See More, Detect Less? Taming Information Leakage in Multi-View Anomaly Detection

Shang-Fu Chen; Kuan-Chuan Peng; Jhih-Ciang Wu; Wen-Huang Cheng; Kai-Lung Hua

cs.CVAug 25, 2026

See More, Detect Less? Taming Information Leakage in Multi-View Anomaly Detection

Authors: Shang-Fu Chen, Kuan-Chuan Peng, Jhih-Ciang Wu, Wen-Huang Cheng, Kai-Lung Hua

Organizations: National Taiwan University Taipei, Taiwan · Mitsubishi Electric Research Laboratories (MERL) Cambridge, MA, USA · National Taiwan Normal University Taipei, Taiwan · National Taiwan University Taipei, Taiwan VinUniversity Hanoi, Vietnam · Microsoft Taiwan Corporation, National Taiwan University of Science and Technology Taipei, Taiwan

Abstract

In multi-view anomaly detection, more cross-view information can actually hurt. When multiple inspection views are naively fused in a reconstruction-based pipeline, normal cues from intact views propagate to the decoder, which faithfully reconstructs anomalous regions, collapsing the reconstruction gap the detector depends on. We call this failure mode cross-view information leakage and show that effective multi-view fusion must explicitly restrict the information reaching the decoder. Building on this insight, we present GLAD(Global-Local Attention Driven framework), the first framework combining vision foundation model features with local and global cross-view fusion for multi-view anomaly detection. The Multi-view Merging Attention (MMA) module performs local cross-view fusion at linear complexity with learnable view importance weighting and token-wise gating, letting each view selectively incorporate fine-grained evidence from other views at $\mathcal{O}(N)$ cost. The Object-Guided Attention (OGA) module captures global context by aggregating class tokens from all views into a single object-level representation and broadcasting it back to patch tokens via temperature-scaled sigmoid gating, replacing the original patch representations rather than adding a residual to preserve the reconstruction gap. Experiments on Real-IAD and MANTA-Tiny show that GLAD outperforms state-of-the-art methods across sample-, image-, and pixel-level metrics, confirming that principled information restriction is key to multi-view anomaly reasoning.

Figures & tables

Figure 1. Cross-view information leakage in multi-view anomaly detection. Unconstrained fusion obscures anomalies by collapsing the normal–abnormal reconstruction gap. GLAD restores this gap through information-restricted fusion. <short description> <long description>

AD Method Conditions	AD Categories
AD Method Conditions	${\mathcal{G}}_{0}$	${\mathcal{G}}_{1}$	${\mathcal{G}}_{2}$	IDIF [ Mao et al., 2025 ]	MVAD [ He et al., 2025 ]	GLAD
Single model for all classes	✗	✓	✓	✗	✓	✓
Multi-view input	✗	✗	✗	✓	✓	✓
Vision foundation model	✗	✗	✓	✗	✗	✓
Token-Level cross-view fusion	✗	✗	✗	✓	✓	✓
Object-Level cross-view fusion	✗	✗	✗	✗	✗	✓

Table 1. Characteristic comparison between GLAD (ours) and prior anomaly detection (AD) works (

{\mathcal{G}}_{0}

: [ Li et al., 2021 , Liu et al., 2023 , Hu et al., 2024 , Roth et al., 2022 , Defard et al., 2021 , Deng and Li, 2022 , Zavrtanik et al., 2021 , Chen et al., 2025 ] ,

{\mathcal{G}}_{1}

: [ You et al., 2022 , He et al., 2024b , Kashiani et al., 2025 , Wei et al., 2025 , He et al., 2024a , Zhang et al., 2025b ] ,

{\mathcal{G}}_{2}

: [ Zhang et al., 2025a , Guo et al., 2025 ] ).

