PIRN: Prototypical-based Intra-modal Reconstruction with Normality Communication for Multi-modal Anomaly Detection.
Authors: YITING LI
Xulei Yang
Jing Zhang
Sichao Tian
Jingyi Liao
Fayao Liu
Venue: ICLR 2026 Poster (2026)
Keywords: anomaly detection
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning

Abstract: Unsupervised multimodal anomaly detection (MAD) aims to detect anomalies by using both RGB and 3D modalities. However, existing methods struggle in few-shot scenarios where the number of normal training samples is limited. Specifically, cross-modal alignment approaches fail to learn reliable correspondences from scarce normal data, whereas memory-based methods often misclassify unseen normal variations as anomalies. To address these issues, we propose \mtd, a prototype-driven reconstruction framework equipped with explicit cross-modal knowledge transfer. Instead of relying on dense feature alignment or heavy memory banks, \mtd uses a compact set of learnable prototypes to capture diverse normal patterns and constrain feature reconstruction. Specifically, our framework incorporates three core innovations. We introduce Balanced Prototype Assignment (BPA), which employs balanced optimal transport to ensure uniform prototype utilization and prevent codebook collapse. Next, we propose Adaptive Prototype Refinement (APR), which uses gated prototype updates to dynamically expand the model's knowledge of unseen normal variations during inference. To enable each modality to assist the other in reconstructing, we further develop a Multimodal Normality Communication (MNC) module that exchanges high-level normal cues between modalities via gated cross-attention.  Extensive experiments on the MVTec 3D-AD, Eyecandies, and Real-IAD benchmarks validate the effectiveness of \mtd, where it consistently achieves superior performance compared to existing baselines under challenging few-shot settings.

Published as a conference paper at ICLR 2026
PIRN: POOTOTYPICAL-BASER INTRA-MODAL RECON- STRUCTION WITH NORMALITY COMMUNICATION FOR MULTIMODAL ANOMALY DETECTION
Yiting Li', Xulei Yang', Jing Zhang", Sichao
Sichao Tian', Jingyi Liao", Fayao Liu
1 Institute for Infocomm Research, A A*STAR, Singapore
School of Computer Science and Engineering, Macao University of Science and Technology IInstitute Medicinal Plant Development, Chinese Academy Medical Sciences and Peking
Union Medical College, China
{li liting, yang-xulei, liu (aaayyo)@astttee @a-star.edu.sg
ABSTRACT
Unsupervised multimodal anomaly detection (MAD) aims to detect anomalies by using both RGB and 3D modalities. However, existing methods struggle few-shot scenarios where the number of normal training samples is limited. Specif- ically, cross-modal alignment approaches fail to learn reliable correspondences from scarce normal data, whereas memory-based methods often misclassify unseen normal variations as anomalies. To address these issues, we propose PIRN, a prototype-driven reconstruction framework equipped with explicit cross-modal knowledge transfer. Instead of relying on dense feature alignment or heavy memory banks, PIRN uses a compact set of learnable prototypes to capture diverse normal patterns and constrain feature reconstruction. Specifically, our framework incorpo- rates three core innovations. We introduce Balanced Prototype Assignment (BPA), which employs balanced optimal transport to ensure uniform prototype utilization and prevent codebook collapse. Next, we propose Adaptive Prototype Refinement (APR), which uses gated prototype updates to dynamically expand the model's s knowledge of unseen normal variations during inference. To enable each modality further develop Multimodal Normality
assist the other in reconstructing, Communication (MNC) module that exchanges high-level normal cues between modalities via gated cross-attention. Extensive experiments the MV'Tec 3D-AD, Eyecandies, and Real-IAD benchmarks validate the effectiveness of PIRN, where it consistently achieves superior performance compared existing baselines under challenging few-shot settings.
INTRODUCTION
Costanzino et al. 2024 Long al. 2025) identifies anomalies by inspecting both RGB images and 3D point clouds. Compared with single-modality methods, MAD-based methods can reveal defects that are invisible either modality alone. However, existing MAD methods still struggle in few-shot scenarios where only handful
Multimodal anomaly detection (MAD; Wang 2023
of normal samples per class are available training (Fang et al.) 2023 Tian et al.) 2024 Huang al.) 2022). For example, cross-modal alignment approaches such CFM (Costanzino al. 2024)
and LSFA (Tu et al. 2024) attempt to learn dense correspondences between RGB and 3D modalities using only normal data. An anomaly is identified when features of modality cannot be predicted from those of the other modality. However, with very few normal samples, the learned mapping captures only narrow cross-modal correlations and fails unseen yet normal correspondences test time. Memory-bank methods such as M3DM (Wang et al. (202)) and SG-DM (Chu et al.) 2023) store normal features exemplars and detect anomalies by measuring the divergence between test sample and its k-nearest exemplars. With limited normal samples, memory- based models fail to capture the full range of normal variation, leading to false positives on mildly deviating test samples.
CCoresponning author.

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Published as a conference paper at ICLR 2026
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Figure Left: Comparison with state-of-the-art methods on the Eyecandies dataset (AUROC, metric). PIRN achieves superior anomaly detection accuracy using less than 1% of the training data, significantly outperforming existing methods in scaa-e scenarios. Right: t-SNE visualization of patch tokens and prototypes in the RGB decoder feature space (MVTec 3D-AD, 10-shot setting). BPA (bottom) yields a more uniform prototype distribution over normal features, whereas a softmax assignment (top) results in underutilized/collpseed prototypes.
As such, both alignment- and memory-based approaches degrade significantly in the challenging few-shot settings (see Fig. Left).
We address these limitations with PIRN: Prototypical-base Intra-modal Reconstruction with Normality Communication for few-shot MAD. Rather than overfitting to sparse data via dense cross-mode matching or relying on large memory banks, PIRN emphasizes robust Intra-modal Fea- ture Reconstruction using vector-quantized codebook of discrete normality-ave aware prototypes (Van) Den Oord et af.) 2017). By reconstructing the features of each modality from a compact codebook, PIRN enforces information bottleneck (Alemi et al. 2017 Seo al.) [2023] (Zhang et al.) (024b) that retains only essential patterns of normal texture and geometry while ignoring irrelevant details. Consequently, anomalies that cannot be well represented by the prototypes yield large reconstruction
errors.
Prototype-based reconstruction in few-shot scenario faces several challenges. First, standard soft- assignment schemes (c.g., softmax attention) are prone to the codebook collapse issue (Zheng & Vedaldi 2023). During training, few prototypes tend to encode common normal modes, whereas the rest receive fewer updates and become inactive. This issue not only reduces the model's capacity but also narrows the prototype codebook's coverage of normality. Second, static prototype codebook learned from scarce training data often fails to capture wide variations in normality (Zhang et al.) 2024a| Wei et al.) 2023). As a result, a normal test sample may contain unseen yet still-normal patterns that cannot align with any learned prototype, leading false- positive predictions. Third, existing prototype- poosetype-based AD methods (Mao et al. 2025 Luo et al., (025) treat each modality in isolation, ignoring the complementary information between texture and geometry. Without effective cross-modal collaboration, subtle defects unique to one modality may go undetected.
