MetaUAS: Universal Anomaly Segmentation with One-Prompt Meta-Learning
Authors: Bin-Bin Gao
Venue: NeurIPS 2024 poster (2024)
Keywords: Anomaly Classification
Anomaly Segmentation
One-Prompt Meta-Learning
Universal Anomaly Segmentation
Primary area: machine_vision
TL;DR: This paper unifies anomaly segmentation into change segmentation and provides a pure visual foundation model, which requires only one normal image prompt and no additional training, and effectively and efficiently segments any visual anomalies.

Abstract: Zero- and few-shot visual anomaly segmentation relies on powerful vision-language models that detect unseen anomalies using manually designed textual prompts. However, visual representations are inherently independent of language. In this paper, we explore the potential of a pure visual foundation model as an alternative to widely used vision-language models for universal visual anomaly segmentation.
We present a novel paradigm that unifies anomaly segmentation into change segmentation. This paradigm enables us to leverage large-scale synthetic image pairs, featuring object-level and local region changes, derived from existing image datasets, which are independent of target anomaly datasets. We propose a one-prompt Meta-learning framework for Universal Anomaly Segmentation (MetaUAS) that is trained on this synthetic dataset and then generalizes well to segment any novel or unseen visual anomalies in the real world. To handle geometrical variations between prompt and query images, we propose a soft feature alignment module that bridges paired-image change perception and single-image semantic segmentation. This is the first work to achieve universal anomaly segmentation using a pure vision model without relying on special anomaly detection datasets and pre-trained visual-language models. Our method effectively and efficiently segments any anomalies with only one normal image prompt and enjoys training-free without guidance from language. Our MetaUAS significantly outperforms previous zero-shot, few-shot, and even full-shot anomaly segmentation methods. Code and Models: https://github.com/gaobb/MetaUAS.

MetaUAS: Universal Anomaly Segmentation with One-Prompt Meta-Learning
Bin-Bin Gao
Tencent YouTu Lab, Shenzhen, China
csgaobbogmail.com com
Code and Models: https://githuu: http://ww...ooggggg///ttttllllat
Abstract
Zero- and few-shot visual anomaly segmentation relies powerful vision-language models that detect unseen anomalies using manually designed textual prompts. However, visual representations are inherently independent of language. In this paper, we explore the potential a pure visual foundation model as an alternative widely used vision-language models for universal visual anomaly segmenta- tion. We present a novel paradigm that unifies anomaly segmentation into change segmentation. This paradigm enables to leverage large-scale synthetic image pairs, featuring object-level and local region changes, derived from existing image datasets, which are independent of target anomaly datasets. We propose a one- prompt Meta-learning framework for Universal Anomaly Segmentation (MetaUAS) that is trained on this synthetic dataset and then generalizes well segment any novel or unseen visual anomalies in the real world. To handle geometrical vari- ations between prompt and query images, we propose a soft feature alignment module that bridges paired-image change perception and single-image semantic segmentation. This the first work achieve universal anomaly segmentation using pure vision model without relying on special anomaly detection datasets and pre-trained visual-language models. Our method effectively and efficiently segments any anomalies with only one normal image prompt and enjoys training- free without guidance from language. Our MetaUAS significantly outperforms previous zero-shot, few-shot, and even full-shot anomaly segmentation methods.
1 Introduction
Visual anomaly classification (AC) and segmentation (AS) aims to group images and pixels into two different semantics, normal and anomalous, facilitating many applications such as industrial defect inspection in manufacturing 4
39 3 74,, medical image diagnosis [28] 65), and video
surveillance [53] 40 [8]. etc. Generally, AS is performed first, and then AC obtained based on post- processing of the segmentation results. Therefore, AS is more essential than AC. AS can be viewed as binary semantic segmentation assuming that pixel-level annotated images are available. Unfortunately, it is usually difficult to collect anomaly images due to their scarcity in practical applications, and pixel-level annotations also involve labor costs. Based on available training images, e.g., only normal, normal with noise, few-shot normal or anomaly, and multi-class normal, have led to different types of AS tasks, such as unsupervised [47] 31
12, 37 29]. fully-unsupervised 99 27. 38 few- shot [24] 14 66 69 [5), and unified AS 770 etc. These methods achieve excellent performance
72
on seen objects but often perform poorly on unseen objects.
To address this fragmentation, recent works, such WinCLIP 26 and AnomalyCLIP (76), have attempted to design universal models, that are capable recognizing anomalies for unseen objects. They typically build on vision-language models (i.e., CLIP 42 benefiting from strong generalization
38th Conference on Neural Information Processing Systems (NcurIPS 2024).

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ability. However, WinCLIP 26 still struggles with handerafted text prompts about defects. Further, AnomalyCLIP [76] learns general prompt embedding through developing specialized modules but requiring fine-tuning on an auxiliary domain dataset with pixel-lveel annotations. In addition to being flexible, these laann-llanggg-aaeeeeeee methods are still weak in anomaly segmentation. Accurate anomaly segmentation is crucial in real-world applications, such as industrial inspection, anomalies often relate to the area, shape, and location where they occur. On the other hand, we know that visual representations are not dependent on language in the animal world [16). In particular, the principles of visual perception in non-human primates are very similar to humans [58]. Although there is room for universal AS based visual- -anguga models and worthwhile further to pursue, in this paper, we want to explore how far we can go with a pure visual model without any guidance from language.
To explore more general AS framework, lets first review how the visual system perceives anomalies. Generally, humans can perceive anomalies when an input significantly deviates from those normal patterns stored in our brains. There is evidence to support this point in neuroscience. For example, predictive coding theory model". The mental model compares its expectations (or predictions) with the actual inputs from the visual cortex. This process allows the brain to perceive anomalies. In fact, PatchCore [47 captures normal local patch features, stores them in memory bank, and recognizes anomalies by comparing input features with the memory bank. In addition, some distribution based methods [12] learn a multivariate Gaussian distribution from normal local features and then utilize distance metric to measure anomalies. However, these memory- and distribution-bust methods usually require a certain number of normal images and thus are limited in universal (i.e., open- scenarios.
+ postulates that the brain constantly generates and updates "mental
Actually, we can build similar concepts in AS. First, given one normal image prompt for each class, we take it as the expected output. Then, the actual input could be any query images from the same class of the normal prompt. Last but not least, how to construct a "mental model" to compare between given normal image prompt and any query images. Despite these challenges, we can imagine that the "mental model" should satisfy several basic principles. First, it should have a strong generalization ability to perceive anomalies facing unseen objects textures. Second, it can perform pixel-level anomaly segmentation only given one normal image prompt. Third, its training does not depend on target domain distribution or any guidance from language.
To obtain the "metal model", we rethink AS tasks and find they can be transformed into change segmentation between one normal image prompt and query images. From this novel perspective, we are capable of leveraging a larger number of synthetic image pairs that exhibit appearance changes based on available image datasets. We assume these synthesized image pairs carry mask annotations indicating change regions. Inspired by the "mental model" in predictive coding theory (43), we propose simple but effective framework that learns the "metal model" in a one-prompt meta-learning manner. The meta-learning ensures strong generalization [8] when applying the model for segment unseen anomalies. Our contributions summarized as follows:
We present a novel paradigm that unifies anomaly segmentation into change segmentation. This paradigm enables us to leverage large-scale synthetic image pairs with object-level and local region changes, thereby overcoming the long-standing challenge of lacking large-scale anomaly segmentation datasets.
We propose one-promp meta-learning framework training synthesized images and generaliz- ing well on real-world scenarios. To handle geometrical variations between prompt and query images, we proposed soft feature alignment module that builds bridge between paired-image change perception and singe-image semantic segmentation.
We provide a pure visual foundation model for universal anomaly segmentation that can serve as alternative to widely used vision-language models. Our method, which requires only single normal image prompt and no additional training, effectively and efficiently segments any visual anomalies. On three industrial anomaly benchmarks, our approach achieves of-th-rt- performance, while also enjoying faster speed and requiring fewer parameters.
2 Related Work
Unsupervised AS aims to segment anomaly pixels for both normal and anomaly testing images only given full normal training images. Unsupervised AS can categorized into two learning paradigms, separated and unified models. Most AS methods focus on training separated models for different

