Logical Anomaly Detection with Masked Image Modeling
Authors: Shunsuke SAKAI
Tatsuhito Hasegawa
Makoto Koshino
Venue: Accepted by TMLR ()

Abstract: Detecting anomalies such as an incorrect combination of objects or deviations in their positions is a challenging problem in unsupervised anomaly detection (AD). Since conventional AD methods mainly focus on local patterns of normal images, they struggle with detecting logical anomalies that appear in the global patterns. 
To effectively detect these challenging logical anomalies, we introduce \textbf{L}ogical \textbf{A}nomaly \textbf{D}etection with \textbf{M}asked \textbf{I}mage \textbf{M}odeling (\textbf{LADMIM}), a novel unsupervised AD framework that harnesses the power of masked image modeling and discrete representation learning. 
Our core insight is that predicting the missing region forces the model to learn the long-range dependencies between patches. 
Specifically, we formulate AD as a mask completion task, which predicts the distribution of discrete latents in the masked region. 
As a distribution of discrete latents is invariant to the low-level variance in the pixel space, the model can desirably focus on the logical dependencies in the image, which improves accuracy in the logical AD.
We evaluate the AD performance on five benchmarks and show that our approach achieves compatible performance without any pre-trained segmentation models. 
We also conduct comprehensive experiments to reveal the key factors that influence logical AD performance. Code is available at: \url{https://github.com/SkyShunsuke/LADMIM}.

Logical Anomaly Detection with Masked Image Modeling
Shunsuke Sakai
sshomasake011222
Department of Engineering University of Fukui
Tatsuhito Hasegawa'
t-hase www-fkuii.a.. ac.jp
Deportment Engineering University of Fukui
Makoto Koshino
koshino wwwhhhnowis@sshaanan.t...
Department of Electronics and Information Engineering
National Institute of Technology, Ishikawa College
Reviewed on OpenReview:
https: openreview. net/ forum? id- wuuaRCHYE3
Abstract
Detecting anomalies such as an incorrect combination of objects or deviations in their posi- tions is challenging problem in unsupervised anomaly detection (AD). Since conventional AD methods mainly focus local patterns of normal images, they struggle with detecting logical anomalies that appear in the global patterns. To effectively detect these challenging logical anomalies, we introduce Logical Anomaly Detection with Masked Image Modeling (LADMIM), a novel unsupervised AD framework that harnesses the power of masked image modeling and discrete representation learning. Our core insight that predicting the missing region forces the model to learn the long-range dependencies between patches. Specifically, we formulate AD a mask completion task, which predicts the distribution of discrete latents in the masked region. As distribution of discrete latents is invariant to the low-level variance in the pixel space, the model can desirably focus the logical dependencies in the image, which improves accuracy in the logical AD. We evaluate the AD performance on five benchmarks and show that our approach achieves compatible per- formance without any pre-trained segmentation models. We also conduct comprehensive experiments to reveal the key factors that influence logical AD performance. Code is avail- able at: https://gitee http//ggtttuu..cee http::/ggggttuu.....otttStyStie SoommStySSunnnukeeAAttt.
1 Introduction
Aulomated visual inspection plays a key role industrial applications, ensuring quality and reliability while reducing human errors and improving efficiency. Anomaly detection (AD) has recently attracted considerable attention as promising approach to building high-performance visual inspection systems (Bergmann al.
2019 Bao et al. 2024). At the heart of AD lies the philosophy that anomalies can be detected by capturing the concept of normality. Since anomalies are characterized by their rare occurrence and diversity, an unsupervised AD that relies solely on normal samples without requiring any anomalies has become a central paradigm in the field of AD (Liu et al. 2024
In industrial images, defects can be classified into structural and logical anomalies. Structural anomalies refer to deviations from normal in the local features of the image, such as scratches or stains on manufactured products. On the other hand, logical anomalies deviations from normal in the global features of the image, such as misalignment of objects incorrect combinations of objects. Logical anomalies differ from those in normal data regarding the relationships between local fcatures. They are more likely to occur in problem seltings where multiple objects are presented in the image. Traditional benchmarks for industrial,
*Corresooddng Author.

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Published in Transactions on Machine Learning Research (3/2026)
such MVTec AD Bergmann al. 2019 and VisA Zou et al. 2022), have mainly addressed structural anomalies in images containing a single object. In contrast, the newly released benchmark MVTec 1OCOO [Bergmann et al. 2022) focuses on problems where multiple objects are presented in the image, allowing the evaluation of detection performance for both structural and logical anomalics. In practical applications, detecting logical anomalies oflen required; therelore, methods must. be developed to detect both logical and structural anomalics.
Current mainstream approaches deep convolutional neural networks to extract useful features to detect anomalies. Such features robust against noise, object rotation, and translation and provide rich repre- sentations of normal data (Cohen & Hoshen 2020; Roth ct al. 2021 Batzner et al. 2023). In particular, representations acquired through supervised pre-training on large, curated datasets like ImageNNt have high discriminative power against anomalies without any fine-tuning (Cohen Hoshen 2020 Roth et al., 2021). With the use of such pre-trained models, there has been a significant improvement detection performance for structural anomalics (Roth et al., 2021 Yu et al., 2021 Lu et al.) 2023). On the other hand, these meth- ods may have lower detection performance for logical anomalies (Bergmann al. 2022). Logical anomalies may not appear in the local features themselves but in the relationships between local features, and current mainstream approaches consider only local features (Bergmann et al. 2022
In this study, we introduce self-supervised learning on normal images using Masked Image Modeling (MTM) to learn the relationships among local features of normal images. In MIM, part of the input image is masked, and the model is trained to restore the features the masked region. To restore the masked region, is necessary understand the relationships among features in normal images. Therefore, MIM can effectively learn dependencies among local features. However, when simply predicting the pixel-level image features of the masked region, there is a problem of blurred reconstruction. This issue fundamentally arises from the uncertainty of feature positions in the masked region. To mitigate this problem, we propose to predict the probability distribution of discrete latent variables in the masked region. The probability distribution of discrete latent variables represents the composition of visual features within the masked region and is invariant to the positions of the features.
This study aims improve detection performance for both logical and structural anomalies with masked image modeling. The contributions of this study as follows:
We propose a novel framework, LADMIM, which has a ViT-based architecture and leverages MIM and Hierarchical Vector Quantized Transformer (HVQ-Trame (Lu al.) (023) for detecting logical and structural anomalies, respectively. LADMIM mitigates the issue of positional uncertainty in AD by predicting the probability distribution of discrete latent variables for the target.
We verify our framework on MVTeeLLOO (Bergmann et al. 2022 and MVTecAD (Bergmann) ct al. 2019), achieving hato-of-tho-an detection accuracy compared with conventional MIM-basod mothods.
Since MIM-based AD essentially avoids the need for tuning information bottlenecks, we demonstrate that our proposed LADMIM is amore robust approach compared with conventional non-MIM based AD (Fig. 3) thereby LADMIM can be casily adapted to more complex logical AD problems.
Our proposed framework represents a departure from existing paradigms, paving the way for a new line of research that has the potential to establish a more robust and unified framework through future developments.
2
Related works
2.1
Unsupervised Logical Anomaly Detection
Logical anomalies are deviations in logical relationships of objects in normal images, which are different from traditional structural anomalies such as cracks and bends (Bergmann al. 20222.. Detecting such logical anomalies requires both object-centric representation and global-context modeling, which makes it challenging. Previous unsupervised AD literatures attempt to improve detection accuracy by enhancing
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either of these two aspects both. Current predominant approaches to detecting logical anomalies are categorized into (a) reconstruction-base (b) memorybank-based. In Fig. we provide visualization of
key concepts of these approaches.
(a) Reconstruction- AD
Normal
[x 811
z
Memory bank M
||
(b) Memorybank-basee AD
ang pen
1
Anomaly
(c) MIM-based AD (Ours)
Figure 1: Conceptual comparison of different approaches in AD.
Reconstruction-bsse AD Batzner et al. 20233: Yao et al. 2023 2024 Sugawara & Imamura (2024): Lu
ct al. 2023 learns to reconstruct only normal images using autoencoders (Fig. [b). Because of the poor general lizailiiyy the model for reconstructing anomalous features, these anomalous features are ideally re- constructed corresponding normal features, enabling reconstruction error as a reasonable anomaly scoring metric. Although these reconstruction-based AD methods detect certain logical anomalies by using spatial bottleneck structure, they inevitably increase the reconstruction error for normal images Batzner et al. (2023); Lu et al. (2023). Additionally, in the absence of a low-dimensional information bottleneck, the model tends to learn an identity mapping, which conveys little information about anomalies while be- ing globally optimal with respect to the reconstruction objective. This shortcoming of reconstruction- based methods is known as the Identity-shorteut (ID-shorteut) Batzner et al. (023); et al. (2023).
sannoorybank--eeee methods Liu et al. (2023b); Kim et al. (2024) Roth al. (2021) store features of normal images in a storage called memorybank. During inference, these methods detect anomalies by measuring the distance between the input image's features and those stored in the memorybank. Some methods attempt to detect logical anomalies with memorybank and achieve competitive accuracy on public benchmarks Liu et al. (2023b) ; Kim et al. (2024). However, these lines of work require pre-trained segmentation networks to extract object-centric representations, thereby constraining their applicability and generalization capability.