We introduce three designs address the above limitations. First, Balanced Prototype Assignment (BPA) formulates the porootoo- matching task as a balanced optimal transport problem (Peyré & Cuturi 2019), thus ensuring that each prototype captures distinct normal pattern. This prevents codebook collapse and promotes diverse normal coverage with limited normal samples. As shown in Fig. [ Right, BPA yields a more uniform prototype distribution than softmax assignment. Second, Adapuve Prototype Refinement (APR) bridges the train-test distribution gap by treating the prototypes as adaptive memory at inference. APR uses lightweight GRU update the prototype vectors based on the test image's normal context, without corrupting them with anomaly contexts. This on-the-fly refinement expands the prototypes' coverage to new normal variations that are absent during training. Third, we introduce Multimodal Normality Communication (MNC) that exchanges prototypical normality knowledge across modalities via a two-stage process. The first stage aligns high-level normal concepts encoded by prototypes across modalities through graph refinement. In the second stage, these refined prototypes serve as anchors to guide fine-graincs feature reconstruction via cross noss-attention. As such, this allows each modality to reinforce the other's understanding of
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Figure 2: (a) Overview of PIRN. Given an RGB image and surface-normal map, PIRN uses pre- trained frozen encoders to extract features Ergb and F.gn- A prototype-aware multi-layer decoder then reconstructs these features into Z bpa (intra-modal purified) and Zunc (cross-modal purified), which used to generate anomaly maps. PIRN introduces three key components: 1) APR for adaptive prototype refinement to capture unseen normal patterns at test time; 2) BPA for balanced prototype assignment to mitigate codebook collapse; and 3) MNC for cross-modes prototype communication. (b) Details of the three components.
normality, enabling more discriminative detection of challenging anomalies (e-g., subtle defects) that might go undetected when each modality is used in isolation.
Together, these modules enable our model to learn and communicate normal patterns effectively across modalities, significantly improving anomaly detection in data-scarce settings. Our main contributions are summarized as follows:
We present PIRN modal Normality Communication framework for few-shot MAD.
a robust Prototypical-based Intra-modal Reconstruction with cross-
We introduce BPA to prevent codebook collapse and capture more diverse normal patterns. A lightweight APR module is further proposed to expand the prototypes' coverage to unseen yet normal variations at inference.
We propose MNC mechanism that shares normal information across modalities via cross-modal knowledge transfer, enabling each modality to help reconstruct the other's normal features and clearly highlight anomalies.
RELATED WORK
2D A Anomaly Detection. Many recent 2D anomaly detection (AD) methods constrain normal feature representations by using discrete prototypes to encode "normality." For example, HVQ-Tram (Lu cet al. 2023) preserves typical normal patterns as a vector-quantized prototype codebook, preventing the "ddentical shortcut" issue and ensuring anomalies cannot be perfectly reconstructed. Similarly,
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RLR (He et al. 2024) introduces learnable reference representation discourage shortcut solutions and explicitly model normal patterns, DPDL (Wang et al. 2025) learns multiple Gaussian prototypes and diffuses normal samples toward these cluster centers, forming a compact normal feature space to exclude anomalies. INP-Former (I.uo et al. 2025) extracts intrinsic normal prototypes directly external memory bank and achieving of-he- art performance in 2D AD tasks. Gong et al. (2019) introduce MemAE, a neemor--augmente autoencoder that utilizes an explicit memory bank record prototypical normal patterns, thereby constraining reconstruction to learned normality. Guo et al. (2023) propose a template complat-guidee guided approach, utilizing exemplars from the normal training library to guide the hierarchical restoration of input features, detecting anomalies via reconstruction deviations. However, lacking explicit cross-modal interaction, such methods are not directly applicable to MAD tasks.
from each test image, eliminating reliance
Multimodal Anomaly Detection. Existing MAD methods mostly rely cross-modal alignment or memory banks, with some exploring architecture search and distillation. Cross-modal alignment approaches (e.g.. CFM (Costanzino al. 2024), LSFA (Tu et al. 2024)) learn to align RGB and 3D features using only normal data, detecting anomalies when one modality's ' features cannot be predicted by the other. These methods fuse texture and geometry cues effectively but need diverse normal samples to establish reliable cross-modal correspondences. Alternatively, memory-based models such M3DM (Wang al. 2023) and SG-DM (Chu al., 2023) store normal feature patterns (either fused or modality-ppecifft and identify deviations as anomalies. Such methods suffer in few-shot settings: any unseen yet normal pattern not shown in the memory can lead to misidentification. Beyond alignment and memory methods, 3D-ADNAS (Long et al. 2025) optimizes feature fusion architectures via neural architecture search.
METHOD
3.1 FRAMEWORK OVERVIEW
To the best of our knowledge, PIRN (overview in Fig. 2 is the first multimodal anomaly detection (AD) framework to integrate vector vector-quantized prototype codebook into a Vision Transformer (VIT; Dosovitskiy et al. 2021 encoder-decoder architecture. Specifically, for each modality, we learn compact codebook of K vector-quantized discrete prototypes. These prototypes serve as reference points for typical normal textures and geometries, constraining reconstruction to rely solely on normal information.
Frozen VIT Encoder. We employ two parallel VIT encoders, Eig and Esn. which are pre-trained and kept frozen. Eeg processes the imput RGB image, while En processes the corresponding surface- normal map. We extract multi-scale features from a set of intermediate layers of each encoder and aggregate them via element-wise averaging to form a single feature map per modality (denoted Ergb and E.n. each in R NC)x) These aggregated feature maps serve as both the input to the decoder and the target for reconstruction.
Cascaded Prototype-Aware Decoder. The decoder consists of a stack of prototype-aware layers that progressively reconstruct normal version of input features. Each decoder layer performs three sequential operations. First, Adaptive Prototype Refinement (APR) updates each modality's prototype codebook via gated recurrent unit (GRU; Chung et al.) 2014). enhancing adaptability to the current sample. Next, Balanced Prototype Assignment (BPA) assigns each patch token to the updated prototypes via balanced optimal transport, promoting uniform prototype utilization. Finally, Multimodal Normality Communication (MNC) aligns the refined prototypes from both modalities through graph-bant -baed attention, and then exchanges high-level normality knowledge between the two modalities.
3.2 BALANCED PROTOTYPE ASSIGNMENT (BPA)
Allowing each token to softly match against all K prototypes can lead to a codebook collapse: some prototypes may eventually become under-utilized, reducing the diversity of normal patterns the codebook can represent. BPA addresses this issue by formulating the token-to-protee prototye assignment a balanced optimal transport (OT) problem. Instead of using softmax assignment that might
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over-concentrate few prototypes. BPA promotes two properties for more uniform prototype usage: (1) patch-to-prototype selectivity, encouraging each patch token to concentrate its mass only few prototype codes (controlled by the OT cost and entropic regularization); and (2) uniform prototype utilization, ensuring all prototypes receive a balanced share of patch assignments. Therefore, BPA encourages each prototype specialize in distinct normal pattern, yielding more diverse and representative codebook.
