GroundingAnomaly: Spatially-Grounded Diffusion for Few-Shot Anomaly Synthesis

Yishen Liu; Hongcang Chen; Pengcheng Zhao; Yunfan Bao; Yuxi Tian; Jieming Zhang; Hao Chen; Zheng Zhi

cs.CVApr 9, 2026

GroundingAnomaly: Spatially-Grounded Diffusion for Few-Shot Anomaly Synthesis

Authors: Yishen Liu, Hongcang Chen, Pengcheng Zhao, Yunfan Bao, Yuxi Tian, Jieming Zhang, Hao Chen, Zheng Zhi, +3 more

Organizations: Beijing Institute of Technology · Beijing Jiaotong University · Li Auto · Tsinghua University

Abstract

The performance of visual anomaly inspection in industrial quality control is often constrained by the scarcity of real anomalous samples. Consequently, anomaly synthesis techniques have been developed to enlarge training sets and enhance downstream inspection. However, existing methods either suffer from poor integration caused by inpainting or fail to provide accurate masks. To address these limitations, we propose GroundingAnomaly, a novel few-shot anomaly image generation framework. Our framework introduces a Spatial Conditioning Module that leverages per-pixel semantic maps to enable precise spatial control over the synthesized anomalies. Furthermore, a Gated Self-Attention Module is designed to inject conditioning tokens into a frozen U-Net via gated attention layers. This carefully preserves pretrained priors while ensuring stable few-shot adaptation. Extensive evaluations on the MVTec AD and VisA datasets demonstrate that GroundingAnomaly generates high-quality anomalies and achieves state-of-the-art performance across multiple downstream tasks, including anomaly detection, segmentation, and instance-level detection.

Figures & tables

Figure 1: Anomaly Generation methods inpaint anomalies onto normal images; Anomaly Image Generation methods jointly generate anomalies with products and predict masks after generation; our GroundingAnomaly grounds anomalies with semantic maps and generate the whole anomalous images.

Figure 2: Proposed framework of GroundingAnomaly : (i) Spatial Conditioning Module that encodes a pixel-wise semantic map fuses with disentangled product and anomaly tokens; (ii) Gated Self-Attention Module , which injects the spatial conditioning into a frozen U-Net. (iii) The framework is trained on mixed batches of normal and anomalous images to leverage cross-domain appearance priors, and it generates diverse, high-fidelity anomaly images.

Figure 3: (i) Spatial Conditioning Module; (ii) Gated Self-Attention Module.

Dataset	DFMGAN [ 6 ]		AnoDiff [ 16 ]		DualAnoDiff [ 18 ]		SeaS [ 40 ]		Ours
	IS	IC-L	IS	IC-L	IS	IC-L	IS	IC-L	IS	IC-L
MVTec AD	1.72	0.20	1.80	0.32	1.91	0.37	1.95	0.34	1.99	0.41
VisA	1.25	0.26	1.26	0.25	1.25	0.25	1.27	0.26	1.29	0.31

Table 1: Comparison on IS and IC-LPIPS on MVTec AD and VisA. Bold indicates the best performance.

Figure 4: Left: Comparison on the generation results on MVTec AD and VisA. Cable, grid and leather to MVTec AD and candle and macaroni2 belong to VisA. Our method generates high-quality anomaly images that are accurately grounded with masks. Right: Visualization of U-Net segmentation results on MVTec AD and VisA. The detection model trained on GroundingAnomaly-generated data exhibits more robust performance.

Category	DRÆM [ 37 ]				DFMGAN [ 6 ]				AnomalyDiffusion [ 16 ]				DualAnoDiff [ 18 ]				SeaS [ 40 ]				Ours
	AUC-P	AP-P	F1-P	AP-I	AUC-P	AP-P	F1-P	AP-I	AUC-P	AP-P	F1-P	AP-I	AUC-P	AP-P	F1-P	AP-I	AUC-P	AP-P	F1-P	AP-I	AUC-P	AP-P	F1-P	AP-I
bottle	96.7	80.2	74.0	99.8	98.9	90.2	83.9	99.8	99.4	94.1	87.3	99.9	99.5	93.4	85.7	100.0	99.7	95.5	87.9	100.0	99.6	94.1	88.1	100.0
cable	80.3	21.8	28.3	83.2	97.2	81.0	75.4	97.8	99.2	90.8	83.5	100.0	97.5	82.6	76.9	98.3	94.5	77.8	75.0	98.5	97.9	81.0	78.2	99.5
capsule	76.2	25.5	32.1	98.7	79.2	26.0	35.0	98.5	98.8	57.2	59.8	99.9	99.5	73.2	67.0	99.2	95.7	51.7	52.4	99.6	98.3	65.5	59.4	98.8
carpet	92.6	43.0	41.9	98.7	90.6	33.4	38.1	98.5	98.6	81.2	74.6	98.8	99.4	89.1	80.2	99.9	99.5	84.5	76.5	99.1	99.5	89.3	86.3	99.6
grid	99.1	59.3	58.7	99.9	75.2	14.3	20.5	90.4	98.3	52.9	54.6	99.5	98.5	57.2	54.9	99.7	99.6	67.7	62.1	99.9	99.7	73.6	69.9	100.0