2.2 Anomaly Detection with Masked Image Modeling
Masked Image Modeling (MIM) is a self-supervised learning framework that involves masking portion of the input image and lcarning to predict the features the masked region from the visible regions. MIM-based AD (Fig. T a promising approach for learning the logical relationships between objects and features in normal images. However, conventional methods (Huang et al. 2023a Li et al.) 2020 Yan et al.
2021 Schwartz al. 2024 Yao et al.) 2022 Yang al. (2023) struggle with low detection performance compared to other reconstruction-based and memorybank- nemorybank-based methods. A critical challenge of MIM is the uncertainty in masked regions, which leads to undesirable prediction errors in normal regions. To mitigate this issue, we propose predicting the probability distribution of discrete latents in the masked regions, which is invariant lo the order of the latents. Additionally, we employ different models to delect logical and structural
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HVQ-Trans
0000 x 2000
ViT Encoder
ViT Decoder
Quantization
LuvO
Backbone
Network
ViT Encoder
MIMHead
EXIM
Random Masking
Prediction p
Target Q
ViT w/ MIM
Figure 2: Overview of our framework. Our framework consists two main components: HVQ-Trans Trans and ViT trained with the MTM objective.
anomalies, respectively. In summary, our proposed AD framework can ideally capture the challenging logical relationship of normal images, which in turn contributes to more accurate detection for both logical and structural anomalies.
3
Methodology
3.1 Overview of the Proposed Framework
An overview of the proposed framework is shown in Fig. 2 The proposed framework detects structural and logical anomalies using different mechanisms. We call this framework for both logical and structural AD Logical Anomaly Detection with Masked Image Modeling (LADMIM)) Structural anomalies are detected using the reconstruction error of features by HVQ-Trans Lu et al. (2023). which also serves as the tokenizer. Logical anomalies are detected through the prediction error of the probability distribution predicted by the ViT-based model, which is self-supervised via MIM using the probability distribution of discrete latent variables from IVQQ-rran
the prediction target. The anomaly scores calculated by cach mechanism are then integrated to compute the final anomaly score of the input image. The normality or anomalousity of an image is determined by applying an appropriate threshold to the anomaly score.
Compared to reconstruction-based methods Batzner et al. 2023 Yao et al. 2023 2024), the proposed framework does not suffer from the ID-shoricut of the inpul because identity mapping results in large reconstruction errors in the masked region. As discussed in Section 4.4 since the ID-shorteut does not occur in our framework, LADMIM can be scaled larger model.
Anomalies are detected through the interaction of more abstract visual features by running predictions in the discrete latent space. Morcover, when masking is applied to the input image, the proposed framework can be considered a distillation-basee method, where HVQ-Trans acts as the teacher and ViT as the student. Masking the input image in the proposed framework promotes learning of long- range dependencies among features normal images, compared to distillation--aser methods Batzner et al. (2023): Guo al. (2023) Zhang et al. (2023). Compared to memory bank-based methods Liu al. (2023b); Kim et al. (2024). the proposed [ramework shares similarities with PSAD Kim et. al. (2024) and ComAD Liu al. (2023b) in using the area distribution of objects to represent normal features. While these methods Kim et al. (0224) Liu ct al. (2023b) require the separate preparation of sogmentation model, the proposed framework utilizes the discrete latent variables of HVQ-Trans, acquired through fully unsupervised learning via the image reconstruction task.

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The novelty of the proposed framework lies in (1) leveraging MIM prediction errors to detect logical anoma- lies and (ii) using the probability distribution (histogram) of discrete latent variables within the masked region as the MIM prediction target, rather than predicting position-wise tokens. To realize this, we employ HVQ-Trans as a unified module that simultancously supports structural anomaly detection and tokenization: it detects structural anomalies via reconstruction error while producing a hierarchical discrete latent. rep- resentation during reconstruction, which naturally serves as a tokenizer for MIM by enabling coarso-to-fine prediction at multiple granularity levels, capabilities not explicitly provided by non-hierarchical VQ variants that are not designed for structural AD. Our ViT is further designed to predict token histogram for the masked region, explicitly addressing positional uncertainty (e.g., local permutations or misalignment) and thereby facilitating the detection of position-invariant logical anomalies without relying on heuristic search procedures that can arise from an information bouteneck. This design is technically motivated by prior work combining MIM with VQ tokenizers Bao et al. (2021); Peng et al. (0022) where discrete visual to- kens provide compact, casicr-to-predict targets and reduce the risk of overfitiigg to unnecessary low-level details; building on this foundation, our histogram prediction extends MIM to a setting where robustness to positional ambiguity is essential. While performance gap to the current SoTA remains, we believe these design choices clarify the technical significance of using MIM with discrete token distributions for anomaly detection and highlight its future potential.
3.2 HVQ-Trans
Hicrarchical Vector Quantized Transformer (HVQ-Trans) Lu et al. method incorporating a hierarchical vector quantization mechanism. HVQ- Trans learns reconstruction model in the feature space of pre-trained models such as EfficientNet Tan & Le 2019). The reconstruction model consists of a 4-layer encoder-decoder Vision Transformer (ViT), where the output of each encoder layer is quantized, and the decoder reconstructs the features using the quantized latent. variables from the encodor. The quantization of latent variables alleviates the ID shorteut problem specific to reconstruction models, achieving SoTA performance among reconstruction--bssed methods on MVTecAD [Brgmann et al.) (2019) as of 2023. Additionally, hierarchical quantization in the HVQ- increases codebook usage, which is important for subsequent MIM VIT training Lu et al. (2023) Since a single large codebook in standard VQ can suffer from dead codes, especially when the number of assignments per update is small relative to codebook size, or when the encoder distribution is highly skewed. HVQ miligates this by decomposing a large vocabulary into multiple smaller codebooks arranged in a coarse-to- hierarchy. Because each sub-codcbook smaller, codes are selected more frequently and reccive more consistent updates, which improves per-level utilization. Moreover, the overall vocabulary corresponds to combinations across levels: if each level uses substantial fraction of its codes, the number of reachable composite tokens increases multiplicatively with the number of levels. This yields a large effective discrete space without relying a single massive codebook that is difficult to populate. In contrast, EMA updates van den Oord et al. (2017): Razavi et al. (2019); Zhang et al.] (2024) mainly stabilize code updates but do not revive never-selected codes; reset strategies Dhariwal et al. (2020); Zhang et al. (2024) can revive dead codes but are heuristic and introduce discontinuities; and RVQ Zhang et al. (2024): Lee et al. (2022) increases capacity but may still show stage-wise imbalance in utilization and increases quantization complexity for defining MIM targets. This is reason why we choose HVQ-Trans as source of discrete latents among various VQ methods van den Oord
2023 is reconstruction based AD
et al. 2017); Peng et al. (2022); Liu et al. (2023a); Huang et al. (2023b).
In this study, HVQ- Trans is used to detect structural anomalies and simultaneously serves as a tokenizer for detecting logical anomalics. In other words, the discrete latent variables inttornal representations of TIVQ-Trans are used as a teacher signal for models that detect logical anomalies.
From here, we describe the detection of structural anomalies using LIVQ-Trans First, the entire set of normal images is expressed as follows:
Dx = fx(0) M
J.m x09 E RCXHXW
(1)
Here, C, H, and W represent the number of channels, height, and width of the images, respectively. M represents the number of training samples (i.e., the number of normal images). Assuming the backbone
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network is før the feature extraction of normal image x(1) E Dv is expressed as follows:
h" = fo(x(1)) h Rdoxxxx
(2)
Here, do, and represent the dimensions, height, and width of the feature map, respectively. HVQ-Tram reconstructs this feature map h using an encoder-decoder Vir. The feature map, flattened along the spatial dimensions, is denoted as h Rdox where N hwc. The VIT encoder is constructed by stacking L layers of standard Transformer Blocks Vaswani et al. (017). and the features transformed by the I-th layer block d are expressed as h' = 9/4 (h"-1)" ) Raxx, Here, d is the embedding dimension of the VIT. The ViT decoder is similarly constructed by stacking L layers of Transformer Blocks. The input to the Transformer Blocks of the decoder is the quantized output each encoder layer. Assuming that the I-th layer's learnable discrete latent variable set (codebook) B' / the quantization of the final layer features of the encoder is expressed as follows:
z byd
where = argmin |/ (h*) -b by E Rd
(3)
The output of each encoder layer 1 < L is quantized using the output of the encoder's final layer obtained from Eq. g as follows:
z' by, where i = argmin||u (hh-1, 221) -by12: LE ( L-
(4)
Here, [ denotes the concatenation of two vectors, and (+) is an embedding map to the space of the dimension the discrete latent variables for each layer. In each layer's decoder block, the corresponding layer's discrete latent variable is used as imput, and the Multi-head Self-Attention (MSA) and Multi-head Cross-Attention (MCA) mechanisms Vaswani et al. (2017) are sequentially applied. Given the quantized latent variable z' of the (-th encoder layer, the output d' of the 1-th decoder layer computed using the output d'-1 of the previous decoder layer as follows:
q' = MSA (q = W,d' --k Wid'-1,v v w + d
d' = MCA (q W. k- W.''.k-W''vv. Wizz +
(5)
d' FFN (ā') + d'.
MSA(-) and MCA(-) represent multi-head self-attention and multi-head cross-attention mechanisms, respec- tively, which take query, key, and value inputs. FFN(-) represents foed-forward network consisting of two fully connected layers and an activation function. Tn the multi-head cross-attention mechanism of the decoder, the output of the previous decoder layer is transformed by the weighted sum of the discrete latent variables z'. This process replaces the anomalous features in q' with discrete latent variables, which are prototypes of normal features acquired during training, thereby increasing the reconstruction error in the anomalous areas.
