AUROC	max-F1
			89.5	24.3
✓			93.7	28.9
✓	✓		94.6	33.6
✓	✓	✓	95.2	37.8

Table 3: Pixel-refinement component ablation. Mean pixel AUROC and max-F1 over seven industrial benchmarks. The language-gated foreground prior (Sec. 3.4 ) is on in every row; only context pooling, affinity refinement, and SAM3 object-part region pooling are toggled. Image-level metrics are unaffected by all three components and are therefore constant across rows, so they are omitted here. Bold marks the best row per column.

Encoder	Pooling	Pixel AUROC	Pixel max-F1	Image AUROC
SigLIP (so400m) [ 51 ]	MAP head	84.2	29.3	91.7
SigLIP2 (so400m, p14) [ 45 ]	MAP head	83.6	27.8	94.6
SigLIP2-g [ 45 ]	MAP head	81.1	27.3	96.4
PE-Core (ViT-L/14) [ 5 ]	MAP head	75.2	26.1	91.6
RADIOv2.5-h (SigLIP distilled) [ 18 ]	MAP head distilled	87.0	30.9	92.8
C-RADIOv4-h (SigLIP2 distilled) [ 36 ]	MAP head distilled	83.3	26.5	95.3

Table 4: Dense patch-text alignment by pooling architecture. Anomaly maps are computed on the MVTec-AD dataset using the raw per-patch margin without refinements. Bold marks the best value per column, underline the second-best, and italics performance below chance. Alongside CLIP and SigLIP model variations, we include the performance of PE-Core [ 5 ] , whose attention-pooled summary embeddings are close to SigLIP’s MAP head. The input is resized to the training resolution of the models when available, otherwise it is set to match the original SigLIP’s native patch grid for MAP encoders, and CLIP (ViT-B/16) [ 34 ] for CLIP variations.

Figure S1 : Effect of the context pooling window size

k

. Ablation of the effect of the window size in the context pooling mechanism over the raw patch margin scores. Measured on MVTec-AD [ 3 ] . AUPRO is computed within a 0.3 limit.

Figure S2 : Effect of the region pooling cluster set. Ablation of the region pooling cluster set over the features produced by the SAM head of C-RADIOv4-h [ 36 ] . All combinations over 8, 32 and 128 are provided. AUPRO is computed with a 0.3 limit.

Figure S3 : Effect of the number of affinity refinement steps. Ablation of the number of affinity refinement steps over the features produced by C-RADIOv4-h trunk encoder. The steps included range from 1 (declared configuration) to 10. AUPRO is computed with a 0.3 limit.

Mechanism	Substrate	AUROC	AUPRO	max-F1
Affinity refinement	DINO head	95.1	85.4	36.7
	RADIO trunk (declared)	95.2	85.9	37.8
	SAM head	95.0	85.3	37.1
	SigLIP2 head	95.1	85.6	37.4
Region pooling	SAM head (declared)	95.2	85.9	37.8
	RADIO trunk	95.3	85.5	35.9

Table S1 : Effect of different feature sets in the anomaly segmentation refinement steps. Pixel-level AUROC (%), AUPRO (%, 0.3 limit) and max-F1 (%), mean over MVTec-AD, VisA [ 55 ] , BTAD [ 31 ] , MPDD [ 21 ] , KolektorSDD (KSDD) [ 43 ] , DAGM [ 47 ] and DTD-Synthetic [ 9 , 2 ] . The declared configuration (Sec. 3.3 ) uses RADIO’s trunk features for affinity diffusion and its SAM3-adaptor features for region clustering. Each of the first two blocks varies the features of one mechanism while the other is kept at its declared setting. Bold marks the best row per block and column.

Foreground stage	AUROC	AUPRO	max-F1
No mask	92.8	82.8	37.0
Mask, no gate	93.7	84.2	37.8
Mask + language gate (declared)	95.2	85.9	37.8

Table S2 : Effect of the foreground mask and language gate on the seven industrial datasets. Pixel-level AUROC (%), AUPRO (%, 0.3 limit) and max-F1 (%) mean over the seven industrial datasets, isolating the foreground stage of Sec. 3.4 from the declared pixel-refinement pipeline (context pooling, RADIO affinity refinement, SAM3 region pooling), which is held fixed across all three rows. Mask, no gate applies the border-colour prior to every image unconditionally; Mask + language gate adds the object/surface abstention margin that decides per image whether to apply it (the declared configuration).