Specifically, let Z { / denote the of N patch tokens input given decoder layer (for the first decoder layer of each modality. Z equals the corresponding encoder output Ergh or E.n). Let P = {px ] applying BPA first refine the prototypes using APR (detailed in the next section), which adapts P to the normal context of the input image. This ensures that BPA operates on prototypes already tailored to the current sample.
k=1 denote the prototype vectors of a specific modality's s codebook. In practice, before
We then define cost matrix C E IR N K with Cuk =
Sa
representing the cosine distance
F., Pl
between patch token Zn and prototype Pk. BPA aims to find an optimal transport plan T E IR Nx that minimizes the assignment cost under equal-mast mass constraints:
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Tak Cnk
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This optimal transport formulation yields a balanced soft assignment to avoid trivial solutions (c.g.. all patches assigned single prototype) and ensures full prototype utilization. We solve using the Sinkhorn algorithm (Cuturi 2013) with entropic regularization. We then use the optimal plan T to reconstruct each patch token as a weighted combination of these prototypes.
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This effectively projects the input query tokens { JN onto the prototype space under the learned OT weights T". Thus, BPA acts as an information bottleneck by reconstructing each patch token from only limited set of normality-aware prototypes. As result, only normal patterns can be reliably reconstructed, and anomalous features in the query input are largely filtered out, leading to large reconstruction errors in anomalous regions. We refer Zopa (=bpa) x==1 intra-modal reconstruction, since they are derived solely from the normal prototypes of the same modality.
3.3 ADAPTIVE: PROTOTYPE REFINEMENT (APR) VIA OPTIMAL TRANSPORT
Our framework relies on set of prototypes {p:]/- as a compact codebook of normal patterns, which allows the model to capture diverse normal patterns during training and adapt to unseen variations test time. To achieve this, we introduce Adaptive Prototype Refinement (APR), which dynamically refines the prototypes using the normal context extracted from the current input. Importantly, APR operates on the patch tokens Z that are extracted from the previous decoder layer (or the encoder's output for the first decoder layer), before any reconstruction is performed in the current decoder layer.
To ensure that only normal patch tokens can contribute to each prototype, we compute an optimal transport aligmment between the patch tokens and the prototypes. Similar to Eq. (I). we derive the OT plan I This plan associates each prototype with a weighted subset of patch tokens 1 that best represents. We then compute context vector for prototype Pk-
in Z {2,
nommalz-normalized) Oe-whigtted mean its matched patch tokens: C₁ = > T 2ns where Fix denotes the k-th column of I* normalized such that Σ. [ 1.
This OT-based context extraction provides robust guidance for prototype refinement. By finding an optimal matching between prototypes and patch tokens under balanced constraints, out-of- distribution (anomalous) patch tends to be assigned more diffusely across prototypes (i.e., with low affinity any single prototype), thereby contributing weakly each prototype context. This
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encourages each prototype Pę be updated primarily using reliable in-distribution (normal) patches, enabling robust refinement even in the presence of minor anomalies. Finally, we update each prototype Ps: to Pk by incorporating its context C₁ via GRU, whose gating mechanism restricts the integration of unreliable anomalous contexts by leaving Pt essentially unchanged.
3.4 MULTIMODAL NORMALITY COMMUNICATION (MNC)
To
model complementary cues from texture (RGB) and geometry (surface normals), we introduce Multimodal Normality Communication (MNC) module that exchanges normal information between the two branches. The key idea is that each modality can assist the other in understanding normality, thereby better highlighting true anomalies and suppressing false positives. To ensure robust knowledge transfer, MNC exchanges prototype-base based normal knowledge between modalities, rather than raw patch features that may contain anomalies during testing. The decoder of each modality is guided to reconstruct features not only from its own prototypes but also from high-level normal patterns of the other modality. MNC operates in two stages: a prototype alignment stage and a cross-modal normality injection stage.
Stage 1: 2D and 3D Prototype Alignment. We treat all prototypes from both modalities as nodes in unified graph and perform cross-modal message passing to align them. Specifically, we construct graph with 2K nodes, consisting of K RGB prototypes and K surface-norma prototypes. We connect each prototype to its nearest neighbors in the other modality using KNN in the feature space of gn-oomaliized prototypes, and then apply multi-head Graph Attention Network (GAT: Veličkovid et al.] (2088) to propagate information across these edges. This graph-based refinement pulls the two sets of prototypes into a shared semantic space: prototypes representing similar structures (c.g.. a flat surface or an edge) are drawn closer and enriched with complementary context from the other modality. Let P ryb and en denote the refined RGB and ourface-norme prototype sets after this alignment. As a result, the two branches obtaín aligned prototypes that encode consistent cross- modal notion of normal texture and geometry. Similar prototype-leve alignment strategies (Huang et al. 2025 Tang et al. 2023 Pahde et al. 2021) have proven effective in multimodal representation learning
Stage 2: Cross-Modal Normality Injection. After alignment, the refined prototypes serve as anchors to guide fine-grained feature reconstruction via cross-attention. In this stage, each patch token from one modality will attend to the other modality's refined prototypes to inject any normal information lacks. To suppress anomalous details in test samples, we first purify each modality's patch tokens using its intra-modal information. Specifically, we use the intra-modal purified tokens = as an attention mask to reweight the original patch tokens 2n channel-wise. This yields purified tokens Z' {zn or ((zpp)))N Jn= where ((.) is the sigmoid function. These purified tokens Z' are then used as queries in the cross-modal attention.
For cross-modal knowledge exchange, we employ cross-attention layer Vaswanni al. 2017) where the refined prototypes of one modality act as keys and values, and the purified patch tokens from the other modality act queries. Taking the RGB branch as example, let Z',gb denote the purified tokens of the RGB branch and P' sn. denote the of stage-1 refined prototypes from the surface-normal branch. We compute the cross-attention output as:
Z. (P WK) (P Wv),
CA(Z' Cg : P' SoftMax
Vd
where Wo, WK, Wy are projection matrices and d is the channel dimension of Z Lgg.
To prevent overwhelming the patch features with irrelevant information, we introduce a learnable gating scalar y to modulate the cross-attentin output. Specifically, we add a scaled version of the cross-attention result to the original token representation:
righ Zr.gb + Grgb CA P',n), Zune Z',, + 9an CA(Z',, am P'
Grgb = tanh()rgb),
Jan = tanh(7ea).
where Yrih and Yan are learnable scalar parameters (one per decoder layer) and Irgh: 9m serve as gates on the cross-modal information. This gating mechanism allows the network control the extent

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Figure 3: Left: Compared existing MAD methods (10-shot), our anomaly maps are sharper with fewer false positives. Right: Comparison of anomaly score distributions for normal and anomalous samples (10-shot, MVTee-3D-AD). PIRN shows clearer distribution separation.
of cross-modal fusion for each layer. By exchanging high-level normality knowledge and injecting it into fine-grained patch tokens, MNC establishes robust correspondence between modalities at the prototype level. Unlike methods that attempt dense patch-to to to-patch alignment between modalities (which can be unreliable given limited data), our prototype-cent centrrrc exchange avoids direct dense mappings and thus offers greater robustness on unseen test samples.