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objects or textures. However, this separated paradigm may be impractical, as it requires high memory consumption and storage burden, especially with the number of classes increasing. In contrast, the unified models attempt to detect anomalies for all categories using a single model. Compared to the separated mode, the unified paradigm is more challenging as requires handling more complex data distributions.
From a modeling perspective, AS methods can be roughly grouped into three groups, embedding, discriminator, and reconstruction. Embedding-based methods, such as PaDiM [2]. MDND [44), PatchCore 477, CS-Flow [49] and PyramidFlow (29 assume that offline features extracted from pre-trained model preserve discriminative information and thus help to separate anomalies from normal samples. They usually model normal features to a normal distribution or store them in a memory bank. Then, anomaly scores are calculated by comparing testing features and the modeled distribution or the memory bank. Discriminator- baseo methods, such as CutPaste [1. DRAEM 72,, 10. and SimpleNet [37. typically convert unsupervised AS to supervised ones by introducing pseudo (synthesized) anomaly samples. The pseudo-anomaly samples are generated by pasting random patches or adding Gaussian noise to normal images or features. Naturally, a binary anomaly classifier or segmentation model can be trained on normal and pseudo-anomaly samples a super- vised manner. Reconstruction-basse AS, such as autoencoder [59] 2 20 23], generative adversarial networks [4] 67 71 and reconstruction networks 73 45 36], assume that anomalous regions
should not be able to be properly reconstructed and thus result in high reconstruction errors since they do not exist in normal training samples. These methods tend to be computationally expensive because they involve reconstruction image space. The recent knowledge distillation [S] 61 52 [3]
or feature reconstruction methods (70 75 68 train a student or reconstruction network to match a fixed pre-traine me-ttraned teacher network and achieve good balance between effectiveness and efficiency.
However, all these methods are limited to recognizing anomalies in a close set as the same as the training set but often perform poorly on unseen classes in open-world scenarios.
Few-shot AS pays attention to learning with only a limited number of normal samples. TDG 54 proposes multi-scale hierarchical generative model, which jointly learns self-supervised discrim- inator and generator in an adversarial training manner. DifferNet [48] detects defects utilizing a normalizing- flow-based density estimation from few normal image features. RegAD [24] learns the sategory-agnostee fcature registration, enabling the model to detect anomalics in new categories given few normal images without fine-tuning- GraphCore (66) utilizes graph representation and provides a visual isometric invariant feature. FastRecon [15] utilizes a few normal samples as a reference to reconstruct normal version for a query sample with distribution regularization, where the final anomaly detection can be achieved by sample alignment. Some works [4 69] consider another few-shot setting where a limited number of samples is given from the anomalous classes. Instead of learning few-shot models with a few normal or anomaly images, we push it to a new extreme only using one normal image as visual prompt at the inference stage, not involving model training.
Zero- and Few-shot AS mainly utilizes large pre-trained trained vision- -aiionna models, e.g., CLIP [42), have shown unprecedented generality, and achieved impressive performance. WinCLIP 26 firstly utilizes multiple handcrafted textual prompts on powerful CLIP model that can yield excellent zero- and few-shot AS performance. AnomalyCLIP 76] learns object-agnostic text prompts that capture generic normality and abnormality in an image regardless of its foreground objects. InCtrl [77] detects residual CLIP visual features between test images and in-context few-shot normal samples. However, optimizing AnomalyCLIP (76) and InCtrl 777 requires an auxiliary domain dataset including normal and anomaly images. PromptAD K2
introduces the concept of explicit anomaly margin, which mitigates the training challenge caused by the absence of anomaly images. Furthermore, AnP 21 incorporates risual-languaas model and large language model applying multi-
turn dialogues. It not only indicates the presence and location of the anomaly but also provides a
detailed description of anomalies. Instead of visual-language models, ACR [30] and MuSc [33] perform zero-shot AS only requiring information from batch- and full-level testing images, but they may be limited in privacy protection scenarios. Overall, existing most methods primarily use textual prompts based on risual-langual explore universal AS using one normal image as a visual prompt without guidance from language or information from testing images.
models to identify anomalies. Different from these methods, we
3

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(4 M1
x
(P))
((,)) + Conv(O.)
F(,,) Encoder
Corce Ba)
Decoder
FAM
ro
IF H
Frozen
Learnabee
Figure 1: The proposed MetaUAS consists of an encoder, a feature alignment module (FAM), and a decoder. is trained on synthesized dataset in a one-prompl meta-learning manner for change segmentation tasks. Once trained, it can segment any anomalies providing only normal image prompt.
3 Method
3.1 Rethinking Anomaly Segmentation
Unlike traditional image segmentation, since anomaly appearance has various ways, it is hard to exhaustively pre-define and collect enough anomaly samples to train an AS model. Most unsupervised AS methods have to use normal samples for building models. However, these unsupervised models limited to recognizing anomalies in a close set but often perform poorly on unseen classes in open-world scenarios, In contrast, humans can quickly learn novel concepts from few training examples. To this end, few-shot AS aims adapt novel classes by only providing few normal images. Unfortunately, existing few-shot AS models are still far behind unsupervised ones.
Zero- and few-shot AS methods rely on powerful vision-language models that are capable of han- dling unseen anomalies, benefiting from their strong generalization. For further boosting zero-shot performance, some recent works attempt optimize models by auxiliary domain dataset including normal and anomaly images with pixel-level annotations. Although they can generalize to different domains, training models with normal and anomaly images conflict with the original intentions of anomaly segmentation to some extent. In addition, visual representations are not dependent on language prompts. Given this perspective, natural question emerges: can an image prompt visual model replace textual prompts vision-lange language approaches for universal anomaly segmentation? And can such a one-prompi vision model be trained on a non-anomaly segmentation dataset?
This paper explores and attempts to answer the above questions. Generally, anomalies mainly include "appearance", "disappearance". and "exchange", which are very similar the types of changes in change segmentation [60]. Change segmentation aims to identify changes that occur on a pair of images captured different times. Therefore, anomaly segmentation will be absorbed into change segmentation if we regard normal prompt and query as a pair of images captured at different times. Indeed, one can imagine that if model capable of perceiving changes, would naturally generalize to anomaly segmentation. This simple transformation allows us to achieve universal AS with visual modality alone. This reason is change segmentation does not require anomaly images, as they can be trained using pairs of images containing any changes, which are easily synthesized by available image datasets.
3.2 One-Prompt Meta-Learning for Universal Anomaly Segmentation
Given a change segmentation dataset Diase (XP, , X² YiJ., where (XP, X?) denotes the i-th image pair and Y; is its corresponding change mask. MetaUAS trains a meta-model based on the base set Diase and then segments query images { Ji=1 with the corresponding normal image prompt X²" from a novel set D Dnovel, where x and X belongs to the same class. Note that the base and novel set are non-overlapping (i.e., Dbase D (oveet
Overview. As shown in Fig. [ our MetaUAS framework is mainly composed of an encoder, a feature aligmment module, and decoder. The encoder extracts hierarchical features using pre-trained model, while the decoder integrates query and prompt features to predict change heatmap. The feature alignment module is bridge between the encoder and the decoder. It aligns the query and normal prompt features to address the geometric variation in spatial position. Concretely, for query image X and its corresponding prompt image X² (X" and X Hx we parallelly extract