Figure 2. Overview of GLAD . Multi-view images are encoded by a frozen pre-trained Transformer. The resulting tokens pass through the Object-Guided Attention (OGA) module for global object-level fusion and the Multi-view Merging Attention (MMA) module for local token-level cross-view fusion, before entering the MLP bottleneck and Transformer decoder for reconstruction. Right panels detail the MMA (middle) and OGA (right) architectures. <short description> <long description>

Method	Venue	S-AUROC	S-AP	S-F1	I-AUROC	I-AP	I-F1	P-AUROC	P-AP	P-F1	P-AUPRO
RD4AD [ Deng and Li, 2022 ]	CVPR’22	85.7	92.4	87.7	83.0	79.6	74.4	97.3	25.8	33.5	90.4
UniAD [ You et al., 2022 ]	NeurIPS’22	87.3	93.1	88.9	83.0	83.4	74.6	97.4	23.7	31.4	87.3
RD++ [ Tien et al., 2023 ]	CVPR’23	87.0	93.3	88.2	83.7	80.9	74.9	97.7	25.8	33.5	90.0
SimpleNet [ Liu et al., 2023 ]	CVPR’23	61.2	77.9	82.1	82.6	79.2	74.1	76.3	3.4	6.9	42.5
DeSTSeg [ Zhang et al., 2023 ]	CVPR’23	89.0	94.6	89.2	82.3	79.2	73.2	94.6	37.9	41.7	40.6
RealNet [ Zhang et al., 2024a ]	CVPR’24	72.0	87.1	82.5	65.2	65.4	62.7	60.5	27.2	24.9	28.7

Table 2. Average performance comparison on Real-IAD dataset. The best and second-best results are marked in bold and underlined . All methods except MVAD are reproduced under the multi-view setting.

Method	Venue	S-AUROC	S-AP	S-F1	I-AUROC	I-AP	I-F1	P-AUROC	P-AP	P-F1	P-AUPRO
RD4AD [ Deng and Li, 2022 ]	CVPR’22	78.3	63.7	68.3	80.5	59.5	62.4	91.8	29.1	34.7	78.8
UniAD [ You et al., 2022 ]	NeurIPS’22	74.9	59.7	65.2	82.7	64.3	65.1	90.7	26.1	32.4	79.6
DeSTSeg [ Zhang et al., 2023 ]	CVPR’23	62.4	49.3	56.5	62.3	37.8	45.5	54.8	12.0	9.0	23.4
RealNet [ Zhang et al., 2024a ]	CVPR’24	55.1	40.9	51.7	57.3	31.8	41.3	50.0	10.3	4.4	15.1
ViTAD [ Zhang et al., 2025a ]	CVIU’25	87.3	80.5	75.9	87.7	75.3	71.0	94.7	39.4	43.1	80.2
MVAD [ He et al., 2025 ]	TMM’25	85.0	76.5	73.8	85.2	69.9	67.9	93.7	32.7	38.5	80.7

Table 3. Average performance comparison on MANTA dataset. The best and second-best results are marked in bold and underlined . All methods except MVAD are reproduced under the multi-view setting.

Dataset	MMA	OGA	S-AUROC	S-AP	S-F1	I-AUROC	I-AP	I-F1	P-AUROC	P-AP	P-F1	P-AUPRO
Real-IAD	✗	✗	90.3	96.2	91.7	89.4	86.8	80.3	98.9	43.1	47.3	94.0
	✓	✗	92.5	96.3	91.6	89.8	87.2	80.8	98.9	42.7	47.0	94.4
	✓	✓	92.4	96.3	91.4	89.7	87.2	80.3	98.9	44.7	48.2	94.9
MANTA	✗	✗	92.3	87.9	83.4	91.3	82.3	77.7	94.8	44.8	48.3	84.2
	✓	✗	92.8	88.3	83.4	91.8	82.8	77.9	94.9	44.6	47.6	85.1
	✓	✓	92.7	88.5	83.6	92.0	83.5	78.1	95.2	47.6	49.6	85.9

Table 4. Ablation study on the contribution of each proposed module. MMA primarily improves sample-level detection, while OGA further boosts pixel-level localization. The best results per dataset are marked in bold .

Approach	Params (M)	Fwd (MB)	Fwd+Bwd (MB)	Time (ms)
Deformable Attention	2.37	2208.1	3091.1	17.38
MVAS Block [ He et al., 2025 ]	3.86	1318.1	9276.0	47.51
Linear Attention (ours)	2.66	528.5	1312.9	12.04

Table 5. Efficiency comparison of cross-view fusion alternatives in the MMA module.