We refer ygg and Zane cross-modal purified reconstructions, they are obtained using normal prototypes from both modalities. We then fuse bpa and Zmnc via glement-wise summation to produce the final reconstructed features for each modality: Z = bbpa + Znyo and Z
Zbpa + Zmne
Training and Inference We train PIRN end-to-end using intra-modal feature reconstruction loss, e.g., a soft mining loss (Luo et al. 2025). In practice, we minimize the cosine distance between the encoder's patch embeddings (Frgt and F.sn) and the corresponding reconstructed embeddings (Zygg and 7 across all spatial locations for both modalities. During inference, we compute an anomaly score map by comparing encoder-extracted features with reconstructed features. For
modality {rgb, sn) and the i-th patch, the anomaly score is d.T)) These patch-level anomaly maps are upsampled to the input resolution and summed across branches produce fused anomaly heatmap. The final image-level anomaly score is the maximum value in this fused heatmap.
cos (!!))) - Z
4 EXPERIMENTS
Implementation Details. We adopt ViT-Base 14 transformer the backbone encoder for RGB and surface-normal inputs, initialized with DINOv2 (Oquab et al. (203)) pre-trained weights and kept frozen during training. We follow FIND's (Li al. 2025) procedure to generate surface normal maps from 3D point clouds. To obtain robust multi-scale features, we aggregate patch tokens from layers 2-10 of the pretrained ViT by deement-wise averaging. Unless otherwise specified, we use K 10 prototypes per modality. The decoder is cascaded architecture with L = 2 layers. We use Adam (Kingma & Ba (014)) optimizer (learning rate 10-4) for epochs few-shot tasks and epochs in all-shot tasks.
Baselines and Evaluation Protocol. We compare PIRN with representative MAD baselines, including BTF (Horwitz & Hoshen 2023). AST (Rudolph et al. 2023), M3DM (Wang et al. 2023 ) CFM (Costanzino et al. 2024),, and AD-ADS [ongg al. (025). We also include the recent 2D prototype- reconstruction method INP-Former (Luo et al. 2025). To ensure a fair comparison in the multimodal setting, the 2D INP-Former baseline adapted two-stream architecture that
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1-8kot
Methed
AUROC;
AUROCy
AUPRO
AUROC,
AUROC,
AUPRO
AUROC
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0.671
0.590
0.503
0.652
0.724
0:325
0.952
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0.690
0.550
0.003
0.633
0.741
0.091
M38
BRUIIELLE Rumed
0.822
0.884
0.577
0.764
0.811
0.907
0.916
0.961
0:11
0.96
0339
0.793
0.967
0.984
0.447
1N7
0.51
0.911
0.917
0.839
0.922
0,991
0,856
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0.990
0.991
0.461
0.885
0.885
0.887
IneAAti Hldhen 20033
0.005
0161
01425
0.633
03334
0.315
Table 2:
Contribution of each compo- nent in few-shot regime, performance un- der 10-shot normal setting on MVTee-3D-
ASI
0.001
0346
0320
0,811
0.097
CTM
0865
0567
0.831
10-ADINI
0383
0.907
0.999
INP Poenat 1430000
Dars
0.855
0.922
0.89
8.91
0.900
0.905
0,872
0.912
0.447
0,870
0.896
AD.
R1F Rocolic Hlston 20233
0.936
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0.07
0.891
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0991
0085
0.65
0.866
6.8
0.983
0.96
0933
0.545
0.841
BGB valy
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6.5
Sobot
0472
3.90
6903
INP FourcT
BIG Surbes Namnah
6BM
0.9M
0.50
LONG
AST
HTF
Breestee Hodeen 1005
0.527
0.851
0.76
0991
0.944
0.99
0.780
0.740
0.002
0.344
0.948
0.311
USE
AlI
M3D
CIM
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0.445
0.92
0.91
0.91
0.964
0,882
0.881
0.777
0.887
0.857
BGB Surfane Nemaak
6.90
0.991
396
095
1575 Nommor
0.961
0.911
0.951
0.91
0.97
231T
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0.963
8.944
0.93
0.948
9.983
0.923
Sacfic Valealaady
8.427
0.477
34%
11915
Nerralk
Table Comparison anomaly detection and localization perfor- mance on MVTec-3D-AD and Eyecandies under different training shots.
Table Effect of modality availability (RGB-only vs. SN-only vs. RGB+SN) under different training shots on MVTec- 3D-AD.
processes RGB images and surface normal maps independently. Both branches use the same ViT-B/14 backbone and input resolution as in PIRN. Following the same post-processing (upsampling and Gaussian smoothing) used in our pipeline, their patch-level anomaly maps are fused via element-wise summation to produce the final predictions. We report AUROC at the image-level (AUROC)), pixel-level AUROC (AUROCp), and AUPRO following prior work (Costanzino al. 2024).
Main Results. As shown in Tab. 1 our method consistently outperforms the best-performing baseline on both HVVecc-3-ADD and Eyecandies across all metries in varying few-shot settings. On MVTec-3D-AD, it improves AUROC, by +3.9 (5-shot), +3.7 (10-shot), and +2.4 (50-shot) over the strongest baseline. On Eyecandies, it improves AUROC, by +3.6 (5-shot), +4.0 (10-shot), and +2.2 (50-shot). These consistent gains validate PIRN's effectiveness under extremely limited training data. PIRN also achieves the best performance in the full-shot setting.
Qualitative Analysis. As shown in Fig. PIRN enables a more accurate anomaly localization with fewer false positives. Furthermore, our method yields a more separable anomaly score distribution with larger margin and less overlap between normal and anomalous scores. These qualitative results further validate PIRN's effectiveness in few-shot scenarios. Fig. in the appendix further highlights the complementary strengths of the RGB and 3D branches. For texture-only anomalies, the RGB branch alone accurately localizes the defect region, whereas the surface-normal branch can produce false positives. We further report per-category results in Appendix Tab. 11
Ablation of Token Aggregation in APR. Tab. compares various token aggregation methods used by APR. Global averaging performs the worst (AUROC, 91.5%) due indiscriminate token pooling. Top-k averaging improves performance (AUROC, = 92.1%), whereas balanced OT yields the best results. The slight gain of balanced OT over top-k averaging indicates that balanced token contributions enable more consistent prototype learning.
Effect of Modality Availability/Rach Component Tab. 3 reports single-modality detection results. Across all shot settings, the model with surface normals (SN) consistently outperforms RGB, e.g., achieving 0.879 vs. 0.827 AUROC, at 10 shots. This indicates that geometric cues carry richer discriminative information for anomaly detection. Combining both modalities yields further improve- ment, with the performance gain most pronounced under the 5-shot setting, where each modality has limited coverage of normal variation. This confirms that MNC's cross-modal communication is particularly valuable when per modality representations are underrepresented due to scarce training data.