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scti offline features, {E li=1 and (FP E Ruixx from the encoder, where denote the (-th stage of the encoder. Then, the feature alignment module independently processes F7 and Ff' for aligning query and prompt in feature space at each scale. Next, these aligned features are contacted and fed into the decoder for predicting change heatmaps. The MetaUAS trained in a meta-learning manner. At inference, given a query image and its normal image prompt, the anomaly mask can be directly predicted. Next, we elaborately introduce them in this section.
Encoder. MetaUAS is compatible with any hierarchical architecture. Considering efficiency, we use the standard convolution -aasdd EfficientNet-b4 [57] as our encoder following UniAD [70]. Given a query image X and its prompt X", we extract multi scale features F7 and Ff" from the l-th
(0=11 1,2,... ... 55 stage of a pre-trained encoder F(:: 0e). that is
Ff F(X"; 0,), = F());
(I)
where 0g is frozen to sufficiently utilize its generalization because it is pre-trained on a large-scale ImageNet.
Feature Alignment Module. We expect to Iearn comparison between query and prompt features (F' and F(') for improving change segmentation. A simple and naive manner is to directly contact them along the channel dimension, that is
Fi Concat (F), FP')
(2)
Then, the fused features Fi are fed into decoder to perform change segmentation. This simple fusion manner may only work when the query and its prompt are aligned in pixel space. However, this usually does not hold in practical applications because it is hard refrain from geometric variations between query and prompt. Therefore, we have to align query and prompt features for better change segmentation. We propose two alignment strategies, hard and soft alignment, for enabling the interaction between query and prompt features.
Hard Alignment aims to search the most similar prompt feature spatial dimension for each query feature. Here, we take cosine similarity (.) as a distance measure. Formally, for any query feature FP (i, E R9, the most similar prompt feature is
PP (i. + FP( (argmin k.1
where (i,j) and (k; denote spatial locations. Considering computational efficiency, we use 1 1 convolution layer Conv(:; 0a) with shared parameters 0g to reduce the dimension of the channel before computing the cosine similarity, that is
ETT + Conv (F;; 0o), 1P + Conv(FF;
(4)
Soft Alignment is different from the hard alignment, which aligns each query feature with a weighted combination on the prompt feature. Similar the hard alignment, we first apply Eq. 4 to reduce computation. The weighted probability is computed with the softmax function on a cross-similarity between the query and prompt features, that is
W WiJkz = Softmax (FF
(5)
where the Softmax operation is applied to the last two dimensions, and thus Σx Σn W I3w = 1. Finally, the aligned prompt feature can be obtained by the weight and original prompt feature, that is
F( + ΣΣ W Wy Fk,
(6)
The hard and soft alignment can adaptively align prompt features with query features, and thus handing geometric variation between query and prompt images to some extent. Appling Eqs. 3 or 6
we obtain an aligned prompt feature and then replace the original prompt feature F." in Eq. with
Decoder. Considering efficiency, we apply the feature alignment module to three high-level features of prompt and query, and thus three fusion features {Fi}² are derived. Change segmentation needs to predict each pixel to determine whether it is changed. Specifically, we utilize UNet [46] ((:;0, as our decoder because is better suited for tasks requiring high precision and the preservation of fine-grane fine-grained details. The UNet integrates all three fused features and two low-level original features and produces a final feature the original image resolution. Finally, a segmentation head transforms the final feature to generate pixel-level change prediction Y. The segmentation head is implemented by simple 1 Ix] 1 convolution layer, Conv(:; 0n), following sigmoid activation.
5

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3.3 Synthesizing Change Segmentation Images
Remote sensing 7 and street scenes [ are two main scenarios in change segmentation. They mainly focus semantic change, and various noises are included in unchanged background regions. Furthermore, the dataset scale is small and the diversity is insufficient. Therefore, it is not suitable for universal change segmentation. Recent works [64 63 22 have exploited generative difftusion models to create synthetic datasets and presented promising performance on real datasets. However, it is hard to guarantee generative annotations are accurate. In contrast, some works have shown that simple synthesis, such as copy-paster also brings strong performance for instance segmentation [19 51 and anomaly segmentation [3] 72. Similar to these works, we want to leverage synthetic change segmentation dataset with accurate change masks. As early discussed, there are three main change types, "appearance", "ddsappearance". and "exchange", where "appearance" and "disappearance" are pair of opposite concepts, and they can be transformed into each other by swapping paired images. Therefore, we only need to synthesize two change types to simulate all three ones.
Object-Level Change. In the famous MS- COCO (35), instances are annotated with poly- gons, and thus their foreground masks are avail- able. Following CYWS [50), given random instance and its binary mask from an image, we could make disappear from the image by in- painting the mask region (56). In this simple manner, we can simulate the "disappearance" change. Meanwhile, the change mask is freely available. The "appearance" change can be eas- ily obtained by swapping original and unpainted images. challenging to synthesize the "ex- change" change because two different instances usually mean different masks. But we can ran- domly paste
multiple instances to given image for rough simulation.
Figure 2: Selected synthesizing image pairs and their change masks. (a) and (b) simulate "appearance" and "disappcarance" synthesizing with mask inpainting (56), and (50), (c) simulate "exchange" synthesizing with random pasting, and (d) simulate local region changes synthesizing with DRAEM (72).
Local- Reglon Change. Object-level changes are generated only by inpainting mask regions or randomly pasting objects as shown in Fig 2 However, anomaly changes are usually diverse, and they may be whole objects local regions.
The local "disappearance" and "appearance" may be failed by inpainting because local regions are easily restored by context. Following DRAEM [72], we first generate binary change mask with Perlin noise and then synthesize a new image by filling the mask region with another image pixels. This synthesis simulate local changes since the binary mask is generated randomly. Given changed image pair, we apply various data augmentations, such as scale transformations, translation, rotation, and color jittering to enhance the diversity of changes during training phase.
3.4 Training and Inference
Traning. We train MetaUAS in meta-learning manner, and each meta-task (X,, Xy, Y;} is one prompt-query pair. The binary cross-entropy loss adopted to optimize the learnable parameters (0.. 0g. and 0%) of MetaUAS, that is
L C= Ex- (ogg)) + (1 (0-Yx) Y)) log(1 (011--Y,)) -Y.)
Inference. For a class- -pess-s query image X", we first randomly select normal image prompt X" from the corresponding normal training set and then process prompt-query pairs online to perform anomaly segmentation. For a alasstagnostic query image, we need to first construct class-aware prompt pool { [ via extracting offline features of all normal prompts { C, in a total of C classes, and then derive the best matching prompt by computing the cosine similarity between the query feature F and the prompt pool. Here, the query and prompt features are obtained by using a global average pooling on the last stage feature from the encoder.

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(b)
(c) (d)
(g)
(0)
(b)
(0)
(d)
(C)
Figure 3: Qualitative comparisons with state-of-the-ee methods on MVTec VisA and Goods. In both two sub-figures (left and right), (b) and (g) represent query images and their anomaly masks, while (a) represent the corresponding normal image prompts. The predicted anomaly maps are shown using different methods, including (c) WinCLIP+ (26). (d) AnomalyCLIP (76), (c) UniAD 770 and (f) our MctaUAS. Best viewed in color and zoom-in.
Given a query image X and the corresponding prompt X", we successively feed them into the encoder, the feature alignment module, the decoder, and the segmentation head, and finally obtain predicted anomaly map Y. Following previous works, we take the maximum of y as the image-level anomaly score, without additional post-procet post-processing.
4 Experiment
4.1 Experimental Setup
Following previous works, we comprehensively evaluate MetaUAS on three industrial anomaly segmentation benchmarks, MV'ee E. VisA (78) and Goods [74). We train universal change segmentation model on a synthetic dataset. To demonstrate cross-domain generalization ability, we directly test it three industry anomaly detection benchmarks without fine-tuning
Competing Methods. We compare MetaUAS and its two variants (MetaUAS* and MetaUAS++) with diverse state-of-the-art anomaly segmentation methods including zero-shot CLIP 42 WinCLIP (26). AnomalyCLIP (76), and one-shot PatchCore 47, WinCLII+ (26), and full- shot UniAD 77. MetaUAS segments any anomalies with only one normal image prompt. Here, the one normal prompt is randomly sampled from normal training images for each class. The results are mean and standard deviation based on 5 independent repeated tests with different random seeds. MctaUAS takes the best-matched normal image from the normal training set as the prompt of query image. Here, the matching degree is computed using the cosine similarity between the query image and all normal training images feature space. MetaUAS builds on MctaUAS*. Following WinCLIP+ (26). we also add the visual prior knowledge from the image encoder of CLIP model to our MetaUAS for a fair comparison. The visual prior knowledge used in MetaUAS is kept exactly the same as WinCLIP+.
Evaluation Metrics. Following previous works, we use ROC, PR, and Flaax metrics for image-level anomaly classification. Similar to the anomaly classification evaluation, we use the same metrics and additionally report Per-Region Overlap (PRO) for pixel-level anomaly segmentation. We argue that the PR and F1 max metrics are better for anomaly segmentation, where the imbalance issue is very extreme between normal and anomaly pixels 78).
4.2 Comparison with Previous Works
Tables and present the comparison results of MetaUAS with the above-mentioned competing methods in generalization and efficiency, respectively.
7