Dataset	Industrial	Clinical	Industrial	Clinical	Industrial	Clinical
	Ours		WinCLIP		SAA+
HeadCT [ 25 ]	92.7 / 89.9	98.0 / 93.9	83.7 / 78.8	77.3 / 71.8	45.1 / 67.3	47.3 / 66.7
BrainMRI [ 23 ]	90.4 / 89.9	96.0 / 94.9	88.9 / 86.4	84.6 / 81.9	51.7 / 77.8	53.8 / 77.2
Br35H [ 17 ]	93.9 / 89.7	97.9 / 94.7	83.9 / 77.1	77.1 / 71.9	54.7 / 68.4	57.1 / 67.9
Mean	92.3 / 89.8	97.3 / 94.5	85.5 / 80.8	79.7 / 75.2	50.5 / 71.2	52.7 / 70.6

Table S3 : Medical anomaly-vocabulary ablation, detection. Image-level AUROC / max-F1 (%) on AdaCLIP’s [ 7 ] three detection-only medical benchmarks, industrial state words (WinCLIP’s own: damaged , {} with flaw/defect/damage ) vs. clinical state words ( {} with a lesion/tumor/abnormality/ pathology , abnormal {} , diseased {} ), class names and every other component held fixed within each method. For SAA+, the clinical state words, filled with each class name, replace its general anomaly prompts and generic “defect” prompt; its property prompts are unchanged. Bold marks the best vocabulary per method, per dataset, per metric.

	Ours		WinCLIP		SAA+
Dataset	Industrial	Clinical	Industrial	Clinical	Industrial	Clinical
ISIC [ 16 ]	90.2 / 77.3	90.5 / 74.0	74.4 / 55.1	70.6 / 51.8	78.0 / 69.3	81.4 / 72.3
ClinicDB [ 4 ]	88.2 / 53.4	89.6 / 54.9	69.7 / 26.7	75.5 / 33.3	78.7 / 47.2	83.7 / 50.5
ColonDB [ 44 ]	82.2 / 42.5	89.5 / 47.8	64.7 / 21.1	69.8 / 23.8	78.2 / 42.2	76.8 / 36.4
TN3K [ 14 ]	64.0 / 30.2	79.5 / 43.4	68.1 / 31.1	68.5 / 31.0	71.3 / 38.3	71.2 / 37.7
Mean	81.1 / 50.8	87.3 / 55.0	69.2 / 33.5	71.1 / 35.0	76.5 / 49.3	78.3 / 49.2

Table S4 : Medical anomaly-vocabulary ablation, localization. Pixel-level AUROC / max-F1 (%) on AdaCLIP’s four localization-only medical benchmarks selection, same vocabulary pair and protocol as Table S3 . The Ours, Clinical column is the declared configuration; the WinCLIP and SAA+ columns isolate the vocabulary swap only. Bold marks the best vocabulary per method, dataset, and metric.

	Image		Pixel
Aspect handling	AUROC	max-F1	AUROC	AUPRO	max-F1
Without correction	93.5	70.7	98.5	89.9	25.9
With correction (declared)	97.5	90.0	98.5	93.8	38.5

Table S5 : The aspect-ratio band-tiling rule, KSDD. Image- and pixel-level metrics (%) on KSDD, the dataset with the most severe aspect ratio deviation. Without tiling resizes each 500

\times

1260 frame directly to a square, an anisotropic stretch; with tiling (declared) splits it into 3 non-overlapping bands. Bold marks the best row per column.

Input resolution	AUROC	max-F1
224	89.6	86.6
384	92.1	89.9
448 (declared)	92.6	90.5
512	93.0	90.5
672	92.7	90.8

Table S6 : Input resolution vs. image-level detection. Mean image-level AUROC (%) and max-F1 (%) for the seven industrial datasets. Bold marks the best value per column.

Dataset	Input resolution	AUROC	AUPRO	max-F1
MVTec-AD	224	88.9	70.9	31.0
	384	91.0	78.2	35.0
	448 (declared)	92.2	82.9	39.0
	512	91.1	78.9	36.4
	672	90.8	78.2	36.2
VisA	224	90.4	67.5	14.9

Table S7 : Input resolution, per dataset and mean over seven industrial benchmarks. Pixel-level AUROC (%), AUPRO (%) and max-F1 (%) for the declared full pipeline run at five encoder input resolutions. Evaluation resolution is fixed at 518px; only the encoder’s input resolution and its resulting patch grid is modified. The context window

k

scales with the grid to maintain its physical receptive field roughly resolution-invariant (

k{=}3,5,5,7,9

); the region count

K\in\{8,32\}

is held fixed. Bold marks the best resolution per dataset per column.