During training, the input h of the encoder reconstructed using the output dt of the L-th layer of the decoder. That is:
T - (d"), ho E ddxxxxw
(6)
where I'(-) represents mapping to the same embedding space as h". The loss function used for training VVQ-Trans is defined as follows:
def
LHVQ
[|ig (h') b'll2 + Ilh' sg (b)
(7)

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Here, sg(+) represents stop-gradient operation that prevents gradients from being backpropagated. Since the quantization of latent variables involves non-differentiable operations, following Adiban et al. (2022) Bengio et al. (2013); van den Oord et al. (2017), this study estimates the gradient using the straight-through estimator (i.e., the gradient with respect to the encoder output is replaced by the gradient with respect to the quantized encoder features).
At inference time, the anomaly score is calculated using the Mean Squared Error (MSE) between h" and h Since vector quantization replaces continuous abnormal latents with discrete normal codes, abnormal patches in h are ideally reconstructed to corresponding normal patches. Thus, MSE is reasonable scoring metric to detect abnormal patches. On the other hand, HVQ-Trans is a method for detecting structural anomalies and employs a patch-wise bottleneck structure. Therefore, it is difficult for HVQ-Trans to detect logical anomalies that require long-range feature dependencies. Next, we describe our proposed method, which focuses on detecting logical anomalies.
3.3 ViT for Detecting Logical Anomalies
We use a ViT-based architecture to detect logical anomalics with MIM, which contrasts with previous CNN- based MIM approaches Huang et al. 20223a ; Li et al. (2020); Yan et al. (021). A standard ViT Dosovitskiy et al. 2020 trained on normal images using MIM, and during inference, it detects anomalies based on the prediction error of features in the masked regions. It uses the probability distribution of the discrete latent variables of HVQ-Trans for prediction. This approach mitigates the positional uncortainty of objects in the masked areas and prevents the model's predictions from being dominated by low-level features.
First, for a normal image x, the feature map obtained from the common Backbone Network fo with HVQ- Trans is flattened to obtain E RdxxN, When masking a proportion r (0 < r < 1) of the N feature tokens, the positions of the masked tokens are M [N], [M] = rN. At this time, the input to VIT is (V1, V2,..., VN, P) and:
h; ifiEM for E NY.
V; e ifi
(8)
Here, and are learnable vectors initialized as zero vectors. VIT is composed of L' layers of ordinary Transformer blocks. aggregates information from visible patch tokens via offf-attention and is then used to predict the distribution of discrete latents the masked region. The output p' of the final layer of ViT corresponding to the prediction token lincarly mapped to predict the probability distribution of the l-th layer discrete latent variable of HVQ-Trans:
pl = MIMHead = Softmax (Linear (p')),
(9)
where Pl RK represents the probability distribution over the discrete latent variables to be prodicted, which is calculated from the internal representation of HVQ-Trans. By quantizing the output of the I-th
layer of the encoder in HVQ-Trans, the set of the indices of discrete latent variables o' E R-V o E [K] is obtained. We call these obtained indices of discrete latent variables a code prediction map. The code prediction map from the I-th layer of the LIVQ-Tram is calculated as follows:
= arg
min mig ( z²I) - -b11
(10)
The probability distribution of the discrete latent variables to be predicted is derived from o'M = (0/)) 1 ie M}, which is restricted to the masked region:
Q' Histogram (g)).
R'ERK
(11)

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For each layer / E {1,2, L}. with the equations
9 and
11 the loss of ViT is defined as follows:
K PM ΣΣ
1
LMM
def
Q%
(12)
VIT is optimized with equation 12 as the objective function. By predicting features in masked regions, VIT learns the relationships among features in normal images. To mitigate the position uncertainty problem in masked regions, we employ a histogram matching loss, which is invariant to the permutation of discrete latent variables. During inference, if an area containing anomalous features not encountered during training is masked, the prediction error for that area increases.
3.4 Training and Inference Procedure
The proposed method trained two stages: (i) training HVQ-Trans, and (ii) training VIT with MIM. IVQQ-Tram is trained to minimize the loss Luvo through reconstruction task in the feature space. After training HVQ-Trans, ViT is trained to minimize the loss LMIM- During VIT training, the weights of HVQ Trans are frozen and are not updated.
During inference, the reconstruction error of HVQ-Trans is evaluated using the MSE, and the average MSE across the entire image is taken as the HVQ -Taans anomaly score Shvq for that image. Additionally, multiple random block-wise masks are generated, and the prediction error of the probability distribution for each mask is evaluated using the L1 distance, with the average being taken as the ViT anomaly score SMIM- The overall anomaly score of the proposed method is obtained by summing the standardized Shvq and SMIM, denoted as Sliv and Shm respectively.
4 Experiments
4.1 Dataset and Evaluation Metrics
MVTecLOCO Bergmann et al. (2022) is dataset focusing five types of industrial products, containing more than 300 normal images for each type and more than 3600 images in total. This dataset contains five different. AD tasks: Breakfast.Box (Box), JuiceBottle (Bottle), Pushpins (Pins), Screw Bag (Bag), and SplicingComnectos (Cable). It primarily addresses the problem sctting where multiple objects are included in an image and allows for the evaluation of detection performance for both structural and logical anomalies. Annotations (ground truth) of anomalous regions at the pixel level are provided for both logical and structural anomalies. The details of the dataset statistics presented in Table We the training set for model training, the validation set for optimizing hyperparameters such as the number of epochs, and the test set for evaluation. We also evaluate detection performance on MVTecAD Bergmann et. al. (2019), which is a popular benchmark for structural anomalies. MVTecAD contains over 5000 images divided into 15 categorics, including objects and textures.
We employ image-level AUROC [%] as an evaluation metric to fairly compare detection performance with other methods. AUROC calculated by plotting the false positive rate the horizontal axis and the true positive rate the vertical axis, dynamically adjusting the anomaly threshold to draw the curve. AUROC reaches its maximum when the true positive rate is and the false positive rate is 0 at certain threshold r", and it becomes 0.5 when using a random predictor.
Table 1: Dataset statistics of MVTecLOCO across different categories. The number of test images is grouped tormal/logical/ttruttuaal
# Images
Box
Bag
Pins
Cable
Bottle
Total
Training
351
360
372
354
335
1772
Validation
62
60
69
59
54
304
Test
102/83/90
122/137/82
138/91/81 /9/11 119/108/85
94/142/94 375/561/333
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Table 2: Quantitative results on the MCTecLOCO. We report image-level AUROC[%] in the single-class setting. The best results among conventional and MIM-based methods are highlighted bold. Notably, our approach significantly improves AD accuracy on MIM-based approaches and surpasses conventional AD methods average detection accuracy, narrowing the gap between current SoTA methods (EAD, PUAD).
Conventional
MIM
SoTA
Category
PC
ComAD
GCAD
SSM
SMAE
MDY
Ours
EAD
PUAD
Logical
Box
74.8
94.7
87.0
32.9
74.3
74.0
86.2 (10.760)
76.1
92.4
Bottle
93.9
90.9
100.0
59.8
96.8
57.8
99.0 (#00.00)
96.3
99.1
Pins
63.6
89.0
97.5
64.2
73.9
43.7
73.5 (10.695)
98.8
91.7
Bag
57.8
79.7
56.0
60.2
58.0
47.2
58.1 (+3.783)
58.3
77.8
Cable
84.4
91.9
89.7
50.3
76.6
65.0
88.0 (+0.836)
94.2
92.3
Average
74.0
87.7
86.0
53.5
75.9
57.6
80.9 (#:06922
84.7
90.7
Structural
Box
80.1
70.0
79.2
32.6
71.7
54.7
81.0 (+0.755)
76.1
75.3
Bottle
98.5
80.5
99.9
59.8
89.9
53.4
98.8 (4002866)
96.3
99.2
Pins
87.9
93.8
95.1
51.5
70.9
85.1
85.9 (+12)
98.8
98.5
Bag
92.0
65.0
87.1
56.2
63.0
86.7 (=0.817)
58.3
92.0
Cable
88.0
63.8
98.3
53.0
64.5
59.2
90.0 (#1.132)
94.2
93.9
Average
89.3
74.6
91.9
50.6
72.0
60.3
88.5 (10.516)
84.7
91.8
Overall
81.7
81.2
83.4
52.1
74.0
59.0
84.7 (+0.561)
88.3
91.2
4.2
Implementation Details
The resolution of the input images is uniformly set to 224 224 [px]. The backbone network used in HVQ- Trans and ViT is the Patch Description Network (PDN) Batzner et al. (2023). Average pooling with a kernel size of 2 is applied to the features obtained from PDN, resulting in feature map hg 24884x2x224 The number of layers L in HVQ Trans is set to 4, the codebook size is 512, and the codebook dimension is 64. AdamW Loshchilov & Hutter (2017) is used as the optimizer, with a weight decay of 1c-4. HVQ- Trans was trained for 1000 epochs. Training took approximately 6 hours using a CPU (Intel Core i9-14900KF)), 32 GB of memory, and a GPU (NVIDIA GeForce RTX 4090 24 GB).
The number of layers L' in the ViT is 2021 1s Peng al. (2022). AdamW Loshchilov & Hutter (2017) is used the optimizer, with weight decay of 1 10-6. The VIT was trained for 500 epochs, taking about 2 hours per class.
4, and a block-wise masking strategy is adopted Bao et al.