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Method
AUROC,
FLOPS. (G)
Latency. L (ms))
Protolypes K
AUROC,
AUROC,
AUPRO
Flmax,
M3DM (2023)
0.954
0,969
0.957
CFM (2024)
0.845
552.53
60.57
K 10
0.963
0.994
0.973
0.962
FIND (SOTA)
0.921
728.46
76.09
50
0.940
0.991
0,966
0.952
(Ours)
0.922
103.36
17.49
K 100
0.901
0.947
Table 4: Computation efficiency comparison on
Table 5: Ablation on the number prototypes K in
shot MVTec-3D-AD.
the all-shot setting on MVTec-3D-AD (normal-only).
Number of layers
AUROC;
AUROC,-
AUPRO
Method
AUROC,
AUROCp
AUPRO
0.921
0.993
0.967
w/o APR medule
0.916
0.990
0.961
L
0.924
0.994
0,968
Global Averaging
0.915
0,989
0.960
0.913
0.992
0.964
Top-k Averaging
0.921
0.991
0.964
0,869
0.985
0.949
Balanced Optimal Transport
0.922
0.991
0.966
Table 6: Ablation the number of decoder layers in the 10-shot setting (only normal data).
Table 7: Performance MVTec-3D-AD under differ- aggregation methods in APR.
Displacement Magnitude Comparisan (Bagel)
(2) Displacement Magnitudo Comparison (Cookie)
Figure 4: Visualization of Feature Displacement via BPA Routing. Green: normal; Red: anomalous.
We further validate each proposed module on the MVTec-3D-AD dataset, with results in Tab, The baseline model (first row) excludes all proposed modules. The full PIRN model achieves superior performance. Removing each component from the full model results in a consistent performance drop, validating the contribution of every component.
Effect of Codebook Size/Decoder Depth. Tab, 5 examines the prototype count K in the all-shot setting. With only K 5 prototypes, the codebook is too small to cover the diversity of normal patterns. contrast, a larger codebook size (K = 50/ degrades performance because excessive prototypes weaken the information bottleneck: it allows anomalous patches to find close matches and be faithfully reconstructed. We also conduct comprehensive ablation studies on the number of decoder layers (L) under the 10-shot setting (Table (), using only normal data. We find L 2 provides the optimal trade-off (0.924 AUROC;). Increasing the depth further (e.g., = 8) leads to significant performance degradation. This demonstrates that excessively deep decoders suffer from overfitting in this few-shot regime.
Computational Efficiency. To clarify the trade-off between complexity and performance, we compare PIRN with representative reconstruction-Dasee MAD baselines, including M3DM (Wang et al. 2023). CFM (Costanzino et al.) 2024), and the recent SOTA FIND (Li et al. 2025), on the 10-shot TeTe--3D-AD setting. We report accuracy (AUROC,) together with FLOPs and latency (input 224 x 224, batch size 1). As shown in Tab, 4 PIRN achieves the best AUROC, (0.922) while being most efficient, requiring only 103.36G FLOPs and 17. 17.49ms latency, which 85% fewer FLOPs and 4.35x faster than FIND (728.46G, 76.09ms).
Analysis of Prototype-based Normality Encoding To better interpret how PIRN's prototypes encode normality, we added new OT-movement visualization in Fig. For several MVTec-3D-AD categories (e.g., bagel, peach), visualize the displacement of patch tokens from their initial

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Published as a conference paper at ICLR 2026
Table 8: Comparison of different methods on the Real-IAD D3 dataset. The highest value in each row is marked in red, and the second highest value in blue.
AUBBC)
AEROC)
AUBOC
(ATBOCC
AEROC:
AITOCC
(MRROC
ALBOC
ATROCC
| (A/ROC)
AEROCC
0063
0244
0.713
0.326
0-363
04655
14.60
0.550
3.330
8,65
108
0663
0213
0.355
0.000
E1004
Jeaiigg Reul
0339
0.01
0.790
1.991
0.031
0.43
09814
0.716
3330
8.00
0.03
0076
0831
8.511
08013
0.011
0961
0773
18881
0.723
03401
03387
1655
1170
3330
0321
0073
1089
0282
0.38
0764
1054
CCTTI beaa
0.33
10114
0.88
0.9%
0836
0377
1621
0881
3513
0.721
1083
0086
0207
3.75
07377
0093
Card bakki
0333
036
0.364
0.30
0339
0465
14800
0.754
1330
0327
0373
1068
0.50
0.13
0395
0083
(1781
0005
08733
1.88
0341
0382
11303
01233
3339
8535
0011
0003
17.0
6130
(98)
13003
1001
18340
1.8%
0:301
1179
1591
2511
8525
1011
008
12800
3331
0382
108
(1633
03723
1389
0.39
0346
D431
0681
1730
3340
6127
6633
673
0739
3.50
1101
0003
01665
03733
0.80
0.50
6317
DA35
1176
01403
1.50
6162
1073
1053
0.23
3.50
6864
1035
01543
0351
11
034
0371
0865
0.35
3471
8.61
0981
0003
2380
8.771
6311
(197
feature space location (2pre) to their final location after BPA+APR+MNC reconstruction (Epost) We project prototypes and tokens into a shared 2D PCA space and draw the displacement vectors (A = Epose Spre). In the plots, translucent lines show per-token movements, and bold arrows indicate the average movement of normal (green) and anomalous (red) tokens. BPA encourages prototypes to serve as stable anchors for normal patterns. Normal embeddings that initially lie near prototype clusters undergo only small shifts during reconstruction. This suggests that the learned prototype codebook covers in-distribution normal patterns well. In contrast, anomalous tokens that initially lie farther away require larger displacements toward normal prototypes. The displacement histograms validates that our prototype-based reconstruction induces strong normal/anomalous discrimination.
Experiment results on Real-IAD D3 dataset. We further conduct comprehensive experiments on the challenging Real-IAD D3 (Zhu al., 2025) dataset in the full-data training setting. Real-IAD D3 comprises industrial components in real-world settings with various anomaly types and complex geometries. As Table 8 shows, PIRN achieves highly competitive performance. It achieves the best overall anomaly localization (AUROCp 0.961) 1) in 13 out of 20 categories, and it also achieves the second- best anomaly detection (AUROC, 0.873). In particular, D (AUROC, 0.890) uses unique tri- modal data representation (combining 2D, Pseudo-3D 3D and 3D imputs). In contrast, PIRN relies solely on two standard modalities: RGB images and surface normals. Despite utilizing fewer modalities, PIRN achieves better localization performance and significantly outperforms D in specific categories, such as fork__rrim_termmeeeee terminal (0.978 vs. 0.819 AUROC,) and lego proppeler (1.000 vs. 0.739 AUROC,).
5 CONCLUSION
We introduced PIRN, a novel framework for few-shot multimodal anomaly detection that unifies prototype-based intra-modal reconstruction with cross-modal normality communication. PIRN models normality from scarce data via an adaptive prototype codebook. Its effectiveness comes from three key innovations: Balanced Prototype Assignment (BPA) utilizes balanced optimal transport to mitigate codebook collapse. Adaptive Prototype Refinement (APR) dynamically adapts prototypes during inference bridge the train-test distribution gap. Multimodal Normality Communication (MNC) facilitates the exchange of high-level normality cues across modalities. Extensive evaluations across MVT 3D-AD, Eyecandies, and Real-IAD D3 demonstrate that PIRN establishes significant performance gains in challenging few-shot settings.