Encoder	Pixel	Image
RADIOv2.5-B (siglip head)	90.36 / 37.78	91.02 / 92.56
RADIOv2.5-L (siglip head)	90.23 / 39.63	93.86 / 94.30
RADIOv2.5-H (siglip head)	90.21 / 40.10	93.36 / 93.08
C-RADIOv4-h (siglip2-g head)	92.15 / 38.98	96.00 / 95.14
C-RADIOv4-so400m (siglip2-g head)	86.34 / 36.97	94.55 / 93.57

Table S8 : Backbone family and size in the full RADIOlogy pipeline. Different RADIO variants evaluated on MVTec-AD dataset using the RADIOlogy pipeline. Every cell is AUROC / max-F1 (%). RADIOv2.5 uses its SigLIP head (SigLIP v1 so400m teacher [ 51 ] ); its foreground-suppression gate threshold is separately leave-one-dataset-out calibrated on a five-benchmark industrial panel, since it operates in a different regime than C-RADIOv4’s declared value. C-RADIOv4 uses its declared SigLIP2-g head and gate. Bold marks the best value per column, underline the second-best.

Dataset	C-RADIOv4-h (declared)	Real teachers
Industrial
MVTec	96.0 / 95.1	96.3 / 95.6
VisA	85.4 / 83.1	83.0 / 81.9
BTAD	94.4 / 90.1	93.8 / 91.9
MPDD	77.0 / 82.0	72.6 / 81.1
KSDD	97.5 / 90.0	98.2 / 86.6

Table S9 : Real teachers (SigLIP2-g and SAM3) vs. the distilled trunk in anomaly detection. The table contains image-level AUROC / max-F1 (%) for each dataset. Bold marks the best value per row.

Dataset	C-RADIOv4-h (declared)	Real teachers
Industrial
MVTec	92.2 / 39.0	88.3 / 38.9
VisA	93.7 / 20.4	95.0 / 25.8
BTAD	96.2 / 52.9	96.1 / 44.8
MPDD	94.4 / 24.7	93.0 / 26.1
KSDD	98.5 / 38.5	99.4 / 41.1

Table S10 : Real teachers (SigLIP2-g and SAM3) vs. the distilled trunk in anomaly segmentation. Every cell is pixel-level AUROC / max-F1 (%). Bold marks the best value per row.

Figure S4 : Failure cases of RADIOlogy. The figure depicts two classes of failure cases of our approach: masking errors and poor feature alignment.

Style	a {} form	the {} form
Cropped	a cropped photo of a {}.	a cropped photo of the {}.
Close-up	a close-up photo of a {}.	a close-up photo of the {}.
Bright	a bright photo of a {}.	a bright photo of the {}.
Dark	a dark photo of a {}.	a dark photo of the {}.
JPEG-corrupted	a jpeg corrupted photo of a {}.	a jpeg corrupted photo of the {}.
Blurry	a blurry photo of a {}.	a blurry photo of the {}.

Table S11 : Scoring prompt templates. Photographic templates

\mathcal{T}

, paired by article. {} is later filled with the state phrase (itself filled with the category name).

MVTec-AD
Category	Image AUROC / max-F1 / AP	Pixel AUROC / max-F1 / AUPRO
Bottle	94.3 / 93.6 / 98.3	93.2 / 62.9 / 86.9
Cable	92.7 / 89.5 / 95.8	88.2 / 27.3 / 73.2
Capsule	92.4 / 93.4 / 98.5	96.6 / 35.7 / 89.4
Carpet	100.0 / 100.0 / 100.0	98.9 / 57.9 / 93.4
Grid	100.0 / 100.0 / 100.0	96.8 / 26.9 / 86.0

Table S12 : Per-category results breakdown for RADIOlogy on the six multi-category industrial benchmarks. The table contains image-level AUROC / max-F1 / AP (%) and pixel-level AUROC / max-F1 /AUPRO (%) under the declared configuration. Bold marks each dataset’s mean.

Table S13 : Scoring state vocabularies. State vocabularies

\mathcal{S}

, by domain. {} is filled with the category name (e.g. bottle , brain MRI ).