Table 2: Comparison on pixel-level segmentation and image-level detection on MVTec AD dataset by training U-Nets on the generated data from DRÆM, DFMGAN, AnomalyDiffusion, DualAnoDiff, SeaS and our proposed GroundingAnomaly.

Category	DRÆM [ 37 ]				DFMGAN [ 6 ]				AnomalyDiffusion [ 16 ]				DualAnoDiff [ 18 ]				SeaS [ 40 ]				Ours
	AUC-P	AP-P	F1-P	AP-I	AUC-P	AP-P	F1-P	AP-I	AUC-P	AP-P	F1-P	AP-I	AUC-P	AP-P	F1-P	AP-I	AUC-P	AP-P	F1-P	AP-I	AUC-P	AP-P	F1-P	AP-I
candle	86.2	18.7	23.7	77.5	87.1	24.7	31.4	85.9	89.1	28.7	33.9	87.2	93.7	48.1	46.5	89.0	89.4	45.1	44.1	87.8	98.9	61.2	58.3	94.4
capsules	90.7	50.4	53.8	85.1	91.3	55.0	58.6	86.5	94.1	60.5	62.3	90.7	95.8	61.8	64.5	92.0	97.2	61.2	64.2	88.7	99.3	78.4	72.1	92.3
cashew	89.0	45.2	47.1	91.1	92.2	65.7	65.1	92.3	95.1	82.1	80.9	95.0	96.5	83.1	87.1	96.1	97.0	80.9	81.3	93.3	99.3	87.1	84.2	95.3
chewinggum	94.3	59.7	65.0	97.0	96.0	67.4	63.0	96.7	97.3	80.5	75.3	95.5	98.8	80.3	77.1	98.8	98.5	81.2	74.8	99.3	99.5	84.6	78.2	99.4
fryum	88.2	41.3	34.1	81.9	90.4	45.7	43.2	89.3	93.4	57.1	52.9	92.1	94.8	62.9	56.8	94.4	95.8	63.8	59.0	93.6	91.5	55.1	51.3	96.4

Table 3: Comparison on pixel-level segmentation and image-level detection on VisA dataset by training U-Nets on the generated data from DRÆM, DFMGAN, AnomalyDiffusion, DualAnoDiff, SeaS and our proposed GroundingAnomaly.

Table 8

Figure 5: Left: Qualitative analysis of SFF. The red outline denotes the ground-truth mask. While SeaS [ 40 ] suffers from spatial misalignment, our model without SFF (w/o SFF) achieves accurate bounding. Our full model (Ours) leverages SFF to achieve both precise spatial grounding and high-fidelity anomaly synthesis. Right: Unseen anomaly generation results on MVTec AD and VisA. GroundingAnomaly is trained on anomalous images and finetuned on normal images from another product.

Table 10

Figure 6: Multi-anomaly generation result of GroundingAnomaly.

Appendix figures & tables19 assets

Supplementary material from the paper’s appendix.

Appendix

Model	IS	IC-L	AUC-P	AP-P	AP-I
$N=1$ $K=1$	1.79	0.37	97.2	80.5	99.1
$N=1$ $K=4$	1.77	0.39	97.7	79.7	98.9
$N=4$ $K=1$	1.73	0.40	95.6	78.7	98.7
$N=4$ $K=4$ ( Ours )	1.99	0.41	99.2	85.9	99.2

Appendix

Table 8: Ablation on different numbers of tokens.

Figure 7: Illustration of ablations on GSM. (i) Gated Cross-Attention Module; (ii) Discarding Visual Tokens; (iii) Ungated Self-Attention Module; (iv) Ours.

Model	IS	IC-L	AUC-P	AP-P	AP-I
GCM	1.71	0.35	98.1	80.7	99.0
DVT	1.72	0.39	98.3	81.7	98.7
USM	1.68	0.40	94.9	74.1	98.0
w/o LoRA	1.93	0.39	98.3	83.3	99.0
Ours	1.99	0.41	99.2	85.9	99.2

Appendix

Table 9: Ablation on Gated Self-Attention Module.