4.3
Main Experimental Results
In Table ? we report the image-level AUROC on MVTecLOCO for our proposed method and other anomaly detection methods in the one-class setting. We show the average and standard deviation of the perfor- mance of models trained with five different random seeds. Table shows the effectiveness of the proposed method compared to other anomaly detection approaches, as well as the performance improvements over existing MIM-based methods. The proposed method demonstrates performance surpassing memorybank based methods (PatchCore Roth et al. (2021). ComAD Liu et al. (2023b)) and conventional MIM-based methods (SSM [Huang et al. (2023a), SMAE|Ya et al. (2022), MAEDAY [Shwwartz et al. (2024) While the proposed method surpasses GCAD Bergmann al. (2022). there exists 3.6 % performance gap between our method and of the current. SoTA methods, EMiciietADD Balzner al. (202)), in terms of overall AUROC, However, the main limitation of EfficientAD is that it relies on the information bottleneck in the autocncoder to detect logical anomalics. Since the information of anomalous regions decays through the information bottleneck, logical anomalies can be delected by calculating the reconstruction error. However, an information bottleneck that is too tight (i.e, the features have smaller dimensionality) leads to significant

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errors in normal regions. One must determine the proper dimensionality of features through time-consuming
hyperparameter tuning. Similarly, this also applies to PUAD Sugawara & Imamuura (2024), which is an
extension of EfficientAD.
In Table 3 we also report the detection performance compared with current So logical anomaly detection
approaches Hsieh & Lai (2024); Kim et al.] (2024): Sugawara & Imannura (2024); Batzner et al. (2023): Zhang et al. (2025); ; Fučka et al. (2025). For a single-class setting, SoTA methods exhibit significantly better performance than our approach. However, they rely on supervision for fine-tuming the segmentation model hand-crafted prompt engineering. Therefore, we primarily focus comparisons with PUAD Sugawara & Imamura (2024) and EfficientAD Batzner et al. (2023). In these comparisons, our proposed framework achieves comparable performance to PUAD in the multi-class setting with only a 1.2% difference in image- level AUROC. Since changing the problem from single-class to multi-clase makes the task more difficult and requires the model to be more expressive, methods that rely on information bottlenecks tend to experience a drop in detection performance. The performance of EfficientAD and PUAD significantly drops by =5%.
Because proposed method does not rely on an information bottleneck, it can be scaled to various model sizes. This suggests that the MIM-based approach has the potential to serve as foundational model for general-purpose anomaly detection. LADMIM and EfficientAD have different feature dimensions. For fair comparison, in Fig. 3 we also show how detection performance changes as the feature dimension of the model increases in a multi-class setting. As the feature dimension increases, our proposed method shows improved detection performance for logical anomalies, while EfficientAD does not exhibit the same trend.
Table 4 presents the detection performance on the MV dataset. Our proposed method achieves the best performance compared to conventional MIM-based approaches. While there is performance gap between our proposed LADMIM and current SoTA methods, it is important to note that our objective is to detect both logical and structural anomalies, rather than focusing solely on structural anomalies.
The proposed method uses HVQ-Trans to detect structural anomalics and ViT to detect logical anoma- lies. However, it remains unclear which specific types of anomalies each model best suited to detect. Additionally, in the proposed method, the prediction target of MIM is set as the probability distribution of discrete latent variables, but it is also possible to set simpler pixcl-level image features the prediction target. Furthermore, the types of anomalies that can be detected are greatly influenced by the visual features represented by the discrete latent variables. Therefore, in this study, we conducted experiments to answer the following questions:
Is it necessary to use both HVQ-Trans and VIT?
How does the prediction target of MIM affect detection performance?
What features do the discrete latent variables represent?
4.3.1 Design Ablations
The original HVQ-Trans paper Lu et al. (2023) does not evaluate its performance in detecting logical anoma- lies using datasets like MVTecLOCO Bergmann et al. (002).. Therefore, the structural and logical AD performance when using HVQ- Trans and ViT independently shown Table 3
As result of this evaluation, it was found that HVQ-Trans achieves high detection performance for struc- tural anomalies, while VIT achieves high detection performance for logical anomalies. Using both models simultaneously improves overall detection performance.
The structural AD performance of VIT is low, with an AUROC of 0.68, because the discrete latent variables do not retain the characteristic information of local normal features and instcad capture more abstract characteristics of objects. On the other hand, ViT achieves higher logical AD performance than HVQ-Trans
Moreover, when HVQ-Trans and ViT are used together, their detection performance exceeds the logical AD performance of either model used individually. This indicates that EVQ-Trans and ViT complement cach other in detecting different types of logical anomalies.
The multi-class setting refers problem sets where one model learns to handle all categories once.
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Table 3: Comparison between current SoTA AD methods and our proposed framework on the MVTecLOCO. We report image-level AUROC[%] in both single- and multi-class scttings. "Seg. Prior" denotes whether the method uses pre-trained segmentation models.
Single-class
Multi-class
Category
CSAD
PSAD
SALAD
LogSAD
Ours
PUAD
EAD
Ours
Logical
Box
94.4
100
99.6
N/A
86.2
(#0.760)
94.2
77.9
86.5 (+0.585)
Bottle
94.9
99.1
99.6
N/A
99.0 (+0.200)
96.6
90.8
98.7 (+0.230)
Pins
99.5
100
99.9
N/A
73.5 (#0.695)
76.6
70.8
72.0 (#2.018)
Bag
99.9
99.3
98.6
N/A
58.1 (+3.783)
70.8
59.0
62.4 (+1.420)
Cable
94.8
91.9
95.8
N/A
88.0 (+0.836)
87.8
83.5
87.3 (+0.979)
Average
96.7
98.1
98.7
89.3
80.9
(#0.692)
85.2
76.4
81.4 (+0.553)
Structural
Box
91.1
84.9
88.8
N/A
81.0 (#00.55)
72.5
77.3
81.5 (#1.313)
Bottle
95.6
98.2
98.9
N/A
98.8 (#0.286)
95.5
96.7
98.2 (#0.141)
Pins
97.8
89.8
98.3
N/A
85.9 (+1.521)
97.7
88.0
86.9 (+1.102)
Bag
93.2
95.7
94.7
N/A
86.7 (+0.817)
88.8
84.3
88.1 (#0.753)
Cable
92.2
89.3
98.6
N/A
90.0 (+1.132)
82.5
94.0
89.6 (+1.29
Average
94.0
91.6
95.8
93.1
88.5 (#0.516)
87.4
88.1
88.9 (+0.267)
Overall
95.3
94.9
97.3
91.2
84.7 (+0.561)
86.3
82.2
85.1
(#0.162)
Seg.
Prior
X
X
Table 4: Performance MVTecAD. We report average detection AUROC [% of LADMIM and other
90
methods on MVTecAD
Bergmann et al. 2019
EfficientAD Logical AUROC
AUROC [%]]
EfficientAD Structural AUROC
LADMIM Logical AUROC
Method
AUROC [%]
84
LADMIM Structural AUROC
Comparison with MIM Methods
82
SSM Huang et al. (202aa)
92.0
80
SCADN
Yan et al. 202T
81.8
LADMIM (Ours)
93.4
100
200
300
500
Comparison with SoTA Methods
Dimensions
GLASS Chen et al. (0024))
99.9
Figure Scalability analysis. We plot the change in image-level AUROC [%] with model dimensions in the multi-class setting.
HypAD Flaborea et al. (2033
99.2
CoMet Aqeel et al. 2225
99.7
LADMIM (Ours)
93.4
4.3.2 The Effect of Combining AVQ-Trans with Prior MIM-Based AD
Our proposed framework heavily relies HVQ-Trans to detect structural anomalies. However, it remains unclear whether other MIM-based approaches improve detection performance when combined with HVQ- we show the average detection performance of the MIM- methods when
Trans predictions. In Table 6 combined with HVQ-Trans. The results indicate that combining HVQ-Trans with MIM-based methods can enhance detection performance for both logical and structural anomalies. Although SMAE Yao et al. (2022) combined with HVQQ-rran achieves better detection performance for logical anomalies than our method, LADMIM exhibits higher overall detection performance on average.
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Table 6: Detection performance when combin- ing HVQ-Trans with MIM-based AD.
Table 5: Design ablation of the anomaly scor- ing MVTecLOCO. We report average imago-level AUROC[%] across all categories.
Method
SA
LA
Avg.
SHVQ
SMIM
SA
LA
Avg.
SMAE
72.0
75.9
73.9
SMAE [w/ HVQ)
80.0
85.4
82.6
91.2
76.7
83.3
MAEDAY
60.3
57.6
58.9
V
68.7
79.3
73.6
MAEDAY (W/ HVQ)
85.4
71.9
78.1
90.3
83.1
86.6
LADMIM
90.3
83.1
86.6
Table 8: The effect of masking strategies. We report image-level AUROC [%].
Table The effect of codebook selection. We report image-level AUROC [%] on the Box.
Masking
SA
LA
Avg.
Source codebooks
SA
LA
Avg.