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Table I: Quantitative comparisons MVTec VisA and Goods. Red indicates the best performance, while blue denotes the second- best result. Gray indicates the model is trained by full-shor normal images.
Anomaly Classification
Anomaly Sogmentation
Datasets Methods
Venue Shot Auxiliary
I-ROC
I-PR
I-Fl
P-ROC
P-PR
P-Fl
P-PRO
CLIP 12
ICML
74,4
89.3
88.7
62.0
6.5
11.2
21.4
PatchCore
37
CVPR 22
1
79.0=#0.8
89.6
88,9=0.33
93.1+:02
37.1=009
42.2==0.8
82.7
WinC1.IP
26
CVPR 23
0
X
90.4
95.6
92.7
82.3
18.2
24.8
61.9
MVTec
WinCLIP+ 26
CVPR
92.8+
96.4
93.8=
93.5:t0.2
38.4
1.
83,9:00.4
AnomalyC1.IP 76
ICLR 24
0
V
91.5
96.3
92.7
91.1
34.5
39.1
81.4
UniAD 00
NeurlPS 22
full
96.7
98.9
96.7
96.8
44.7
50.4
90.0
MetaUAS
90.7++0.7
95.7=00.6
02.5++0.3
94,6+0022
59.3+
57,5+
82.6+:0.6
MetaUAS
94.2
97.6
93.9
95.3
63.7
61.6
83.1
MetaUAS
95.3
97.9
94.6
97.6
67.0
62.9
92.5
CLIP 4
ICML 21
59.1
67.4
74.5
56.5
1.8
3.6
22.4
PatchCorz
17
CVPR 22
1
64.2
0..0
75.5:10.55
95.5:10.3
16.5 1.7
26.0
84.6:10.5
WinCLIP
26
CVPR 23
0
X
75.5
78.7
78.2
73.2
5.4
9.0
51.0
WinCLIP+
26
CVPR
80,5422.
82.1
81.3+100
94,4410.1
15.9+0.2
23.2+0.4
79.3++0.3
VisA
AnomalyC1 11
76
ICLR 24
81.9
85.4
80.7
95.5
21.3
28.3
86.8
UniAD 70
NeurlPS 22
full
90.8 X
93.2
87.8
98.5
34.3
39.1
84.8
MetaUAS
81,21
84.5
1.4 80.2:10.7
92.2:10.7
42.7:10.8
44.7:10.6
1..4:11.5
MetaUAS
1
83.4
85.7
81.3
92.0
43.9
45.6
57.3
MetaUAS
85.1
87.2
82.3
98.0
48.1
48.6
85.5
CLIP 4P
ICML 21
0
X
51.8
57.3
71.3
55.3
4,3
2.0
16.4
PatchCors
48.31
54.2.20.5
71.3:10.1
84.3:1.55
4.5:0.2
9.340.3
55.6:1:1.0
WinCLIP
26
CVPR 23
0
52.2
58.2
71.4
73.0
5.0
10.2
44.5
Gooods
WinCLIP+
26
CVPR 23
1
X
53.5+
58.620.2
71.5+
85.5:+0.66
5.7
01.
56.6+11.2
Anomaly
CLIP
ICLR
57.2
63.3
71.4
83.5
16.9
24.0
63.3
UniAD
A0
NeurlPS 22 full
67.5
72.1
74.6
90.4
15.0
20.6
66.1
MetaUAS
54.51 1.0
58.5
71.5:00.1
88.510
8.6:10.7
14.0++0.7
59.0
MetaUAS
1
90.1
91.7
85.7
97.4
53.7
55.5
70.8
MetaUASS
89.9
89.9
86.2
97.9
49.0
55.8
88.0
Generalization. First, MetaUAS with one normal image prompt achieves competitive performance among all zero-, few- and full-shot methods both on MVTec and VisA. This suggests that it is possible to boost anomaly classification and segmentation performance only with visual information alone. But on Goods dataset, MetaUAS seems to perform similarly to other methods. Different from MVTec and VisA, Goods consists of six groups and each group contains dozens or even hundreds of subcategories (484 in total). In fact, it is challenging to address multi-classes with a single model, and the state-of-the -ftt--artttt- UniAD is not good even using all normal training images. contrast, MetaUAS only uses one normal image prompt for each group, which means that the prompt image does not match most query images from multiple subcategories. Furthermore, MetaUAS* significantly outperforms almost all competing models when the best-matched normal image is used to take the normal prompt of each query image. In addition, WinCLIP+ boosts few-shot AS with dense similarity between few-shot prompts and query images. For fair comparison, we also add the visual prior knowledge from the image encoder of CLIP model to our MetaUAS* (denoting as MetaUAS++)) We that the performance can be further improved when introducing the visual prior of CLIP models to MetaUAS+.
Efficiency. We measure complexity and efficiency with the number of parameters and forward inference times. The evaluation is performed on one V100 GPU with batch size 32. The number of parameters of WinCIlP+ and AnomalyCIIP is 10x and 20x of MetaUAS due to the large vision- language backbone. Compared to state-of-the-un our MctaUAS+ achieves the best performance using the single model with half of the parameters and faster inference time. What is more, MetaUAS has 10x fewer parameters and 100x speed improvement compared to WinCLIP+, which still performs better.
Qualitative Comparisons. Figs. A1 and show some selected visualizations from MVTec, VisA, and Goods testing images using the of-te-o--arss and our MetaUAS. Generally, MetaUAS segments anomalies more accurately and produces fewer false positives. MetaUAS is robust to different image prompts from the same category, especially for those categories with large geometric variations, such as screw. We believe that better performance can be derived if the objects or textures of the prompt image and query images can be roughly aligned.
8

---

Table 2: The complexity and efficiency comparisons. The performance of anomaly classification and segmenta-
tion reported on MVTec.
Methods
Backbone
#All Params(#Lcanaale)
Input Size
Times (ms)
I-ROC
P-ROC
P-PR
CLIP
42
ViT-B-16+240
208.4 (0.0)
240x
74.4
62.0
6.5
47
256x226
36.4
79.0::0.8
93.1=0.2
37.1+-0.9
PatchCorc
E-b4
17.5 (0.0)
512x512
145.1
79.110.7
93.1=0.2
37.
WinCLIP 20
201.3
90.4
82.3
18.2
ViT-B-16+240
208.4 (0.0)
240x220
WinCLIP+
339.5
92.8+12
93.5=0.2
38.411.2
Anomaly
VIT-L/14 336px
433.5 (5.6)
518x518
154.9
91.5
91.1
34.5
UniAD 70
Eb4
27.1 (7.7)
224x
5.0
96.7
96.8
44.7
MctaUAS
90.7110.7
9..6-002
59.3.11,4
Eb4
22.1 (4.6)
3.1
MetaUAS
256x256
94.2
95.3
63.7
MetaUAS++
BM4-VIT-B-1+2400
139.3 (4.6)
204.8
95.3
97.6
67.0
MetaUAS
90.4 1.8
92.9=0.4
57.2+11.9
Eh4
22.1 (4.6)
12.0
MetaUAS
512x512
93.2
93.3
MetaUAS++
Eb4-VIT-B-162400
139.3 (4.6)
213.0
94.8
97.1
65.8
Query
Power1
P
3
Pheret
Proupe
Prosuct
GT Mask
Metutit
MistrAs
MeucAs
Mestino
Mestin
Ment
Query
Poorg
Picepe
Prorgg
GT Mask
McDLAS
MeaUAS
MEQUAS
ModUAS
MeaUAS
MeulisS
MeaLAS
Figure 4: Anomaly segmentation for query images with different normal image prompts including 5 random prompts and optimal prompt (denoting as prompt*). The anomaly segmentation maps are generated with MetaUAS, MetaUAS* and MctaUAS++.
4.3
Ablation Study
We perform component wise analysis MVTec with x56 inputs.
The influence of feature alignment module. As reported in Tab. Bb. we conduct experiments with combinations of different align strategies and feature fusions. The experimental results demonstrate that using soft alignment better than the other two ones (no alignment and hard alignment) the comprehensive results of AC and AS. Moreover, we compare the feature concatenation (Concat) with clement-wised addition (Add) and absolute difference (AbsDiff) for aggregating prompt and query The Add is not suitable fuse two types of features
features. The Concat operation is the best as it fails to depict the image changes, and it may lead to confusion between these two features, as its results are the worst. The AbsDiff is widely used in the change segmentation field. However, this method may result the loss of contextual information. In contrast, direct concatenation preserves all information and allows the network to adaptively leam the fusion, yielding the best results.
The effects change types. The diversity synthetic data critical for good generalization. We use object-level changes and local region changes to ensure this diversity. Experiments show that object-level changes contribute more performance than local changes in Tab. Bp. This is natural because the object-level change is implemented by inpainting object regions and randomly pasting