ACKNOWLEDGEMENT
This work is supported by the Agency for Science, Technology and Research (A*STAR) under its MTC Programmatic Funds (Grant No. M231.760021).
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A
APPENDIX
Algorithm provides pseudocode for the full PIRN pipeline.
Algorithm Pseudocode of the proposed PIRN framework.
1: Input: RGB image and surface-normal map
2: Output: Anomaly map (heatmap)
3: Extract multi-scale features Ergb and Esn using frozen ViT encoders
4: Initialize patch tokens: Zrgb &- Ergh. Zon x- Esn.
5: Randomly initialize prototypes Prgb and Pen-
6: for each decoder layer = to L do
7: Adaptive Prototype Refinement (APR)
8: Compute I rgb and r" by solving the balanced OT in Eq. ① for (Zrgb, Prgb) and (Z Psn)
r.gh and ck for each prototype by column-normalized OT-weighted 9: Compute context vectors Ck averaging of patch tokens
Update each prototype pr and p using GRU(PI. Balanced Prototype Assignment (BPA) Compute T and T by solving Eq. (T) for (Zrgh: Prgh) and (Zsny Pan)
11:
Reconstruct patch tokens: 2 Tg Pight Zbpa T Psn
(Bq.
14:
MNC Stage 1: 2D and 3D Prototype Alignment
15:
Align prototypes via cross-modal graph attention to obtain refined Pr and P
16:
MNC Stage 2: Cross-Modal Feature Injection
17:
Purify patch tokens: Lg = Zrgb of (Z
Z' = Zon (Z
18:
Compute coss-attention outputs: CA(Zrgb- Psn.) and CA(Z's.: Prgh)
(Eq.
19:
Apply gating to obtain Z,nbe and Zene
(Eq.
20:
Fuse reconstructions: Zrgb Zbpa + Zught
Z
Zbpa + Zgnn
rgb Zrec
21:
Update Zrgb f- 7rgh- Z
sn
22: end for
23: Compute per-modality patch anomaly scores: d/rab) = - cos( E((rb)) Z and d(
- cos( EE (an)
24: Fuse modalities to obtain the final anomaly map: d, = d(rrb) + g(on)
B MORE IMPLEMENTATION DETAILS
B. DETAILS GATED PROTOTYPE UPDATE VIA GRU.
To ensure that prototypes coherently represent normal features, we update each prototype by incorpo- rating this context vector {ca} through a gated recurrent unit (GRU) update. We treat the original prototype P& as the hidden state and its context C as the input to GRU cell, producing an updated prototype P'z-
The GRU's gating mechanism dynamically controls the extent to which each prototype is updated, ensuring that only normal context is integrated while minimizing the incorporation of anomalous information during testing. Formally, the update for prototype pk is given by:
Ug = W= [pa: cxl + b.).
(5)
Wr [psi ca) + b.).
(6)
P tanh W [Tk 2 pki ck] + b).
(7)
P/4 uu Pk (1 un) o Ps
(8)
where I: -] denotes vector concatenation, @ is element-wist wise multiplication, o(.) the sigmoid activation, and W., W,, W (with corresponding biases b2, b,, b) learnable weights. Eqs.
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RGB
Shape
RGB Pred
Shape Prod
Fused Prod
1. T.xttur Anomaly
2. Shape Anomaly
3. Texture Shape Anomaly
Figure 5: Qualitative results Eyecandies with various types of anomalies, showing the comple-
mentary roles of the 2D and 3D branch in PIRN.
RGB
Image
Surface Normal(SN)
RGB Branch Prediction
SN Branch Prediction
RGB + SN
Prediction
Localization Performance on Large Anomalies
Figure 6: Localization performance test samples with large pervasive anomalies (10-shot normal training). PIRN accurately highlights extensive defects, demonstrating the APR module's robustness against prototype corruption when anomalies dominate the input.
the standard GRU equations adapted to prototype refinement setting: uy is the update gate that decides how much the previous prototype Ps to keep, Fx is the reset gate that modulates the influence of the past prototype when computing candidate update, and Ps. is the candidate
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Method
AUROC,
AUROCp
AUPRO
Backbone
AUROC,
AUROCp
AUPRO
Softmax Attention
0.832
0.967
0.929
DINOvI ViT-B/8
0.892
0.974
0.946
Lincar Attention
0.845
0,968
0.931
DINOv2 ViT-B/14
0.923
0.993
0,968
Sigmoid Attention
0.954
Balanced Optimal Transport
0.922
0.991
0.966
DINOv2 ViT-L/14
0.928
0.994
0.970
Table 9: Ablation of prototype assignment in BPA on MVTee-3D-AD.
Table 10: Comparison of anomaly detection and local-
ization performance on MVTec-3D-AD under different backbones (10 shots).
prototype state. The final refined prototype P/. is convex combination of the old prototype and the candidate state, weighted by the update gate.
This GRU-based update mechanism is crucial for maintaining robustness during prototype refinement. the context Og aligns well with the original prototype, the GRU will produce a small ug, allowing the new information Pa to significantly override the old state Ps. Otherwise, if the context is unreliable due to an anomalous region that does not match any learned prototype, the update gate un will be high (near 1) keep the prototype unchanged. In this way, the GRU acts learnable gate: it adaptively suppresses anomalous information and only injects context when is deemed normal.
B.2 GENERATING SURFACE NORMAL IMAGES FROM POINT CLOUD.
We follow FIND's s et al. (205 procedure to generate surface normal maps. The MVTec 3D- AD dataset provides per-pixel 3DD point cloud data for each sample. Using these organized point clouds, we compute a corresponding surface normal map. According to FIND [i et al. (2025). background pixels in point cloud are removed following the standard procedure in M3DM (Wang et al. 2023). For the foreground pixels, we use Open3D to compute normals by fitting a local plane to each point's 's neighborhood (KD-tree search with nearest neighbors initial normal vector. We further enforce directional coherence using Open3D's orient.norre cons i stent tangent p ane with a connectivity setting of 50. After surface normal estimation, we project the computed normals back onto the image grid to form a normal map. For model training, we convert the normal vectors into a color image by linearly mapping each normal component the [0, 255] range. The resulting normal maps saved PNG images.
30). This produces an
B.3 HARDWARE FOR TRAINING.
All experiments were conducted workstation running Ubuntu 20.04 LTS. The system was equipped with two NVIDIA RTX 4090 GPUs with 128 GB of RAM. Our implementation was in Python 3.9 using Py Torch 1.13 (with CUDA 11.6 and cuDNN 8.1 for GPU acceleration). We also utilized OpenCV 4.8 for image processing and Open3D 0.17 for 3D data handling.