Figure 8: Qualitative comparison of spatial conditioning. GroundingAnomaly achieves the highest fidelity and conditioning accuracy. Futhermore, models conditioned on binary masks, by contrast, often produce anomalies that are less semantically similar to the expected anomaly class.

Model	IS	IC-L	AUC-P	AP-P	AP-I
ControlNet+Mask	1.81	0.39	96.7	69.2	96.1
ControlNet+SM	1.86	0.40	96.9	70.3	96.2
GLIGEN+Mask	1.85	0.39	98.1	73.3	97.7
GLIGEN+SM	1.88	0.42	98.3	75.2	97.8
Ours+Mask	1.89	0.39	98.2	74.9	97.1
Ours+SM	1.99	0.41	99.2	85.9	99.2

Appendix

Table 10: Ablation on spatial conditioning.

Model	DFMGAN				AnomalyDiffusion				DualAnoDiff				SeaS				Ours
	AUROC	AP	F1-max	maxIoU	AUROC	AP	F1-max	maxIoU	AUROC	AP	F1-max	maxIoU	AUROC	AP	F1-max	maxIoU	AUROC	AP	F1-max	maxIoU
MVTec AD
BiSeNet V2	94.57	60.42	60.54	45.83	96.27	64.50	62.27	42.89	96.88	66.21	61.17	46.97	97.21	69.21	66.37	55.28	97.58	75.03	68.71	52.34
UPerNet	92.33	57.01	56.91	46.64	96.87	69.92	66.95	50.80	97.17	72.08	67.55	51.15	97.87	74.42	70.70	61.24	97.24	77.09	75.60	59.49
Segformer	93.20	58.50	58.10	47.30	97.10	71.20	68.00	52.10	97.63	73.55	68.71	56.33	97.50	81.30	74.85	59.81	98.53	82.63	75.51	60.66
Average	93.37	58.64	58.52	46.59	96.75	68.54	65.74	48.60	97.23	70.61	65.81	51.48	97.53	74.98	70.64	58.78	97.78	78.25	73.27	57.50

Appendix

Table 11: Comparison on trained unified supervised segmentation models for pixel-level anomaly segmentation on MVTec AD and VisA.

MVTec AD
Model	DFMGAN			AnomalyDiffusion			DualAnoDiff			SeaS			Ours
	AUROC	AP	F1-max	AUROC	AP	F1-max	AUROC	AP	F1-max	AUROC	AP	F1-max	AUROC	AP	F1-max
BiSeNet V2	90.90	94.43	90.33	90.08	94.84	91.84	91.33	96.79	94.49	96.00	98.14	95.43	96.41	97.87	95.88
UPerNet	90.74	94.43	90.37	96.62	98.61	96.21	97.89	99.00	96.77	98.29	99.20	97.34	98.63	98.77	97.24
Segformer	90.97	94.69	91.11	95.73	98.66	96.37	96.97	98.79	97.03	98.33	99.17	97.41	98.74	98.93	97.55
Average	90.87	94.52	90.60	94.14	97.37	94.81	95.40	98.19	96.10	97.54	98.84	96.73	97.93	98.52	96.89

Appendix

Table 12: Comparison on trained unified supervised segmentation models for image-level anomaly detection on MVTec AD and VisA.

Figure 9: Examples of 1-shot and 2-shot generated images.

Figure 10: P-AUROC of U-Nets trained by synthesized data.

Figure 11: Examples of anomaly generation on MVTec AD.

Figure 12: Examples of anomaly generation on MVTec AD.

Figure 13: Examples of anomaly generation on MVTec AD.

Figure 14: Examples of anomaly generation on VisA.

Figure 15: Examples of anomaly generation on VisA.

Category	DFMGAN		AnoDiff		DualAnoDiff		SeaS		Ours
	IS $\uparrow$	IC-L $\uparrow$	IS $\uparrow$	IC-L $\uparrow$	IS $\uparrow$	IC-L $\uparrow$	IS $\uparrow$	IC-L $\uparrow$	IS $\uparrow$	IC-L $\uparrow$
bottle	1.62	0.12	1.58	0.19	2.17	0.36	1.78	0.21	1.97	0.35
cable	1.96	0.25	2.13	0.41	2.12	0.43	2.09	0.42	2.23	0.43
capsule	1.59	0.11	1.59	0.21	1.60	0.31	1.69	0.21	1.92	0.34
carpet	1.23	0.13	1.16	0.24	1.36	0.29	1.21	0.25	1.39	0.30
grid	1.97	0.13	2.04	0.44	2.09	0.42	2.62	0.44	3.13	0.53

Appendix

Table 13: Per category IS and IC-LPIPS on MVTec AD.