---

Table 3:
Ablation studies on MVTec. Default settings are marked in blue.
(a) Effect of feature alignment module.
(b) Learn or freeze encoder?
Aolgn Fusion |-ROC I-PR P-ROC P-PR P-PRO
No. Mo.cBonk Learn?
I-ROC I-PR P-ROC P-PR P-PRO
1
No Concat 82.8
92.5
88.4
44.9
67.5
1
E-b4
Leam
86.5
93.6
93.1
50.3
74.6
2
Hard Concat
87.1
94.7
90.7
48.2
77.0
2
E-b4
Freeze
91.3
96.2
94.6
59.6
82.6
Soft Concat 91.3
96.2
94.6
59.6
82.6
3
E-h6
Freeze
90.1
95.5
95.1
56.9
80.8
4
Soft Add 71.8
86,9
73.2
24.0
45.2
EViT-b3
Freeze
89.5
95.7
95.3
58.5
80.9
5
Soft AbsDift 84.1
92.4
88.4
45,9
68.4
5
M-v2
Freeze 76.2 87.8
87.6
33.7
61.0
(c)
Effects of change types and decoder module.
(d) Effects of the number of training samples.
No.
Ny. Decoder
||-ROC I-PR P-ROC P-PR P-PRO
No. #Samples I-ROC I-PR P P-ROC P-PR P-PRO
Only Loc.
UNet
83.1
92.8
87.7
44.3
76.1
1
10%
82,0
91.9
85,4
36.5
62.1
2
Only Obj.
UNet
90.5
96.0
94.5
58.3
75,4
2
30%
87.4
93.6
89.1
50.6
73.8
Obj++Loc.
UNct
91.3
96.2
94.6
59.6
82.6
3
50%
91.0
96.2
92.9
57.1
74.3
Obj.+Loc.
FPN-Cat
86.9
86.9
91.6
49.9
76.7
4
70%
91.1
96.4
94.5
57.0
78.3
5
Obj.+Loc. FPN-Add
88.4
94.7
94.1
51.4
73.1
5
95%
91.3
96.2
94.6
59.6
82.6
objects, which has a larger space than local region synthesis. Undoubtedly, combining them further enhances the diversity of synthetic changes, thus further improving performance.
Learn freeze encoder. In Tab. Bp, if we train the encoder like other modules, the performance drops on both AC and AS. We speculate that it makes the network overfit change segmentation dataset, which will degenerate generalization. We also evaluate other backbones, such EfficientNet-b6 [57], Efficient ViT-b3 [ and MobileNetV2 [53]. The EfficientNet- b4 performs better than others. The reason might be that shallow networks cannot extract discriminative features, while deep networks focus more on semantic features. AS requires more structural and texture features.
The effects of decoder module. In the decoder, we compare UNet [46] with FPN [34]. U-Net has been widely used and validated in many segmentation tasks due to its effectiveness and efficiency, while FPN is type of network designed for object detection that requires recognizing objects at various scales. As reported in Tab. 3c, both different types of FPN are worse than UNet.
The effects on training samples scale. To investigate the influence of training samples scale on model performance, we conduct experiments with different training subsets where each one is generated by randomly sampling the original training set at various rates, such as (10%, 30%, 50%, 70%, 95%) J. The performance of each model on the MV'ee testing set is reported in Tab. 3d. It can be seen that MetaUAS still works when the number of training images is small scale (e.g., 50%), and the performance can further improve when increasing the number of training samples.
5 Conclusion
This paper is the first study to focus on universal anomaly segmentation using pure visual information, enabling the segmenting of unseen anomalies without any training on target anomaly datasets or reliance on language guidance. First, we rethink anomaly segmentation tasks and find they can be unified into change segmentation. This paradigm shift allows to break away from the persistent challenge of lacking large-scale anomaly segmentation datasets. Naturally, we are capable of leveraging large-scale synthetic image pairs with object-level and local region changes derived from available image datasets. Second, we propose a simple but effective universal anomaly segmentation framework, i.e., MetaUAS. We train MetaUAS in a one-prompt meta-learning manner on this synthesized dataset. To handle geometrical variations between prompt and query images, we propose soft feature alignment module that bridges paired-image change segmentation and singe-image semantic segmentation. This makes it possible to use sophisticated semantic segmentation modules for change segmentation. MetaUAS achieves superior generalization using only one normal image prompt on three industrial datasets. Meanwhile, MetaUAS enjoys faster inference speed and fewer parameters. We believe MetaUAS will serve as an alternative to widely used lsngual models for universal anomaly segmentation. Limitation. The performance of MetaUAS can be affected by using inappropriate normal image prompts. this study, we leverage cosine similarity to identify the most suitable prompts when the category of the query image is unknown. In scenarios involving fine-graines objects, may be essential to train classification model to accurately predict the categories of the query images.
10

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(b)
(c)
(d)
(g)
(n)
(b)
(c)
(d)
(c)
Figure A1: Qualitative comparisons with state-of of- art methods on MVTec, VisA and Goods. In both two sub-figures (left and right), (b) and (g) represent query images and their anomaly masks, while (a) represent the corresponding normal image prompts. The predicted anomaly maps shown using different methods, including (c) WInCI.IP+ (66), (d) AnomalyCI.IP 76.. (c) UniAD 770 and (f) MetaUAS. Best viewed in color and zoom-in.
15

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Table A1: Quantitative results MVTee with MetaUAS, MetaUAS* and MetaUAS*
Anomaly Classificatio
Anomaly Segmentation
Methods
Calegories
I-ROC
I-PR
I-F1 mas
P-ROC
P-PR
P-Flmux
P-PRO
bottle
98.31:0.8
9925=
9..9=
97.6
25.9
77.9
94
cable
9..8+:1.5
95.1=009
16
95.24:0.4
64.1=113
63.0=116
85.8+
capsule
67.1#5.2
89.8=3.0
91.4=005
94.2+00.
23.6=6..5
33.7=4.3
54.347.0
carpet
99.8110.3
99.9-0.1
99.3:10.7
97.4:044
73.7-1..
68.1-1177
94,4100.
grid
9..61:12
98.1=0.5
92.7+1.3
89.0+1.22
25.1=2.7
33.8+1.6
70.0+
hazelnut
97.912.2
98.9-1.2
95.0.1322
98.1
66.21998
60.31884
87.913.55
leather
99.9+0022
100.0=0.00
99.7=0.3
99.7+0000
71.2=0,9
65.4=00.8
95.8=+0.8
metal nur
94.41:99
98.6==111
95.0=t1.2
95.0+0.7
76.9
70.9+22.6
87.1
MetaUAS
pill
92.3+:144
98.5-002
94,2=111
96.410.6
70.1=2.9
63.8-2.3
88.6+2.1
serew
63.5+5.00
84.4=34
85,5+0.3
92.1+.11
8.1=2.9
14.4=3.9
72.4+5.7
95.6:10.6
98.5-0.1
94.4111
95.34:0.5
8..621.0
79.5.10.7
92.0-.0.8
toothbrush
92.21188
97.2=
91,5
98.9-:0.2
1..2
69.2-0.4
81.0
transistur
79.71666
79.3-6.8
71.914.2
8244332
37.215.1
37.7
67.113.6
wood
98.5+00.3
99.5=0.1
96.7
94.1
70.0=1.7
65.9=2.1
19.0
zipper
95.912.5
98.5 1.3
96.1=112
14
62.1
59.5
78.7+
mean
90.7:10.7
95.7=006
92.5++0.3
94.6++0.2
59.3=1.4
57.5-111
82,6+0..6
bottle
99.6
99.9
98.4
97.5
85.6
77.5
95.4
cable
95.3
97.6
91.9
96.3
67.5
65.9
90.2
capsule
80.1
94.9
93.5
95.8
40.5
48.3
57.6
carpet
99.6
99.9
98.9
97.0
73.9
68.7
93.2
grid
96.2
98.7
94.8
90.8
28.7
37.1
75.6
hazelnut
97.9
98.8
89.1
leather
100
100
99.7
65.5
96.4
MetaUAS*
metal
96.2
99.1
95.2
96.3
81.4
73.3
91.0
pill
95.3
99.2
94.7
94.8
64.8
$9.9
86.3
screw
84.2
94.5
87.6
95.0
29.4
33.4
61.7
tile
95.1
98.3
93.4
94.6
83.3
78.8
91.2
toothbrush
93.6
91.6
92.3
98.9
70.3
70.5
78.6
72.8
wood
98.8
99.6
96.8
94.3
68.4
88.2
zipper
89.3
96.3
93.7
94.2
63.7
61.5
79.0
mean
94.2
97.6
93.9
95.3
63.7
61.6
83.1
bottle
96.8
cable
95.5
97.7
91.9
97.1
67.4
66.4
91.6
capsule
83.4
95.7
92.7
97.8
43.3
49.4
90.0
carpet
99.8
100
98.9
99.5
80.6
71.0
98.0
grid
99.6
99.9
98.2
98.2
36.5
39,4
94.7
hazelnut
100
100
100
99.1
79.1
74.1
92.7
leather
100
100
99.7
71.6
98.9
MetaUAS+
metal nut
96.3
96.5
73.7
92.1
95.8
99.3
95.0
96.8
68.5
60.9
94.1
88.2
95.6
91.3
98.4
34,4
33,9
90.5
tile
96.1
98.6
94.0
98.1
88.4
79.3
95.4
toothbrush
94.4
97.9
92.3
99.4
72.6
70.9
91.7
transistor
91.1
88.5
80.5
91.6
51.0
50.6
78.6
wood
99.0
99.7
96.8
96.7
69.9
95.0
zipper
96.4
93.4
96.0
64.7
61.0
87.9
95.3
97.9
94.6
97.6
67.0
62.9
92.5
A Implementation Details.
Following UniAD 770, we extract multi-scale features from all 5 stages of EfficientNet-b4 [57 encoder. In the feature alignment module, the three highest-level features are used to perform query- prompt alignment, and the channel number is reduced to half of one of the original channels before calculating the similarity between query and prompt. Therefore, we derive three aligned features of query and prompt using the feature alignment module. Finally, these three aligned features and two original low-level query features from the first and second stages are fed into the decoder and segmentation head for change segmentation. The model is trained with 30 epochs on 8 Tesla V100 GPUs with batch size 128. We freeze the encoder and optimize the feature alignment module, the
16