Table S14 : Foreground/background gate vocabularies. The abstention gate that decides when to skip the foreground mask re-purposes the scoring templates

\mathcal{T}

(Table S11 ) with foreground/background descriptors.

\theta

refers to the gate’s foreground threshold value (different on each domain).

	Image	Pixel
Published	79.8 / – / –	68.4 / – / –
Released lookup	79.8 / 83.6 / 87.0	68.4 / 19.1 / 29.3
Corrected lookup (ours)	82.4 / 88.0 / 91.3	94.3 / 52.5 / 69.9

Table S15 : DIVAD on BTAD. Image AUROC / max-F1 / AP and pixel AUROC / max-F1 / AUPRO (%). The released ground-truth lookup pairs most test images with the mask of another image; correcting it yields the numbers reported in the main paper.

Anomaly Detection

Anomaly detection finds rare or unexpected patterns in data, from defective products and system faults to fraud and security threats.

See all 657 papers

VisualAD: Language-Free Zero-Shot Anomaly Detection via Vision Transformer
Mar 9, 2026Yanning Hou, Peiyuan Li, Zirui Liu +4Zero-Shot Anomaly Detection Vision Foundation Model Adaptation
College of Intelligence Science and Technology, National University of Defense Technology, Changsha, China · School of Artificial Intelligence, Anhui University, Hefei, China · State Key Laboratory of Opto-Electronic Information Acquisition and Protection Technology, Anhui University, Hefei, China
Anomaly-Aware Vision-Language Adapters for Zero-Shot Anomaly Detection
May 12, 2026Muhammad Aqeel, Maham Nazir, Uzair Khan +2VLM Adaptation Medical Image Anomaly Detection
Dept. of Engineering for Innovation Medicine, University of Verona, Italy · School of Computer Science and Engineering, Beihang University, China · Dept. of Engineering for Innovation +1
WMoE-CLIP: Wavelet-Enhanced Mixture-of-Experts Prompt Learning for Zero-Shot Anomaly Detection
Mar 6, 2026Peng Chen, Chao HuangPrompt Learning for VLMs Zero-Shot Anomaly Detection
School of Cyber Science and Technology, Shenzhen Campus of Sun Yat-sen University, Shenzhen

Mar 9, 2026J/K move · Enter open · S save

VisualAD: Language-Free Zero-Shot Anomaly Detection via Vision Transformer

Yanning Hou, Peiyuan Li, Zirui Liu, Yitong Wang+3

College of Intelligence Science and Technology, National University of Defense Technology, Changsha, China · School of Artificial Intelligence, Anhui University, Hefei, China · State Key Laboratory of Opto-Electronic Information Acquisition and Protection Technology, Anhui University, Hefei, China

Zero-Shot Anomaly Detection Vision Foundation Model Adaptation Medical Image Anomaly Detection

Open paper AskPDF arXiv

Zero-shot anomaly detection (ZSAD) requires detecting and localizing anomalies without access to target-class anomaly samples. Mainstream methods rely on vision-language models (VLMs) such as CLIP: they build hand-crafted or learned prompt sets for normal and abnormal semantics, then compute image-text similarities for open-set discrimination. While effective, this paradigm depends on a text encoder and cross-modal alignment, which can lead to training instability and parameter redundancy. This work revisits the necessity of the text branch in ZSAD and presents VisualAD, a purely visual framework built on Vision Transformers. We introduce two learnable tokens within a frozen backbone to directly encode normality and abnormality. Through multi-layer self-attention, these tokens interact with patch tokens, gradually acquiring high-level notions of normality and anomaly while guiding patches to highlight anomaly-related cues. Additionally, we incorporate a Spatial-Aware Cross-Attention (SCA) module and a lightweight Self-Alignment Function (SAF): SCA injects fine-grained spatial information into the tokens, and SAF recalibrates patch features before anomaly scoring. VisualAD achieves state-of-the-art performance on 13 zero-shot anomaly detection benchmarks spanning industrial and medical domains, and adapts seamlessly to pretrained vision backbones such as the CLIP image encoder and DINOv2. Code: https://github.com/7HHHHH/VisualAD

Abstract

Anomaly Detection

Explore similar work

VisualAD: Language-Free Zero-Shot Anomaly Detection via Vision Transformer

Anomaly-Aware Vision-Language Adapters for Zero-Shot Anomaly Detection

WMoE-CLIP: Wavelet-Enhanced Mixture-of-Experts Prompt Learning for Zero-Shot Anomaly Detection