Approach	S-AUROC	I-AUROC	P-AUROC	P-AP	P-F1	P-AUPRO
Deformable Attention	92.4	89.7	98.9	43.3	46.7	94.4
Linear Attention (ours)	92.5	89.8	98.9	42.7	47.0	94.4

Table 6. Accuracy comparison of cross-view fusion alternatives in the MMA module.

TR.	SG.	NoG.	Temp.	S-AUROC	I-AUROC	P-AUROC	P-AP	P-F1	P-AUPRO
✗	✗	✗	✗	92.2	91.3	94.7	43.7	46.9	84.0
✓	✗	✗	✗	92.4	91.7	95.0	47.0	49.4	85.4
✓	✓	✗	✗	92.3	91.8	95.1	46.8	49.3	85.4
✓	✗	✓	✗	92.4	91.7	95.1	47.7	50.0	86.0
✓	✓	✓	✗	92.6	91.9	95.3	47.7	49.8	85.7
✓	✓	✗	✓	92.4	91.8	95.2	47.8	50.1	85.9

Table 7. Ablation study on OGA information-restriction mechanisms. All variants include MMA and OGA. TR.: token replacement; SG.: sigmoid gating; NoG.: no global token feedback to per-view CLS tokens; Temp.: sharpened temperature. The best results are marked in bold .

Figure 3. Qualitative comparison of anomaly maps on (A) Real-IAD and (B) MANTA. We show the ground-truth (G.T.) annotation and the anomaly maps produced by MVAD, Dinomaly, and GLAD (ours). Warmer colors indicate higher anomaly scores. <short description> <long description>

Setting	S-AUROC	S-AP	I-AUROC	I-AP	P-AUROC	P-AP	P-F1	P-AUPRO
Baseline (all 5)	92.37	96.28	89.73	87.25	98.93	44.67	48.26	94.89
w/o C2	91.13	94.84	89.44	87.00	98.92	44.74	48.38	94.80
w/o C2,C3	90.57	93.11	89.10	86.50	98.91	44.55	48.18	94.78
w/o C2,C3,C4	90.12	89.81	88.99	85.66	98.83	44.02	47.84	94.58

Table 8. Missing-view experiment. We mask out C2, C3, and C4 and renormalize view-importance weights over the remaining views without retraining.

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GeoMAD: Geometry-Aware Multi-View Anomaly Detection via Deformable Fusion and Distributional Alignment

Shang-Fu Chen, Jhih-Ciang Wu, Kuan-Chuan Peng, Wen-Huang Cheng+1

National Taiwan University Taipei, Taiwan · National Taiwan Normal University Taipei, Taiwan · Mitsubishi Electric Research Laboratories (MERL) Cambridge, MA, USA+3

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Open paper AskPDF arXiv

Multi-view anomaly detection (MvAD) detects defects by exploiting complementary observations from multiple camera viewpoints. The central challenge is to fuse views with sufficient geometric awareness while remaining scalable to multi-class industrial settings. Existing methods typically fall into two extremes: voxel-based fusion provides explicit geometric alignment but requires costly 3D construction and class-specific assumptions, whereas lightweight patch-based fusion is efficient but relies on discrete candidate matching and lacks continuous cross-view correspondence. In this paper, we propose GeoMAD, a unified multi-view, multi-class AD framework that addresses both geometric correspondence deficiency and distributional inconsistency. Our Cross-view Deformable Fusion Module (CDFM) learns content-adaptive, view-pair-specific sampling offsets directly on 2D feature maps and arranges them across a multi-scale window pyramid with image-global reference sampling, enabling hierarchical cross-view correspondence without camera calibration, voxel construction, or class-specific 3D supervision. We further introduce Distributional View Alignment (DVA), a self-supervised cross-view regularization loss that aligns each view's bottleneck distribution against a per-instance view-centric target, enforcing global consistency without pixel-level correspondence. Together, CDFM and DVA bridge local geometric correspondence and global distributional consistency, providing geometry-aware and distribution-consistent fusion while preserving the efficiency of 2D feature-space learning. Extensive experiments on Real-IAD and MANTA-Tiny show that GeoMAD achieves strong detection and localization performance in unified MvAD.

Abstract

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