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Table A2: Quantitative results VisA with MetaUAS, MetaUAS and MetaUAS++.
Anomaly Classification
Anomaly Segmentation
Methods
Categories
I-ROC
I-PR
I-F1
P-ROC
P-PR
P-Flma
P-PRO
candle
84.7=11.1
85.2
19.
99.340.1
60.04:2.3
57.3++1.7
63.0+332
capsules
77.7=3.9
86.4+:2.3
19.7
96.5
40.5+t4.1
44.8::3.4
76.9:22
cashew
78.9:5..1
90.1=22.4
8..2=117
9.
49.41:377
50.4+22.4
51,8:4.22
chewinggum
95.8100.2
98.2
93,5
98.5
85.2:11.5
79.6::1.11
69.6:1119
fryum
83.5:12.44
91.9.11.4
84.0-14
65.2.154
14.955.5
23.0166.6
23.9-2.55
macaronil
73.0:16.0
77.0=1:.4
71.2-20
82442.1
13.146.3
21.24...0
31.5-4000
MetaUAS
macaroni2
60.8::4.2
59.8+3355
68.0=0.7
89.545.77
2.3:1:11
7.4229
56,4=11.7
pebl
13.4
76.0=
75.1=9.6
98.2+:0.6
66.1+5.8
62.9+4.11
71,4=6.3
pch2
76.0#:2.9
76.6#
72.9
94.5
30.8+2.7
39.0
66.4
peh3
77.1:33.
79.8
728
97.0104
42.733
42.9:1999
58.5
pcb4
95.2-12.4
95.0.12.1
89.3-..1
97.1
41.343.2
pipe fryum
9..811.
9.25
93.9
96
66.013.4
62.7+2.7
86.2
mean
81.211.7
1..5
80.2
92.2:10.7
42.7:1..88
44.7:10.6
60.411
candle
84.4
85.4
78.8
98.9
59.8
57.5
55.9
capsules
83.4
91.0
82.3
97.1
48.3
50.6
74.7
cashew
84.3
92.1
85.6
88.8
43.5
45.6
48.8
chewinggum
98.0
93.3
98.6
85.9
80.1
70.4
fryum
92.8
83,4
67.1
13.7
20.6
22.4
macaronil
71.6
74.3
71.1
81.0
4.7
10.4
24.6
MetaUAS
macaroni2
60.3
57.9
67.6
91.0
2.8
9.6
63.1
peb1
86.9
84.8
80.8
98.6
78.8
74.5
63.8
pcb2
79.9
75.0
95.9
34,9
64.5
pcb3
79.7
81.6
73.9
96.4
46.4
46.4
52.5
pcb4
96.1
95.3
91.1
95.4
43.7
46.9
62.8
pipe fryum
95.6
97.8
92.5
95.1
64.8
63.5
82.6
83.4
85.7
92.0
43.9
45.6
57.3
candle
85.8
86.3
79.8
98.3
58.5
57.5
92.9
capsules
91.0
82.3
98.3
51.5
51.8
80.4
cashew
93.5
98.5
chewinggum
95.8
98.3
93.3
99.5
86.0
80.2
85.1
fryum
89.6
94.9
88.2
96.6
38.6
44.5
81.9
macaroni
73.1
76.3
70.8
96.9
7.8
12.5
81.1
MetaUAS*
macaroni2
64.4
67.6
97.7
10.5
89.6
pobl
87.9
86.0
99.3
81.8
75.7
82.4
pch2
80.4
79.1
75.4
97.4
35.1
41.8
77.4
pcb3
80.7
82.3
75.1
96.8
46.7
47.2
85.7
peb4
96.6
95.8
91.6
97.2
43.6
47.1
84.3
pipe fryum
mean
85.1
87.2
82.3
98.0
48.1
48.6
85.5
decoder, and the segmentation head with Adam 25 using weight decay 0.0005 and learning rate 0.0001. We conduct experiments based the open-source framework PyTorch.
follow CYWS (50) and use the same procedure for synthesizing the change segmentation dataset. Specifically, given labeled image from existing instance segmentation dataset, i.c., MS-COCO, we randomly selected one or several instances and then could make disappear from the image by inpainting the mask region [56]. It worth noting that the binary change mask between the inpainted and original images can be freely available because these selected instances have been manually annotated at the pixel level. We keep the dataset setup similar to CYWS [50] possible. Specifically, the change segmentation dataset synthesized using the randomly selected 60,000 images from the MS-COCO training inpainting union mask of a random set of labeled instances. Then, all these 60,000 samples are divided into training and validation sets with ratio of 0.95:0.05. During training, randomly employ object-level change and local-region change with probability of O.5.
For each image, synthesized image is generated by
B Competing Methods.
To demonstrate the superiority of MetaUAS, we compare MetaUAS and its variants (MetaUAS and MetaUAS++) with diverse state-of-the-ant methods. Implementation and reproduction details are summarized as follows:
17

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Table A3: Quantitative results Goods with MetaUAS, MetaUAS+ and MetaUAS++.
Anomaly Classification
Anomaly Segmentation
Methods
Categorics
I-ROC
I-PR
I-F1
P-ROC
P-PR
P-Fl
P-PRO
cigarette box
58.9413.8
63.2:43..6
74.2-10.5
88.41:33
21.14:3.3
28.9=2.9
62.91:3.3
drink bottle
55.1+1122
59.3+
70.6=002
92.4+0.7
7.34-16
12882.0
64.6+00.7
MetaUAS
can
52.11:3.4
66.7.10.0
86.6111.4
7.611.11
14.0-0.8
58.410.5
food bottle
55.0:1:.4
64.4:1:.5
75.0=:011
89.910.3
8.410.6
14.2=0.6
59.4:110
food box
52.9::2.6
65.6:42.55
77.7==0.3
8..4+:177
4.4
8.3+1.11
57.2++2.3
food package
12.7
50.4+188
64.8==0.1
87.
2.8+0.66
5.7
51,6+332
mean
1..5:11.00
58.5:10..4
71.510.11
88.5110.6
8.6.10.7
14.0_0.7
59.011.3
cigarette box
98.9
99.2
96.0
98.7
78.0
73.8
88.0
drink bottle
85.2
86.7
80.9
98.9
62.2
61.3
68.1
drink can
96.7
97.1
91.5
93.8
44.9
53.7
57.9
MetaUAS
food bottle
90.1
93.1
86.2
97.1
50.5
52.1
70.1
food
86.9
92.4
84.5
98.3
54.6
54.8
67.5
food package
81.8
74.8
37.0
73.6
mean
90.1
91.7
85.7
97.4
53.7
55.5
70.8
cigarette box
96.3
96.4
98.6
74.9
74.0
95.3
drink bottle
58.7
61.4
87.6
drink 97.2
97.5
91.8
96.7
42.8
$4.9
86.5
MetaUASS
food bottle
90.4
92.9
86.7
97.5
44.1
52.2
88.6
food box
85.2
87.4
84.1
97.8
46.5
$4.6
85.5
food puckage
83.6
78.1
76.9
97.9
27.0
37.4
84.4
89.9
89.9
86.2
97.9
49.0
55.8
88.0
is a powerful vision-language model, and it has strong zero-shot generalization ability. Following previous works,
CLIP
two classes of text prompt templates, "A photo of normal [cls]" and "A photo an anomalous [cls]", where "cls" denotes the target class name. The anomaly score is computed by cosine similarity between textual features and the class token of query image. For anomaly segmentation, we extend the above computation from class tokens to local patch tokens.
WinCLIP [26 is zero-shot anomaly segmentation method based on CLIP. A large set of hand- crafted textual prompts is designed for anomaly classification. window scaling strategy used to obtain better anomaly segmentation. We keep all parameters the same as in their paper. Note that no official implementation of WinCLIP is available, our results are based on an unofficial implementation!
WinCLIP+ (26 combines the complementary prediction from both language-guided and visual- based for better anomaly classification and segmentation. The language-guided prediction is the same as WinCLIP. For visual-based prediction, first simply stores multi-scale features for given few-shot normal images and retrieves the memory features based on the cosine similarity. The final anomaly score is derived by averaging these scores.
AnomalyCle CLIP 76 learns object-agnostic text prompts that capture generic normality and abnor- mality in image regardless of its foreground objects. But AnomalyCLIP requires fine-tuning an auxiliary domain dataset including normal and anomaly images. AnomalyCL.I is zero-shot anomaly classification and segmentation method, and it is capable recognizing any anomalies. We use the official model to report performance for anomaly classification and segmentation.
UniAD 770 is unified unsupervised anomaly segmentation method for addressing multi-classes anomalies with a single model. Different from most zero-/few-shor anomaly segmentation models, UniAD learns feature reconstruction with transformer-bas based encoder-decods architecture all normal training images. We use the official code to train the specific model for each dataset.
PatchCore 47 is a popular unsupervised anomaly classification method that enjoys training-free. For fair comparison, we modify the official implementation in two folds. First, we replace the original WideResNNtt550 backbone with EfficientNet-b4 Second, the memory-bank construction is limited to only one normal image for each class.
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25