C ABLATION STUDIES
C.1 ADDITIONAL ABLATIONS ON BPA AND BACKBONE
We provide additional ablations analyzing (i) prototype assignment strategies in BPA and (ii) backbone architectures for the frozen encoders on MVTee-3D-AD. As shown in Tab. 9 softmax and linear attention yield the weakest results (AUROC, < 85%), suggesting that enforcing balanced prototype usage crucial for stable reconstruction under limited data. As PIRN uses frozen encoders, feature quality impacts performance (Tab. 10). On 10-shot MVTec 3D-AD, DINOv2 (ViT-B/14) outperforms DINÓvI (ViT-B/8), improving AUROC, from 0.892 to 0.923 (+3.1%) due to richer semantic representations. Scaling to ViT-L/14 yields marginal gains (0.928). Crucially, PIRN remains robustly competitive even with the sub-optimal DINOv1.
C.2 ROBUSTNESS TO LARGE-SCALE ANOMALIES.
Fig. shows qualitative results on test samples where anomalies occupy a large portion of the object. PIRN remains robust because APR performs constrained, single-step refinement around the learned
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Table 11: Comparisons per-category anomaly detection performance on MVTec-3D-AD.
Method
Bagel
Cable Gland
Carrot
Cookie
Dowel
Foam
Peach
Rope
Tire
Mean
AUROC,
BTF
Horwitz
2023
0.938
0.765
0.972
0.888
0.664
0.904
0.726
0.865
AST
Kusatan of al 2021
0.983
0.873
0.976
0.971
0.932
0.885
0.974
0.981
1.000
0.797
0.937
M3DAT
Wang
0.994
0,909
0.972
0.976
0.960
0.942
0.973
0.899
0.972
0.850
0.945
CFM
Cossanzin of Ml.
2024
0.994
0.888
0.984
0.993
0.888
0.911
0.954
3D-ADNAS (nyy er ml 2012
0.997
1,000
0.971
0.986
0.966
0.948
0.897
0.873
1,000
0.867
0.951
Shape Guided (CMi
0.986
0.894
0.983
0.976
0.857
0.990
0.860
0.947
PIRN
0.971
0.973
0.941
0.957
0.975
0.993
0.992
0.950
0.996
0.880
0.963
AUPRO@330
BTF
Horwitz Hoshen 2023
0.976
0.969
0.979
0.973
0.933
0.888
0.896
0.912
0.950
0.971
0.959
AST
Kudolph 2025
0.970
0.947
0.981
0.939
0.913
0.906
0.979
0.982
0.889
0.940
0.944
Wang
0.979
0.950
CTM
(C (Cosannimo et al 2024
0.979
0.972
0.982
0.945
0.950
0.968
0.950
0.943
0.950
0.981
0.971
Shape Golted
2023
0.981
0.973
0.982
0.962
0.978
0.981
0.974
0.975
0.976
PIRN
0.966
0.978
0.983
0.972
0.976
0.971
0.981
0.978
0.974
0.951
0.973
normal codebook rather than unconstrained test-time re-learning. First, APR extracts prototype contexts via dissimilar to all normal prototypes, thereby limiting the influence of anomalous regions. Second, the GRU gate (trained only on normal data) tends to close when the context is unreliable, preventing prototype corruption.
optimal transport, which assigns negligible mass to patches that are
C.3 PER-CATEGOR RESULTS ON MVTEC-3D-AD.
PIRN demonstrates strong detection performance the MVTec-3ID-AD dataset (Table 11 achieves a mean AUROC, of 0.963, surpassing the strongest baseline CFM (0.954) and the previous state-of-the-art 3D-ADNAS (0.951). It also outperforms other representative baselines such as M3DM (0.945) and AST (0.937). These results demonstrate that PIRN not only improves average detection accuracy but also localizes diverse texture and shape defects in complex anomaly detection scenarios.
D VISUALIZATION OF ANOMALY LOCALIZATION.
Fig. 7 and Fig. illustrate PIRN's anomaly localization results on the Eyecandies and MVTec 3D-AD datasets. respectively. These qualitative results highlight PIRN's robustness in capturing fine-grained defect details while avoiding false positives.
E LLM USAGE DISCLOSURE.
We used large language model to support language editing for the camera ready version, including improving clarity, grammar, and overall presentation of the manuscript. All scientific contribu- tions, such as research conception, methodological design, experimental setup, result analysis, and interpretation-wert carried out by the authors, who take full responsibility for the content.
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Published as a conference paper at ICLR 2026
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Figure 7: Visualization of localization performance on the Eyecandies dataset (5-shot normal training). From left right: RGB images, surface normals, anomaly maps predicted by the RGB branch, anomaly maps predicted by the surface-normal branch, the fused anomaly map.
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Published as a conference paper at ICLR 2026
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Figure 8: Visualization of localization performance the MVTec 3D-AD dataset (5-shot normal training). From left to right: RGB images, surface normals, anomaly maps predicted by the RGB branch, anomaly maps predicted by the surface-normal branch, the fused anomaly map.
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Reviews:
[{"review_id": "G5igVWY584", "reviewer": "Reviewer_tAGm", "summary": "This paper proposes a multimodal anomaly detection framework that builds on recent prototype-learning ideas. Its core concept is similar in spirit to INP-Former but introduces tailored modules for multimodal settings, especially in modeling cross-modal normality and refining normal prototypes. The authors identify three fundamental challenges in multimodal prototype learning and correspondingly design three key modules. The overall structure is clear, the motivation is sound, and the approach achieves strong performance compared to prior alternatives.\n\nHowever, the analysis of the proposed modules is relatively shallow. Without deeper insights into why these modules work and how they contribute, the technical credibility and reproducibility are weakened. Moreover, the experimental setup and comparisons contain unclear details that should be clarified for fairness and rigor.", "questions": "Can the authors clearly explain how Balanced Prototype Assignment differs from the coherence loss in INP-Former and INP-Former++? Are there theoretical or empirical justifications?\n\nSince RGB and normal maps capture shared and distinct characteristics, what is the motivation for aligning their prototypes? Is there a mechanism to preserve modality-specific cues rather than forcing strict alignment?\n\nIn Table 1, is INP-Former trained only with RGB, while others use RGB-D? If so, how is fairness ensured in comparisons?\n\nCan the authors provide deeper interpretation—for example, visualizing the correspondence between learned prototypes and latent features—to explain how prototypes encode normality?\n\nWhy does the method generalize so well in the few-shot regime? Which part of the design contributes most?\n\nDoes this problem occur only under few-shot settings or also in full-data training?\n\nWill the authors evaluate on Real-IAD3 or other emerging multimodal anomaly detection datasets to further validate effectiveness?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The paper explicitly identifies core challenges in multimodal prototype learning and proposes targeted modules to address them. This gives the paper a logical progression.\n\nThe proposed components appear reasonable and well-grounded, with links to existing prototype-learning paradigms.\n\nThe method outperforms other approaches in the reported benchmarks, suggesting its effectiveness in multimodal anomaly detection.\n\nThe model performs well in few-shot scenarios, which is practically valuable for real-world industrial inspection.", "weaknesses": "The paper primarily provides quantitative results. There is limited explanation of how or why each module improves performance. This lack of insight weakens the claimed contributions.\n\nThe Balanced Prototype Assignment (BPA) appears conceptually similar to coherence loss in INP-Former. Without clearer distinctions, novelty remains questionable.\n\nSince RGB and normal maps capture complementary information, perfect alignment is unrealistic. The paper should justify why alignment is beneficial and how complementary features are preserved.\n\nIt is mentioned that INP-Former is trained only on RGB, while other methods in Table 1 seem RGB-D-based. This may make the comparison unfair.\n\nFigure 2 is visually unclear and difficult to follow; Figure 4 offers limited insight and could be condensed.\n\nEyecandies and MVTec 3D are largely saturated; broader evaluation (e.g., Real-IAD3) would strengthen the experimental evidence.