Checkerboard
88.1
78.2
83.2
[1]
80.1
78.4
79.3
Random-0.2
85.1
75.1
80.1
[2]
80.7
78.2
79.5
Random-0.4
85.3
76.3
80.8
[3]
79.0
77.9
78.5
Random-0.6
87.2
78.9
83.1
[4]
79.5
78.1
78.8
BlockRandom-0.22
87.5
80.9
84.2
[1,2]
82.2
79.3
80.8
BlockRandom-0.4
88.2
78.1
83.2
[1,2,3]
82.3
79.0
80.7
BlockRandom-00.6
88.1
78.6
83.4
[1,2,3,4]
86.2
81.0
83.6
Adaptive-0.4
91.0
77.2
84.1
Also, in Fig. 4 plot histograms of the predicted anomaly scores produced by HVQ-Tram Trans and VIT. Since each score has different scale and distribution, apply normalization before summing them. In Table 7 we evaluate detection performance with different aggregation strategies, including simple addition, min-max, and Z-score normalization. As the histograms Fig. 4 indicate, each score distribution contains some outlier points, which are potentially anomalous images. We apply Z-sccore aggregation, motivated by the presence of these outliers, as Z-scores are more robust outliers. Furthermore, for the Box category, SMIM attains the highest AUROC for logical anomaly detection. This suggests that detection accuracy could be further improved by dynamically adjusting the aggregation weights.
The effect of score aggregation. We report average detection AUROC [%] the Box for different aggregation strategies.
Table 7:
MIM
200
HVQ
Count
150
Strategies
T.A
SA
Avg.
100
SMIM
92.0
63.6
77.8
ShVQ
79.6
79.5
79.6
50
A Agregation
0
15 Anomaly Score
10
addition
82.9
81.1
82.0
min-max
85.9
82.5
84.2
Z-score
87.4
82.6
85.0
Figure 4: Score Histogram. We plot his tograms of SHvQ and SMIM before aggregation.
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Table 10: Impact of loss design. We report image-level AUROC [%] on the Box.
Table 11: Impact of changing the MIM predic- tion target. We report image-level AUROC [%
Metric
SA
LA
Avg.
Prediction Target
SA
LA
Avg.
KT. divergence
85.1
80.5
82.8
Pixel
91.1
74.8
82.2
JS divergence
84.1
80.4
82.3
Feature
88.9
83.4
86.1
Hellinger distance
79.4
78.2
78.8
Code
90.7
78.0
83.9
L1 distance
86.2
81.0
83.6
Code Histogram
90.3
83.1
86.6
4.3.3 The Effect of Masking Strategies
In the context of MIM, the choice of masking strategics affects the propertics of the learned representations
Bao et. al. [2021] He et al. (2021) Assran et al. (2023). Moreover, in unsupervised AD, previous work has
attempted to identify the optimal masking strategies Li et al. 2020 Huang et al. (2023a); Yan et al. (2021)
In Table 8 we show the image-level AUROC [%] of LADMIM on MVTecLOCO with different masking
strategies. For checkerboard masking, we randomly select one of the checkerboard masks with different grid sizes 3, 6, 12. For random masking, denote Random-r random masking where portion of patches are masked. For block random masking, we denote BlockRandom-r portion of patches are masked. We randomly choose the aspect ratio of each block from [0.3, 3] and its position, ensuring that the sum of the gathered masks covers the r portion of the patches. Finally, we also investigate an adaptive masking strategy, where masked patches are determined by the top-r portion of patches with the largest prediction errors. We denote Adaptive-r as this masking strategy and set r 0.4, which achieved the best performance in our hyperparameter search. For all masking strategies, we train LAVIT for 200 epochs in the multi-class setting and report the average AUROC across all categories.
block-wise random masking where
We observe that mask coverage is important for random masking: a higher masking ratio leads to better performance. Conversely, for block random masking, a large masking coverage results in significant uncer- tainty in the masked regions, causing dogradation in detection performance. While checkerboard masking works well as a random masking strategy, block random masking with 20% masking shows the best detection performance in this setting. Since adaptive masking focuses the model on small potential anomalous regions, it outperforms other strategies in detecting structural anomalies. However, detection performance for logical anomalies is limited due to the overly focused mask region.
4.3.4 Selection of Prediction Targets
In Table T. we report image-level detection performance on the Box category for different subsets of code- books from HVQ-Trans as targets. While performance limited when predicting only one of these code- books, combining them yields substantial performance improvements. This is because predicting multiple histograms from different codebooks reduces the variance of the prediction targets and captures different feature gramularities.
In Table 10 we also investigate the impact of MTM loss design. Notably, the simple L1 distance achieves the best detection performance, while KL or JS divergence is one of the most popular options for calculating the discrepancy between different probabilistic distributions. However, in our setting, target histograms can be sparse probabilistic vectors due to the low codebook usage. This motivates us to use the L1 distance as a loss function to improve training stability.
From the perspective of representation learning using MIM, the selection of prediction targets in masked regions is crucial Bao et al. (2021); b; Peng et al. (2022): ; Chen et al. (2024b); Assran et al. (002).. This also applics to AD, where the choice of prediction targets such as predicting the image itself or features within the masked region significantly affects detection performance. Table I1 shows the detection performance of ViT with different prediction targets. In this evaluation, we assessed performance when predicting the image,
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features (i.e., outputs of the backbone network), discrete representations, and the probability distribution of discrete representations in the masked region.
The results showed that the average detection performance was highest when using the probability distribu- tion of discrete representations. When the image itself was the prediction target, detection performance for structural anomalies was the highest, but performance for logical anomalies was low. This because accu- rate reconstruction of local image features is necessary for detecting structural anomalies, while capturing higher-level feature relationships is more important for detecting logical anomalies. Detection performance for logical anomalies improved when using discrete representations features. However, the performance was still inferior to the proposed method due to the positional uncertainty of objects.
4.3.5 Analysis on Internal Representations of HVQ- TranT
Figure shows visualization of the discrete latent variable predictions made by HVQ-Trans. Here, normal sample of Juice Bottle from MVTecLOCO latent variables for each layer, shown in the upper part of Fig, 5
example. HVQ-Traas predicts a map of discrete
By observing the prediction results, we can see that different discrete latent variables are predicted to some extent based on the semantics of the object. Additionally, the lower part of Fig. 5 shows image patches corresponding to specific discrete latent variables. For example, the 4th discrete latent variable the codebook corresponds to the contents of the bottle, while the 2nd discrete latent variable corresponds to the bottle's label.
On the other hand, the predictions made by HVQ-Trans are not perfect. As shown in the lower part of Fig. []] the fourth discrete latent variable in the codebook represents not only the contents of the bottle but also the bottle itself and the background. It predicts the same discrete latent variable for labels of different products, such as cherries and oranges. This issue, where the same discrete latent variable is predicted for different objects, referred as Code Collision Liu et al. (2023a); Hou et al. (2022); Huang al. (2023b). Additionally, multiple different discrete latent variables can be predicted for the same object, problem
Reference Images
Discrete Latents
HVQ Trans
I 2 = 1 =
Code=4
"Juice Contents"
Code=2
"Label "
Figure Qualitative visualization of discrete latent variables prediction by HVQ-Trans. We visualize
quantized discrete latents in the [-th layer where I E {1,2,3,4 4} in experiments.
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Normal (org)
A Logical anomaly (rec)
Normal (rec)
Logical anomaly (org)
10
Structural anomaly (org)
Structural anomaly (rec)
5
UMAP-2
0
-5
-10
-5
10
UMAP-1
Figure 6: Qualitative visualization of original and reconstructed features by HVQ-Trans Juice Bottle. We apply UMAP Mclnnes al. (2018) to the average pooled feature representations for the dimensional reduction. UMAP-1 and UMAP-2 simply denote the two coordinates of the 2D UMAP embed- ding computed from pooled feature representation using cosine similarity.
known Code Redundancy Huang et al. (2023c); Liu et al. (2023a): Huang et al. (2023)). Such tokenizer prediction issues significantly impact detection performance.
In Fig. G we visualize the distributions the original features and their corresponding reconstructions using UMAP McInnes al.] (2018). Since UMAP reflects local neighborhood structure the original feature space, we can easily analyze the dynamies of HVQ-Trans reconstruction in high-dimensional feature space. For visualization,
project the original features into two-dimensional space using UMAP based on cosine similarity. Each axis (UMAP-1 and UMAP-2) is simply one coordinate of the Iearned 2D UMAP embedding and has no intrinsic physical meaning; the embedding can be arbitrarily rotated or reflected without changing the neighborhool relationships. Tt. should be noted that structural anomalies are not distinguished these visualization experiments because average pooled operation diminishes the difference between normal and anomalous patches. We observe that most logical anomalics are successfully reconstructed into near-normal features and exhibit large reconstruction errors. However, there are many logical anomalies that cannot. be reconstructed into normal one. Therefore, still need to employ additional MIM module for detecting such logical anomalies.
4.4 Failure Case Analysis
One limitation of MIM-based approaches is their sensitivity the masking strategy. Since MIM-basod methods detect anomalous regions by computing prediction errors masked regions, the mask must effectively cover the anomalous areas. However, if the mask is too large, it introduces significant uncertainty within the masked region. Therefore, the masking strategy must strike a better balance between coverage and uncertainty.
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Missing Pins
Excess Pins
Input
r
Image
m
Masked Image
I
Figure 7: Failure case analysis. We provide examples of logical anomalies that the current SoTA method (EfficientAD) correctly detected, but LADMIM failed to detect. In the Pins category, each compartment in the image must. contain exactly one pushpin. Ideally, each mask should cover only a single compartment; however, with random masking, this condition is often violated.
In Fig. provide examples of logical anomalies on Pushpins where LADMIM failed to detect anomalics but EfficientAD succeeded. If the mask is too large relative to these anomalous areas, it fails to detect missing or excessive elements, Additionally, a randomly selected mask may not adequately cover regions where anomalies are likely to occur, leading to smaller prediction errors even in anomalous areas.