Reviews:
[{"review_id": "XrWXv45zVc", "reviewer": "Reviewer_DDkp", "summary": "This paper considers the anomaly segmentation task as a change segmentation task.  Then the large-scale image pairs with object-level and local region changes are synthesized to train a universal anomaly segmentation framework MetaUAS.  This only needs one normal image as the prompt.  The soft feature alignment module is proposed to handle geometrical variations between\nprompt and query images.  This method achieves state-of-the-art performance on MVTec AD, VisA, and Goods datasets.", "questions": "See the weaknesses.", "limitations": "None.", "rating": 7, "confidence": 5, "soundness": 3, "presentation": 4, "contribution": 4, "strengths": "1. This paper synthesizes large-scale image pairs with object-level and local region changes, and desiges a universal anomaly segmentation framework that only need one normal image as prompt. This idea is novel and has practical implications.\n2. This paper propose the soft feature alignment module to handle geometrical variations between prompt and query images. This is a novel, simple and effective alignment method.\n3. This method is compatible with various feature extractors, and is not limited to CLIP like some previous methods.\n4. The authors conducted experiments on three anomaly detection datasets. Significant improvement has been achieved, which proves the powerful generalization ability of this method.", "weaknesses": "1. The three fused features and two low-level original features are input into decoder. How these features are used in the decoder needs to be explained in more detail.\n2. CLIPSeg [1] also uses one image as the prompt to perform segmentation tasks. It is beneficial to discuss and compare with CLIPSeg in this paper.\n\n[1] Lüddecke T, Ecker A. Image segmentation using text and image prompts[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2022: 7086-7096.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "+ **DDkp-Q1**: How these features are used in the decoder needs to be explained in more detail.\n\nOur method implements the decoder using standard UNet. Furthermore, we also compare the UNet with the FPN decoder in Table 3(c). In our paper, we omit the details of UNet and FPN because they are two popular modules that have been standardized and integrated into the [PyTorch library](https://github.com/qubvel-org/segmentation_models.pytorch/tree/main/segmentation_models_pytorch).  As we know, the decoder of UNet and FPN is widely used in dense visual prediction tasks, such as semantic segmentation and object detection. They use a top-down structure with lateral connections to fuse multi-scale input features and output a single high-resolution feature map. Considering reproducibility, we will make the codes and models of MetaUAS available.\n\n\n+ **DDkp-Q2**: It is beneficial to discuss and compare with CLIPSeg.\n\nThank you for pointing out a related work, CLIPSeg [1]. The CLIPSeg has one-shot semantic segmentation capabilities by providing one support image to the pre-trained CLIP model. However, its better performance still struggles with the powerful CLIP model and textual prompts. Different from the CLIPSeg, our method is compatible with various pre-trained models and does not require any guidance from language, as pointed out by you and Reviewer sm3a. \n\n[1] Image Segmentation Using Text and Image Prompts, CVPR 2022.", "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": "Aj0QFzST8w", "reviewer": "Reviewer_gb5F", "summary": "The proposed method reformulates the one-shot anomaly detection task as a change detection task. The proposed method is trained with a synthesitzed change dataset, where objects are added, deleted or exchanged. Additionally, local changes are generated by pasting out-of-distribution textures on images in random locations. The proposed method is trained in a meta-learning manner where each meta-task is a prompt+query pair where a specific change must be detected and accurately segmented. The trained model is applied to industrial anomaly detection, where a single anomaly-free image is used as the prompt while a potentially anomalous image is used as the query. The model must then segment potential changes between the prompt and query image.", "questions": "- Why are only convolutional networks used? Transformer networks should also work well here? A Transformer backbone in the ablation in Table 3b would be nice.\n- How is the visual prior from CLIP introduced to the framework. In Table 2 the complexity of MetaAS*+ suggests that Eb4 and ViT are both run but this is not well explained.\n- Couldn’t this be extended to the few-shot setup at inference by just adding more prompts and aligning features? If no, why not? If yes, why limit the evaluation to one-shot?", "limitations": "The authors have adequately addressed the limitations.", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The proposed method is interesting and it deviates significantly from the top performing few-shot anomaly detection methods since most recent methods are based on CLIP.\n- The ablation study is mostly well done and individual components seem to be evaluated properly.\n- The proposed method achieves good results\n- The change detection dataset generation is an interesting approach to creating a dataset and it is interesting that the method generalizes well to industrial inspection datasets even though the training dataset is from a different domain.", "weaknesses": "- Clarity issues in the paper (Exactly how prompt pooling is performed in Section 3.4.)\n- The way CLIP is added to the architecture in MetaAS*+ is not clear. \n- Figure 4 - First column and the rest of the figure should be separated better. Currently it seems like the entire row depicts GT masks or queries.\n- The evaluation of backbones is limited to convolutional models. The only transformer is used with the MetaAS*+ model.\n- MetaAS*+ uses CLIP and achieves the best performance. Why this helps with the one-shot anomaly detection task is not intuitive and a further discussion would be beneficial for the reader.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "+ **gb5F-Q1**: A Transformer backbone in the ablation in Table 3b would be nice.\n\nThanks for your suggestion. As pointed out by DDkp and sm3a, our method is compatible with various pre-trained models, including Convolutional and Transformer architecture networks. Considering the efficiency, we employ EfficientNet-b4 (E-b4) as our encoder, which is the same as in the previous UniAD.\n\nFollowing your suggestion, we replace the convolution-based EfficientNet-b4 with the recent EfficientViT [1]. Specifically, three EfficientViTs with different capacities (b1, b2 and b3) are used as our encoders, and the corresponding anomaly detection results on MVTec are reported in the following table. We can see that their performance is still lower than that of EfficientNet-b4 in most metrics. Other more powerful and efficient backbones deserve further exploration. In the revised version, we will add these results.\n\n|Backbone\t|Total (M)\t| Learnable (M) |I-ROC |I-PR |P-ROC |P-PR |P-PRO|\n| :---  |    :----:   |  :----:  |  :----: |  :----: |  :----: |  :----: |  :----:  |\n|E-b4 \t|22.1\t|4.6\t|**91.3** |**96.2** |94.6 |**59.6** |**82.6** |\n|EVit-b1 |8.0\t|3.4\t|89.1 |94.7 |94.1 |55.8 |80.6 |\n|EVit-b2 |19.5\t|4.5\t|88.5 |94.9 |93.0 |56.3 |75.1 |\n|EVit-b3 |44.7\t|5.7\t|89.5 |95.7 |**95.3** |58.5 |80.9 |\n\n\n\n+ **gb5F-Q2**: How the CLIP's visual prior is introduced into the framework.\n\nWe apologize for the confusion about MetaUAS*+. In fact, we have given more details of diverse state-of-the-art methods, our MetaUAS and its variants (MetaUAS* and MetaUAS*+) in the Sec. B of Supplementary Materials. Here, we give some explanations about MetaUAS, MetaUAS\\* and MetaUAS*+, and hope to address your concerns. \n\nThe MetaUAS is capable of segmenting any anomalies only just by giving one normal image prompt. Note that the normal image prompt is randomly selected from a specific normal training set in MetaUAS. Different from MetaUAS, the MetaUAS* searches the best-matched normal image as a vision prompt from the normal training set for each query image. The MetaUAS*+ builds on MetaUAS*, but introduces the visual prior from a CLIP model. The visual prior knowledge is obtained by computing the cosine similarity between the query feature and the corresponding prompt feature extracted from the CLIP vision encoder, which is the same as WinCLIP+ [2] and PromptAD [3].\n\n+ **gb5F-Q3**: Whether the evaluation can be extended from one-shot to few-shot?\n\nOur method can be flexibly extended to a few-shot setting, where a few normal image prompts are provided. However, we seek to push the limits of simple but effective methods for more general and challenging settings of anomaly segmentation. To this end, we explore universal anomaly segmentation from the perspective of a framework paradigm, where synthesized images are considered for one-prompt meta-learning, geometrical variations between prompt and query images can be effectively handled, and the inference is more efficient and simple without any guidance from language or fine-tuning on target datasets.