\n\nIt is unclear whether this issue occurs only in few-shot cases or in general.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "EuZIh8jH67", "reviewer": "Reviewer_HJaY", "summary": "Existing multimodal anomaly detection methods often perform poorly in few-shot scenarios. To address this issue, the paper introduces a prototype-driven, reconstruction-based method. It leverages prototypes to build a codebook that filters out anomalous features in the bottleneck. The method also aligns RGB and depth information, integrating multimodal data into the reconstruction process. Finally, anomalies are localized based on the reconstruction error.", "questions": "Please refer to \"weakness\" section.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "+ The proposed codebook-based reconstruction approach is well-suited for the few-shot problem. Additionally, the optimal transport strategy makes excellent use of the limited available normal samples.\n+ The idea of having modalities mutually assist in reconstruction is highly novel, and the results show it to be quite effective.\n+ The experiments in both few-shot and all-shot settings effectively demonstrate the method's validity. The ablation study is also thorough and convincing.", "weaknesses": "+ The prototype-based reconstruction method is quite similar to the approaches in some previous works [1, 2]. However, these papers were not cited.\n+ The proposed design appears to be quite complex. Its computational complexity seems significantly higher than that of previous reconstruction-based methods. It would be helpful to know if the authors have considered this trade-off.\n+ The paper lacks a clear justification for the choice of the number of decoder layers (2 for few-shot and 8 for all-shot). The authors should consider adding this as a factor in the ablation study.\n+ There is a minor issue of a duplicate citation for the paper \"Revisiting multimodal fusion for 3d anomaly detection from an architectural perspective.\"\n\n[1] Gong, D., Liu, L., Le, V., Saha, B., Mansour, M. R., Venkatesh, S., & Hengel, A. V. D. (2019). Memorizing normality to detect anomaly: Memory-augmented deep autoencoder for unsupervised anomaly detection. In Proceedings of the IEEE/CVF international conference on computer vision (pp. 1705-1714).\n[2] Guo, H., Ren, L., Fu, J., Wang, Y., Zhang, Z., Lan, C., ... & Hou, X. (2023). Template-guided hierarchical feature restoration for anomaly detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 6447-6458).", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "OgnSexnTka", "reviewer": "Reviewer_sM1V", "summary": "The paper proposes a multimodal anomaly detection method (RGB + Surface normals) named PIRN. The method is based on the idea that every image can be reconstructed without anomalies with correctly chosen “prototypes” if it is only trained with normal data. The paper proposes two main ways to improve the baseline method (INP-Former): the first one lies in the improvement of the extracted prototypes (balancing + refinement), and the other lies in combining the prototypes extracted from the RGB image and the surface normals. The method is then evaluated on two popular datasets, MVTec3D and Eyecandies, in both low-shot (the authors use the word few-shot, but that is typically reserved for k <= 8) and full-data regimes. In both regimes, they achieve great results", "questions": "I have several questions. I have sorted them from most problematic to least problematic.\n \n1. What is the performance of PIRN in scenarios where only RGB or only Surface Normals are available?\n2. Does INP-Former get only the RGB as the input or also the surface normals? Is the performance improved if the surface normals are used as input as well?\n3. How much time is required to train PIRN, and what is the inference speed of PIRN? What is the inference speed of the model without APR?\n4. How many prototypes are used?\n5. How does PIRN work with other backbones?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- Cross-modal prototype interaction is a well-thought-out solution. The results in the ablation study confirm this\n- The model works extremely well in a low-shot scenario.", "weaknesses": "- The method fails to compare with some methods that outperform it in the full-shot scenario on MVTec 3D: 3DSR (WACV 24) and TransFusion (ECCV 2024). As these two methods are trained with synthetic anomalies, I would assume they do not work as good as PIRN in the low-shot scenario. I would recommend adding them at least to Table 5.\n- It is unclear how INP-Former is trained for comparison on MVTec 3D. Are both modalities input, or only one?\n- The number of prototypes is not specified anywhere\n- It is unclear how the model performs in scenarios where only RGB or only Surface Normals are available\n- How robust is the model to the choice of the feature extractor? Let us say, what if we used a DINOv3 ViT instead of the “standard” ViT?\n- Computational complexity is not compared to other methods", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "O66aAKURCR", "reviewer": "Reviewer_tn1N", "summary": "This paper introduces PIRN, a framework for few-shot multi-modal anomaly detection that leverages a prototype-based intra-modal reconstruction approach. The core contribution lies in three synergistic components: Balanced Prototype Assignment (BPA) to mitigate codebook collapse, Adaptive Prototype Refinement (APR) to handle unseen normal variations at test time, and Multi-modal Normality Communication (MNC) to enable knowledge transfer between modalities. By focusing on reconstructing features from a compact, adaptive codebook of normal patterns, PIRN addresses the limitations of existing methods in data-scarce scenarios and achieves new state-of-the-art performance on benchmark datasets.", "questions": "- Have you investigated the failure cases of the APR module when dealing with test samples containing large or pervasive anomalies? Is there a mechanism to prevent prototype corruption in such scenarios?\n- BPA is designed to prevent prototype under-utilization, so I am wondering that is there a risk of learning *redundant* prototypes when *K* is large relative to the few-shot training data, where multiple prototypes capture nearly identical normal patterns?\n- How much does the performance of PIRN depend on the quality of this pre-training (e.g., DINOv2 vs. standard ImageNet pre-training)?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "- The paper addresses a practical problem of few-shot multi-modal anomaly detection in industrial quality control. The proposed prototype-based reconstruction paradigm is an elegant solution.\n- The technical contributions are clearly presented. Each of the three key components (i.e., BPA, APR, MNC) is designed to solve a specific challenge.\n- The experimental evaluation demonstrates significant performance gains over strong baselines across multiple datasets and various few-shot settings. The ablation studies effectively validate the contribution of each proposed module.", "weaknesses": "- My main concerns lie in the potential computational complexity and practical deployability of the proposed PIRN framework. The model incorporates multiple computationally intensive steps within each decoder layer, including two iterative optimal transport calculations for BPA and APR, and a graph attention network for MNC. The paper lacks any discussion on inference latency or FLOPs, which is a critical metric for real-world industrial inspection systems that often require high throughput. This omission makes it difficult to assess whether the impressive accuracy comes at the cost of practical usability.\n- The robustness of the APR module relies on the assumption that anomalies are sparse. Its effectiveness might degrade in cases of large-scale anomalies, where the *normal context* itself becomes contaminated.\n- The performance of the model seems highly dependent on several key hyperparameters, most notably the number of prototypes *K*. No sensitivity analysis is provided to show how performance varies with these choices.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)