Thus, carefully designed masking stralegy could boost performance settings like Pushpins. For example, similar to SSM Huang et al. (2023a), iterative refinement of the mask based on reconstruction error could be considered an inference-time masking strategy.
4.5 Computational Cost Analysis
Table 12: Inference speed measured by frame
In Table 12, we compare inference efficiency with frame per second (FPS) on a single RTX-4090. While Patch- Core and SALAD exhibit lower inference efficiency due to heavy feature-matching putation, our proposed LADMIM achieves second-bes inference speed.
per second (FPS) on single RTX-4090.
Method
FPS
segmentation--modee comn
PatchCore
13
SALAD
15
EfficientAD
260
5 Conclusion
LADMIM (Ours)
78
In this study, we addressed the problem setting of AD in which multiple objects are present in an image, and both logical and structural anomalies can occur. To effectively detect both types anomalies, we combined a reconstruction- based model (EVVQ-Trans) and a ViT trained with an MIM objective; these models are responsible for detecting structural and logical anoma- lies, respectively. Specifically, the ViT trained with MIM addresses the problem of positional uncertainty in masked regions by predicting distributions of discrete latents. We demonstrated substantial accuracy improvements compared to conventional MIM-based methods and comparable performance to the current SoTA methods. Additionally, we clarified the impact of changing the prediction target in the MIM-based model and highlighted issues related to the tokenizer, suggest ing future directions for improving MIM-based models.
Limitations. While predicting code histograms mitigates the uncertainty problem in masked regions, it makes it harder to detect logical anomalies that require modeling order-related relationships, although other types of logical anomalies still be effectively detected. This limitation can be alleviated by introducing stochastic prediction module, such diffusion-based masked prediction. Additionally, with the current large-scale random masking strategy, our method fails to localize the logical anomaly location. For the
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future direction, replacing random masking with small-scale semantic masking would improve logical anomaly localization.
References
Mohammad Adiban, Kalin Stefanov, Sabato Marco Siniscalchi, and Giampiero Salvi. Hierarchical residual learning based vector quantized variational autoencoder for image reconstruction and generation. In Brit. Mach. Vis. Cons., 2022. doi: 10.48550/ arraa 2ww...02...5544...
Muhammad Aqeel, Shakiba Sharifi, Marco Cristani, and Francesco Setti. Towards real unsupervised anomaly detection via confident meta-learning. In Proceedings of the IEEE/CVF International Conference Computer Vision (ICCV), pp. 4858-4867 October 2025.
Mahmoud Assran, Quentin Duval, Ishan Misra, Piotr Rojanowski, Pascal Vincent, Michael G. Rabbat, Yann LeCun, and Nicolas Ballas. Self-supervised learning from images with joint-embedding predictive architecture. IEEE Conf. Comput. Vis. Pattern Recog., pp. 15619-15629, 2023. doi: 10.1109/CVPRRB 2023.01499.
Hangbo Bao, Li Dong, and Furu Wei. Beit: Bert pre-training of image transformers. ArXiv, abs/ 229/216060824, 2021. doi: w0.45550//rXiv.... S...xi22106002...
Jinan Bao, Hanshi Sun, Hanqiu Deng, Yinsheng He, Zhaoxiang Zhang, and Xingyu Li. Bmad: Bench- marks for medical anomaly detection. 2024 IEEE/CVF Conference on Computer Vision and Pattern Recogrition Workshops (CVPRW), pp. 4042 4053, 2024. doi: (L110//CVVRW633222222222222
Kilian Batzner, Lars Heckler, and Rebecca König. Efficientad: Accurate visual anomaly detection at millisecond-level latencies. pp. 127 137, 2023. doi: 10.1 http://WWVV770.222..0000
Yoshua Bengio, Nicholas Léonard, and Aaron C. Courville. Estimating or propagating gradients through stochastic neurons for conditional computation. abs/1308.3432, 2013. doi: 10.48550/ar http://aaiiv..1..333331 1308.3432.
Paul Bergmann,, Michael Fauser, David Sattlegger, and Carsten Steger. Mvtec ad a comprehensive real-world dataset for unsupervised anomaly detection. IEEE Conf. Comput. Vis. Pottern Recog., pp. 9584-9592, 2019. doi: D0.109//VVR.20119000
Paul Bergmann, Kilian Batzner, Michael Fauser, David Sattleggor, and Carsten Steger. Beyond dents and scratches: Logical constraints in unsupervised anomaly detection and localization. Int. J. Comput. Vis., 130:947 969, 2022. doi: 000077/s1163-022--111
Qiyu Chen, Huiyuan Luo, Chengkan Lv, and Zhengtao Zhang. A unified anomaly synthesis strategy with gradient ascent for industrial anomaly detection and localization. In European Conference on Computer Vision, pp. 37 54. Springer, 2024a.
Xiaokang Chen, Mingyu Ding, Xiaodi Wang, Ying Xin, Shentong Mo, Yunhao Wang, Shumin Han, Ping Luo, Gang Zeng, and Jingdong Wang. Context autoencoder for self-supervised representation learning, IJVV, 132:208 223, 2024b. doi: 10.1007/s1 12110/2311111-02331-5------
Niv Cohen and Yedid Hoshen. Sub-image anomaly detection with deep pyramid correspondences. ArXiv, abs/2005.02333 02357, 2020. doi: htlps://doi..rr////
2015550/aXXi.2000
Prafulla Dhariwal, Heewoo Jun, Christine Payne, Jong Wook Kim, Alec Radford, and Tlya Sutskever. Juke- box: A generative model for music. arXie preprint arXiv:2005. 00341, 2020.
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Un- terthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. An image is worth 16x16 words: Transformers for image recognition at scale. Computer Vision and Image Understanding, abs/2010.1199 11929, 2020. doi: 10.48550/ aiv 2010.11929
17

---

Published in Transactions on Machine Learning Research (3/2026)
Alessandro Flaborea, Bardh Prenkaj, Bharti Munjal, Marco Aurelio Sterpa, Dario Aragona, Luca Podo, and Fabio Galasso. Are certain it's anomalous? In Proceedings of the IEEE/CVE Conference Compuler Vision and Pattern Recognition (CVPR) Workshops, pp. 2897-2907, June 2023.
Matic Fučka, Vitjan Zavrtanik, and Danijel Skočaj. SALAD Semantics-Awan Logical Anomaly Detection. In Proceedings of the IEEE/CVE International Conference on Computer Vision (ICCV), October 2025.
Hewei Guo, Liping Ren, Jingjing Fu, Yuwang Wang, Zhizheng Zhang, Cuiling Lan, Haoqian Wang, and Xinwen Hou. Pemplate-guides hicrarchical feature restoration for anomaly detection. Int. Conf. Comput.
Vis., pp. 6424 6435, 2023. doi: 10. 10.1109/ICC/V htt////CVV1100.22..0000555
Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Doll'a ar, and Ross B. Girshick. Masked autoencoders are scalable vision learners. IEEE Conf. Comput. Vis. Pattern Recog,, pp. 15979 15988, 2021. doi:
10.1109/CVPR52688.22 202.00055..22.0155..
Yupeng Hou, Zhankui He, Julian McAuley, and Wayne Xin Zhao. Learning quctor- item represen- tation for transferable sequential recommenders. 2022. doi: 10.1145/5443507.3533444
Yu-Hsuan Hsich and Shang-Hong Lai. Csad: Unsupervised component segmentation for logical anomaly detection, 2024. URL https://arxiv: htrg/abs/24400 15628
Chaoqin Huang, Qinwei Xu, Yanfeng Wang, Yu Wang, and Ya Zhang. Self-supervised masking for unsu- pervised anomaly detection and localization. IEFE Trans. Multimedia, 2023a. doi: 10.1109/TMM.2022 3175611.
Mengqi Huang, Zhendong Mao, Zhuowei Chen, and Yongdong Zhang. Towards accurate image coding: Improved autoregressive image generation with dynamic vector quantization. IEEE Conf. Comput. Vis. Pattern Recog., pp. 22596-22605, 2023b. doi: 111109/CVVP52999.22 2tt.:/....22201114.
Mengqi Huang, Zhendong Mao, Quang Wang, and Yongdong Zhang. Not all image regions matter: Masked vector quantization for antoregressive image generation. IEEE Conf. Comput. Vis. Pattern Recog., pp. 2002-2011, 2023c. doi: 10.1109 http///V2222222..000022
Soopil Kim, Sion An, Philip Chikontwe, Myeongkyun Kang, Ehsan Adeli, Kilian M. Pohl, and Sanghyun Park. Few shot part segmentation reveals compositional logic for industrial anomaly detection. AAAI, 38
(8):8591-8599, 2024. doi: 10.1609/ v38i8. 28703.
Doyup Lee, Chihcon Kim, Sachoon Kim, Minsu Cho, and Wook-Shin Han. Autoregressive image generation using residual quantization. In 2022 IEEE/CVF Conference on Compuler Vision and Pollern Recognation (CVPR), pp. 11513 -1522,, 2022. doi: 10.1109/CVPPP (CVVR52688.2022.000
Zhenyu Li, Ning Li, Kaitao Jiang, Zhiheng Ma, Xing Wei, Xiaopeng Hong, and Yihong Gong. Superpixel masking and inpainting for self-supervised anomaly detection. In Brit. Mach. Vis. Conf., 2020. URL
https://wwe bww.bmb022220oofftecm www.eoooeereen....m erence. httaaaae....aaaeeeeaappprr//////
Dianbo Liu, Alex Lamb, Xu Ji, Pascal Junior Tikeng Notsawo, Michael Curtis Mozer, Yoshua Bengio, and Kenji Kawaguchi. Adaptive discrete communication bottlenecks with dynamic vector quantization. In AAAI, pp. 8825-8888 (1538883,448 2023a. doi:
26061.