Category	DFMGAN		AnoDiff		DualAnoDiff		SeaS		Ours
	IS $\uparrow$	IC-L $\uparrow$	IS $\uparrow$	IC-L $\uparrow$	IS $\uparrow$	IC-L $\uparrow$	IS $\uparrow$	IC-L $\uparrow$	IS $\uparrow$	IC-L $\uparrow$
candle	1.19	0.23	1.28	0.17	1.27	0.15	1.20	0.12	1.25	0.17
capsules	1.25	0.22	1.39	0.50	1.37	0.55	1.58	0.60	1.58	0.60
cashew	1.25	0.24	1.27	0.26	1.29	0.24	1.21	0.28	1.21	0.33
chewinggum	1.33	0.24	1.15	0.19	1.19	0.20	1.29	0.27	1.29	0.32
fryum	1.28	0.20	1.20	0.14	1.17	0.14	1.14	0.21	1.14	0.26

Appendix

Table 14: Per category IS and IC-LPIPS on VisA.

MVTec AD
Category	DFMGAN	AnoDiff	DualAnoDiff	SeaS	Ours
	mAP $\uparrow$	mAP $\uparrow$	mAP $\uparrow$	mAP $\uparrow$	mAP $\uparrow$
bottle	39.56	47.44	45.41	43.77	50.90
cable	58.23	58.07	59.07	57.40	60.16
capsule	7.83	7.31	7.52	9.23	10.30
carpet	15.97	14.19	26.75	23.48	26.38

Appendix

Table 15: Per category instance-level mAP (%) for MVTec AD (top) and VisA (bottom) of Faster R-CNN.

MVTec AD
Category	DFMGAN	AnoDiff	DualAnoDiff	SeaS	Ours
	mAP $\uparrow$	mAP $\uparrow$	mAP $\uparrow$	mAP $\uparrow$	mAP $\uparrow$
bottle	35.90	40.90	41.12	40.45	44.75
cable	67.08	59.87	44.93	57.71	54.82
capsule	9.09	6.94	10.29	12.11	12.11
carpet	17.28	18.59	34.82	25.92	33.91

Appendix

Table 16: Per category instance-level mAP (%) for MVTec AD (top) and VisA (bottom) of DETR.

MVTec AD
Category	DFMGAN	AnoDiff	DualAnoDiff	SeaS	Ours
	mAP $\uparrow$	mAP $\uparrow$	mAP $\uparrow$	mAP $\uparrow$	mAP $\uparrow$
bottle	43.63	55.66	51.47	52.04	56.84
cable	66.80	64.29	68.13	61.67	68.60
capsule	8.40	11.03	8.28	9.30	11.54
carpet	24.04	23.21	35.81	29.22	35.32

Appendix

Table 17: Per category instance-level mAP (%) for MVTec AD (top) and VisA (bottom) of YOLOv5.

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Industrial anomaly detection (AD) is characterized by an abundance of normal images but a scarcity of anomalous ones. Although numerous few-shot anomaly synthesis methods have been proposed to augment anomalous data for downstream AD tasks, most existing approaches require time-consuming training and struggle to learn distributions that are faithful to real anomalies, thereby restricting the efficacy of AD models trained on such data. To address these limitations, we propose a training-free few-shot anomaly generation method, namely O2MAG, which leverages the self-attention in One reference anomalous image to synthesize More realistic anomalies, supporting effective downstream anomaly detection. Specifically, O2MAG manipulates three parallel diffusion processes via self-attention grafting and incorporates the anomaly mask to mitigate foreground-background query confusion, synthesizing text-guided anomalies that closely adhere to real anomalous distributions. To bridge the semantic gap between the encoded anomaly text prompts and the true anomaly semantics, Anomaly-Guided Optimization is further introduced to align the synthesis process with the target anomalous distribution, steering the generation toward realistic and text-consistent anomalies. Moreover, to mitigate faint anomaly synthesis inside anomaly masks, Dual-Attention Enhancement is adopted during generation to reinforce both self- and cross-attention on masked regions. Extensive experiments validate the effectiveness of O2MAG, demonstrating its superior performance over prior state-of-the-art methods on downstream AD tasks.

Abstract

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