Jiaqi Liu, Guoyang Xie, Jinbao Wang, Shangnian Li, Chengjie Wang, Feng Zheng, and Yaochu Jin. Deep industrial image anomaly detection: A survey. Machine Intelligence Research, 2111):00-1335, 2024.
Tongkun Liu, Bing Li, Xiao Du, Bingke Jiang, Xiao Jin, Liuyi Jin, and Zhuo Zhao. Component-awean anomaly detection framework for adjustable and logical industrial visual inspection. Advanced Engineering Informatics, 58:102161, 2023b. ISSN 1474-0846. doi: 10.1016/j.aci.20222 000.............
llya Loshchilov and Frank Hutter. Decoupled weight decay regularization. In Int. Conf. Learn. Represent., 2017. doi: 00.48550/arXiv.17/0 1711.05111....1..
18

---

Published in Transactions on Machine Learning Research (3/2026)
Ruiying Lu, YuJiie Wu, Long Tian, Dongsheng Wang, Bo Chen, Xiyang Liu, and Ruimin Hu. Hierarchical vector quantized transformer for multi-class unsupervised anomaly detection. In Adv. Newral Inform. Process. Syst. pp. 8487 8500, December 2023. doi:
Leland McInnes, John Healy, Nathaniel Saul, and Lukas Großberger. Umap: Uniform manifold approxima- tion and projection. Journal of Open Source Software, 3(29):861, 2018. doi: 2221005/jss.0086.. URL
htttps://doi... org/ 21105/joss 0000000000000000 00861
Zhiliang Peng, Li Dong, Hangbo Bao, Qixiang Ye, and Furu Wei. Beit v2: Masked image modeling with vector-quantized visual tokenizers. ArXiu, abs/2208..66666 06366, 2022. doi: 10448550/arXX122220...33
Ali Razavi, Aäron van den Oord, and Oriol Vinyals. Generaling diverse high-fidelily images with VQ- VAB-2... Curran Associates Inc., Red Hook, NY, USA, 2019.
Karsten Roth, Latha Pemula, Joaquin Zepeda, Bernhard Scholkopf, Thomas Brox, and Peter Gehler. To- wards total recall in industrial anomaly detection. IEEE Conf. Comput. Vis. Pattern Recog, pp. 14298 14308, 2021. doi: 10.1109 10.1109/CVPR 0PP.PR5668..0222.0111
Eli Schwartz, Assaf Arbelle, Loonid Karlinsky, Sivan Harary, Florian Seheidegger, Sivan Dovch, and Raja Giryes. Maeday: Mae for few- and zero-shot anomaly-detection. Compuler Vision and Image Undersland- ing, 241:103958, 2024. ISSN 1077-3142. doi: 1.1006/...vin.2224.10.
Shota Sugawara and Ryuji Imamura. Puad: Frustratingly simple method for robust anomaly detection. In 2024 IEEE International Conference on Image Processing (TCIP), pp. 842-848, 848, 2024. doi: 10.1109/ ICIP51287.2024.1
Mingxing Tan and Quoe Le. Efficientnet: Rethinking model scaling for convolutional neural networks. pp. 1105-6114, 2019. doi: 10.48550/arXii.1005.111
Aäron van den Oord, Oriol Vinyals, and Koray Kavukcuoglu. Neural discrete representation learning, In NIPS'17: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 6309-6311 December 2017. doi:
Ashish Vaswani, Noam M. Shazeer, Niki Parmar, Jakob Usækoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. In Adv. Newral Inform. Process. Syst., 2017. doi: 10.48550/ 11448550//rXi.11111 10X...1706.0372..
Xudong Yan, Huaidong Zhang, Xuemiao Xu, Xiaowei Hu, and Pheng-Ann Hong. Learning semantic context from normal samples for unsupervised anomaly delection. In AAAI, 2021. doi: 10. 10.1609/aaae v3ai.v3554..14220
Minghui Yang, Jing Liu, Zhiwei Yang, and Zhaoyang Wu. Slsg: Industrial image anomaly dotection by learning better feature embeddings and one-class classification. ArXiu, 2023. doi: 10.1016/j.palcoo.222 2024. 110862.
Haiming Yao, Xue Wang, and Wenyong Yu. Siamese transition masked autoencoders as uniform unsupervised visual anomaly detector. ArXiv, 2022. doi: 10.48550 1.44550/arXiv.... arv. 2211 220.0000......99....
Haiming Yao, Wenyong Yu, Wei Luo, Zhenfeng Qiang, Donghao Luo, and Xiaotian Zhang. Learning global- local correspondence with semantic bottleneck for logical anomaly detection. IEEE Transactions on Cir- cuits and Systems for Video Technology, 34:3589-3600 2023. doi: 2TD.0//TTSTTT..22.333333
Haiming Yao, Wei Luo, Wenyong Yu, Xiaotian Zhang, Zhenfeng Qiang, Donghao Luo, and Hui Shi. Dual- attention transformer and discriminative flow for industrial visual anomaly detection. IEEE Transactions on Automation Science and Engineering, 21(4):6126 6140, 2024. doi: 10.1109/ 11111099TTEE222221 202...2222333215555
Jiawei Yu, Ye Zheng, Xiang Wang, Wei Li, Yushuang Wu, Rui Zhao, and Liwei Wu. Fastflow: Unsupervised anomaly detection and localization via 2d normalizing flows. abs/2111.07677,, 2021. doi: 10.48550/ar /ir 2111.07677.
19

---

Published in Transactions on Machine Learning Research (3/2026)
Jie Zhang, Masanori Suganuma, and Takayuki Okatani. Contextual affinity distillation for image anomaly detection. pp. 148-157, 2023. doi: 10.1109/WA WACC57701.2024.000
Jinjin Zhang, Guodong Wang, Yizhou Jin, and Di Huang. Towards training-free anomaly detection with vision and language foundation models. In IEEE/CVF Conference Computer Vision and Pattern Recognition (CVPR), 2025.
Xin Zhang, Dong Zhang, Shimin Li, Yaqian Zhou, and Xipeng Qiu. Speechtokenizer: Unified speech tokenizer for speech language models. In The Twelfth International Conference on Learning Representations, 2024.
Yang Zou, Jongheon Jeong, Latha Pemula, Dongqing Zhang, and Onkar Dabeer. Spot-the-diference self- supervised pre-training for anomaly detection and segmentation. In Eur. Conf. Compul. Vis., 2022. doi: 10.1007/978-3-00 0-188----3--- 081-20056644 23.
20


Reviews:
[{"review_id": "hK5eFSLsbm", "reviewer": "Reviewer_i9UQ", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "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": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This work studies the problem of unsupervised logical anomaly detection in industrial inspection, where anomalies arise from incorrect global relationships among multiple objects rather than local structural defects. The authors propose LADMIM, a hybrid framework that combines a reconstruction-based model (HVQ-Trans) for structural anomaly detection with a masked image modeling (MIM) approach based on Vision Transformers for logical anomaly detection. The key idea is to predict the distribution of discrete latent variables in masked regions, rather than pixel-level or feature-level reconstructions, in order to mitigate positional uncertainty and emphasize global compositional dependencies. The method is evaluated on MVTec LOCO and MVTec AD, with extensive ablation studies.", "claims_and_evidence_yn": "Yes", "claims_explanation": "Strengths:\n* The paper convincingly motivates why logical anomaly detection is fundamentally different from structural AD and why existing methods (CNN features, memory banks, pixel reconstruction) are ill-suited for capturing global dependencies. The discussion is well grounded in recent benchmarks (MVTec LOCO) and prior literature.\n* The division of labor between HVQ-Trans (structural anomalies) and ViT-MIM (logical anomalies) is principled and empirically justified.\n* Extensive experimental results are provided.", "audience_yn": "Yes", "audience_explanation": "The paper intersects several active research directions of interest to the TMLR community:\n* Unsupervised and self-supervised anomaly detection\n* Discrete latent representations and vector quantization\n* Structural properties and compositionality in vision models", "requested_changes": "Major Comments:\n\n* The proposed framework heavily builds upon existing components, in particular HVQ-Trans and standard MIM-based ViT architectures. While the idea of predicting histograms of discrete latent variables is interesting, the reviewer wonders whether this constitutes a fundamentally new algorithmic insight, or whether it is primarily a re-combination of existing techniques with a new interpretation tailored to logical anomaly detection.\n\n* The core design choice—using histogram prediction in discrete latent space—removes positional and ordering information by construction. While this alleviates uncertainty in masked regions, it also fundamentally limits the types of logical anomalies that can be detected. This trade-off is only discussed later in the paper and should be more clearly acknowledged as an inherent limitation of the method.\n\n* The MIM loss is defined as an L1 distance between predicted and target histograms. This choice is intuitive but somewhat ad hoc. The reviewer wonders if there is a specific technical reason to choose this metric rather than other distribution discrepancy criteria.\n\n* The MIM loss sums histogram errors across all HVQ-Trans layers. It is unclear whether all layers contribute equally to logical anomaly detection. An ablation or discussion on layer weighting (or selecting a subset of layers) would improve understanding.\n\n* The experimental evaluation, although extensive, relies exclusively on image-level AUROC. Given that the method’s performance is strongly tied to masking coverage, it is unclear how well the approach localizes anomalies or how sensitive it is to different anomaly sizes and spatial configurations. Additional evaluation beyond image-level AUROC would strengthen the empirical claims.\n\n* The final anomaly score is obtained via a heuristic combination of standardized reconstruction and MIM-based scores. While empirically effective, this fusion lacks theoretical justification, and it is unclear how sensitive the results are to this specific choice. Also, a learned or adaptive fusion strategy could potentially improve robustness and deserves discussion.\n\nMinor Comments:\n\n* The relationship between $p^{\\prime}$ and the probability distributions of different layers of latent variables is not clearly discussed.", "broader_impact_concerns": "No explicit concerns.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "9vKnMm1lds", "reviewer": "Reviewer_uPQ7", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "No", "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": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "**Summary**\n- This research aims to solve an anomaly detection task that is widely used in industry in an unsupervised manner. In particular, it focuses on the logical aspect among logical and structural anomalies and proposes Logical Anomaly Detection with Masked Image Modeling (LADMIM). By leveraging both Masked Image Modeling, which has recently made a significant impact in the ML community, and the Vector Quantization technique to perform logical anomaly detection, it shows superior performance compared to existing methods on two benchmark datasets.\n\n**Strenghts**\n- In Figure 4, the intermediate results of the network that readers may be curious about are effectively presented, and in particular, it effectively shows what kind of visual characteristics each code represents. The feature maps of the discrete latents are also presented to help understanding by showing which parts each layer focuses on.\n\n- By introducing discrete representation learning, it demonstrates the possibility of extending the method toward a foundational model. Through this, it is expected to be able to make predictions in more diverse and complex anomaly detection environments, and additional experiments on this seem necessary.\n\n- Including Figure 2, the methodology in Section 3 is kindly explained by dividing it into steps. The explanation is so detailed that there were no parts that were difficult to understand.\n\n\n**Weaknesses**\n- The logical justification for applying MIM and Vector Quantization is insufficient. While the characteristics of MIM and Vector Quantization can be understood through the related works, the paper does not include empirical results or theoretical reasons explaining why previous studies did not apply them, or why they are difficult to apply directly to this task.\n\n- Overall, the experiments are insufficient. First, the comparison baselines are limited, and even those are not the latest studies but outdated works (2021, 2023, 2024). Many studies were published in 2025, and comparative experiments with them are necessary. In addition, the datasets are limited to only two: MVTecLOCO and MVTecAD. Comparisons in more diverse environments, including both baseline models and LADMIM, would better demonstrate the effectiveness of this work. Finally, if the results of LADMIM and the comparison models are visualized and shown differently for each case, it would make it easier for readers to understand.", "claims_and_evidence_yn": "No", "claims_explanation": "As described in the Weaknesses, the direction of this work is novel, but the process of validating that direction is insufficient. There are no experiments showing what advantages of MIM and VQ led the authors to choose them when constructing the architecture, and how, through them, the method becomes able to capture what existing approaches fail to predict. In addition, there are many variations of Vector Quantization, but the paper does not make it clear why the Hierarchical Vector Quantized Transformer was selected among numerous options. To justify this choice, the work should either provide comparisons or ablation studies with other Vector Quantization methods, or theoretically explain how the hierarchical property helps anomaly detection in the feature space; however, this cannot be confirmed in the paper. Furthermore, there are no concrete experiments showing what the MIM and VQ methodologies have learned in the feature space. As the authors claim, the paper needs to empirically demonstrate what the proposed method learns in the feature space in order for the authors’ logic to be convincing.", "audience_yn": "Yes", "audience_explanation": "The study “Logical Anomaly Detection with Masked Image Modeling” seems likely to provide interest to TMLR’s audience. Anomaly detection is a very traditional field and is a representative task for applying deep learning in industry. In particular, distinguishing between logical and structural anomalies and detecting them in that way makes it easier for users to interpret the results. In addition, the combination of the widely used MIM architecture and the Vector Quantization technique enables more robust predictions. Although the working mechanism of the method is not specifically visualized, for example through intermediate result examples, the quantitative results of the methodology are meaningful. It seems that experiments could be conducted on more diverse benchmark datasets and environments, and this work is expected to influence attempts to combine these techniques not only in anomaly detection but also in other areas.", "requested_changes": "- Please explicitly mention in the text the meaning of \"l\" in Fig. 4. Please add it to the main body or explain it in the caption.\n\n- A more detailed explanation of Fig. 5 is needed. Although there is an explanation in Section 4.4, assuming the readers are people who do not know this dataset and task well, it is necessary to describe the characteristics of the dataset and explain in a detailed and friendly way what each mask and result specifically mean.\n\n- Adding experimental results by separating Unsupervised Anomaly Detection and Supervised Anomaly Detection could increase the credibility of this work. In particular, the comparative experiments are insufficient. SCADN in Table 4 is a study from as long as four years ago, and this is an outdated study in the rapidly changing deep learning community. Accordingly, I additionally request quantitative and qualitative comparisons with the latest SOTA papers such as [1], [2], [3], [4], [5]. Concrete comparisons with the above methods would be able to emphasize the technical strengths of LADMIM.\n\n- It seems necessary to specifically mention what the ID shortcut problem is. Rather than simply citing prior work, it is necessary to explain it concretely for readers as a problem that this work aims to solve.\n\n- I understand the intention of performing Vector Quantization. However, is there a reason for choosing the Hierarchical Vector Quantized Transformer among numerous Vector Quantization methods? Please explain what characteristics the hierarchical approach has compared to other techniques among Vector Quantization methods. Also, please describe how such advantages can be used in Logical Anomaly Detection.\n\n- It would be good to add a logical explanation of how the simple image reconstruction error, Mean Square Error, is used as an anomaly score.\n\n- What is the basis for claiming that the comparison methods in Table 3 have “implicit leakage of logical anomaly information”? Did that work reveal such aspects, or was it written directly by the authors of this paper?\n\n- Although the work showed through design ablation that each core network of HVQ-Trans is robust to logical and structural anomalies, is there no way to verify this using t-SNE visualization of the network output features?\n\n- What meaning does the architecture of this work have beyond being a combination of existing MIM methods and VQ methods?\n\n[1] A unified anomaly synthesis strategy with gradient ascent for industrial anomaly detection and localization (ECCV 2024)\n\n[2] Hyperbolic anomaly detection (CVPR 2024)\n\n[3] Towards real unsupervised anomaly detection via confident meta-learning (ICCV 2025)\n\n[4] SALAD--Semantics-Aware Logical Anomaly Detection (ICCV 2025)\n\n[5] Towards Training-free Anomaly Detection with Vision and Language Foundation Models (CVPR 2025)", "broader_impact_concerns": "This work does not involve privacy-sensitive personal data or ethically problematic datasets; therefore, there are no broader impact concerns to report.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "C0NiSzpeRK", "reviewer": "Reviewer_nwVm", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "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": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This paper proposes LADMIM, a unified unsupervised anomaly detection framework that effectively detects both structural and logical anomalies. By combining HVQ-Trans and ViT, the method focuses on predicting the probability distribution of discrete latent variables in masked regions, which is invariant to positional uncertainty and better captures global logical relationships. Extensive experiments on MVTecLOCO and MVTecAD show that LADMIM significantly outperforms existing MIM-based approaches and achieves competitive performance with state-of-the-art methods.", "claims_and_evidence_yn": "Yes", "claims_explanation": "Strengths\n* Effectively targets logical anomalies arising from object relationships, which are typically challenging for anomaly detection methods that rely primarily on local features.\n* Adopts a clear functional separation between structural and logical anomaly detection, leveraging HVQ-Trans for structural anomalies and a ViT-based MIM model for logical anomalies.\n* Demonstrates substantial performance improvements over existing MIM-based anomaly detection methods for both logical and structural anomaly detection tasks.\n* Operates in a fully unsupervised manner without requiring segmentation priors, enhancing its applicability and generalization potential.\n\nWeaknesses\n* Is unable to detect order-sensitive logical anomalies, as the histogram-based discrete latent distribution does not preserve spatial or sequential ordering information.\n* Provides limited discussion on time-based robustness and distribution shift, since the experimental evaluation is restricted to static datasets.", "audience_yn": "Yes", "audience_explanation": "This paper addresses the important and relatively underexplored problem of logical anomaly detection in unsupervised settings, which is relevant to the TMLR audience interested in representation learning and self-supervised methods.", "requested_changes": "* Investigate adaptive or uncertainty-aware masking strategies at inference time. Introducing adaptive masking based on reconstruction or prediction uncertainty may improve robustness and reduce failure cases.\n* Provide a more detailed analysis of computational cost and efficiency trade-offs.\n* Text and numbers in the plot (such as figure 3) is small that it is sometimes hard to read. Please increase the font size to be more readable.", "broader_impact_concerns": "No concerns.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "lCgGfQsde8", "reviewer": "Reviewer_9764", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "No", "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": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "Unfortunately this paper is outside my area of expertise and thus I am unable to review it.", "claims_and_evidence_yn": "No", "claims_explanation": "Unfortunately this paper is outside my area of expertise and thus I am unable to review it.", "audience_yn": "No", "audience_explanation": "Unfortunately this paper is outside my area of expertise and thus I am unable to review it.", "requested_changes": "Unfortunately this paper is outside my area of expertise and thus I am unable to review it.", "broader_impact_concerns": "Unfortunately this paper is outside my area of expertise and thus I am unable to review it.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Decision: Accept (poster)