\n\nHere, we present a straightforward manner to extend our approach from one-shot to few-shot. We use an average of all shots’ predictions as the final anomaly segmentation map. In the following table, we can observe a significant performance improvement from 1-shot to 3-shot. However, the performance tends to stabilize when reaching 5-shot. We leave it to future work on how to more elegantly extend MetaUAS from one-shot to few-shot.\n\n|Shot\t    |I-ROC |I-PR |P-ROC |P-PR |P-PRO|\n| :---  |    :----:   |  :----:  |  :----: |  :----: |  :----: | \n|1\t\t\t|91.3 |96.2 |94.6 |59.6 |82.6|\n|3\t\t\t|92.7 |96.9 |95.6 |63.4 |85.5|\n|5\t\t\t|93.0 |97.1 |95.9 |63.9 |86.1|  \n\n+ **gb5F-Q4**: How prompt pooling is performed?\n\nWe obtain a feature representation by global average pooling on the highest-level feature from the encoder. For a class-agnostic query image, we match the corresponding normal image prompt using the cosine similarity between the query feature representation and all offline prompt feature representations.  The above details have been given in Lines 265-270. \n\n\n+ **gb5F-Q6**: A further discussion of MetaUAS*+.\n\nThanks for your suggestion. The MetaUAS*+ can be seen as an ensemble of MetaUAS* and the visual prior knowledge of CLIP. Therefore, the performance improvement is intuitive from an ensemble learning perspective. We will add the discussion to the revised version.\n\n\n[1] EfficientViT: Lightweight Multi-Scale Attention for High-Resolution Dense Prediction, ICCV 2023.\n\n[2] WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentation, CVPR 2023.\n\n[3] PromptAD: Learning Prompts with only Normal Samples for Few-Shot Anomaly Detection, CVPR 2024.", "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": "6jztLBvfSw", "reviewer": "Reviewer_sm3a", "summary": "The authors introduce a method for anomaly segmentation that relies solely on visual information and does not require any anomaly training data or language guidance. Their method is based on change segmentation, which allows for the synthesis of large-scale image pairs for training the anomaly segmentation model. The proposed MetaUAS framework demonstrates its generalizability across three industrial anomaly segmentation datasets: MVTec, VisA, and Goods, using one normal image per class as a prompt. Within their framework, the authors extensively experimented with different modules, including an image encoder, feature alignment, and a segmentation decoder. Notably, their feature alignment approach, which is based on a weighted combination of image prompt features, proves to be highly effective for anomaly segmentation by bringing together features of semantic segmentation and change segmentation.", "questions": "- Some more details on the training process would be appreciated. For instance, there is no information on how much training data was required to achieve the presented performance or how robust the model training was. The training data seems to be created in an automated fashion; to what extent does the amount of training data affect the anomaly segmentation performance?\n- One could use more than one image as a prompt to potentially obtain more robust anomaly scores. How efficient is the inference? In particular, compared to other existing approaches, could one afford a larger prompt pool or multiple forward passes of MetaUAS?\n- Relying on the changes in the visual appearance of objects seems to be a valid approach for anomaly segmentation in industrial settings. The same objects often appear in the same scene, and the anomalies are clear, small visual changes. Therefore, guidance with language seems unnecessary, as the semantic information is not crucial for this kind of anomaly segmentation. However, for other datasets on anomaly segmentation, semantic information seems crucial, such as the [SMIYC](https://segmentmeifyoucan.com/) benchmark, where any obstacle on the road is considered an anomaly. Could MetaUAS also be applied to such more challenging tasks where the normal \"object\" itself (e.g., the road) could have varying visual appearances as well?", "limitations": "The authors have discussed limitations. Potential negative social impact have not been mentioned.", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "- A novel approach is presented by formulating anomaly segmentation as change segmentation.\n- Extensive experiments on multiple datasets and in different model configurations demonstrate the effectiveness of the presented method, MetaUAS. In particular, the anomaly segmentation performance benefits from the specifically designed soft alignment module.\n- It is shown how efficiently a change segmentation dataset can be created with synthetic data, which is then used for training anomaly segmentation.\n- MetaUAS, trained on synthetic data, generalizes well to real-world problems. Notably, the method does not require any anomalous data for training but still outperforms existing methods that use such auxiliary data.\n- Unlike other existing methods, MetaUAS does not require guidance from language and solely relies on visual information.", "weaknesses": "- At least one normal image per class is always needed as a prompt to perform anomaly segmentation. All prompts must be processed by the encoder to extract their features, which could become expensive as the number of classes increases.\n- The prompt features can be stored offline as a prompt pool to reduce forward passes at test time when using MetaUAS. However, it is unclear whether a single example can represent all normal examples for a class effectively (Figure 4 highlights the issue of different prompts). Searching for the best matching normal image for each query image is obviously resource-intensive.\n- The anomalies shown in the industrial datasets relate to small changes in the appearance of the objects. The objects are also always displayed in the same scene. It is noted that the orientation of the object affects the anomaly segmentation performance. To further investigate the limitations, it would be interesting to see what would happen if the scenes changed more dramatically, which is realistic in open-world settings. For example, would MetaUAS be robust if the same normal object were shown with a different background?\n- Minor: The paper could benefit from better writing and structure. There are a few typos throughout the text, and the figures and tables could be placed differently. It is not always easy to follow.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "+ **sm3a-Q1**: The effects of the number of training data for the anomaly segmentation performance.\n\nThanks for your interest in the effects of training scale. We are also very concerned about this issue, which has been analyzed in our ablation studies (Sec. 4.3). The corresponding experimental results are reported in Table 3(d), with the related analysis provided in Lines 361-366. The number of synthesized images and the split of training and validation are described in Sec. A of Supplementary Materials. Here, we provide further explanations.\n\nTo investigate the influence of training scale on model performance, we conduct experiments with different training subsets where each one is generated by randomly sampling the original training set at various rates, such as {10%, 30%, 50%, 70%, 95%}. In Table 3(d), it can be seen that MetaUAS still works when the number of training images is small scale (e.g., 50%), and the performance can further improve when increasing the number of training samples. Unless otherwise specified, the default number of training images is 95% of the synthesized dataset.\n\n+ **sm3a-Q2**: Compared to other existing approaches, could one afford a larger prompt pool or multiple forward passes of MetaUAS?\n\nWe agree with you that using more normal image prompts can potentially enhance the robustness of the model. Additionally, we have observed that the performance gains tend to saturate when the number of normal image prompts increases to a certain level (e.g., 5) (see the response to gb5F-Q3). Assuming that each query image is provided with 5 normal image prompts, this means that the inference cost could increase by up to 5 times. However, considering that we use a lightweight model, even when 32 prompt-query pairs are processed in parallel during forward inference, the required time of each pair is only 3.2ms, as reported in Table 2. Therefore, our method can efficiently handle larger prompt pools compared to existing CLIP-based models.\n\n+ **sm3a-Q3**: Could MetaUAS also be applied to such tasks where the normal ``object'' itself (e.g., the road, SMIYC) could have varying visual appearances as well?\n\nOur method is primarily towards open-world industrial anomaly detection, which has become an attractive yet challenging topic. It may not be suitable to directly apply our method in these scenarios where the normal ``object'' itself or its context exhibits large variations in appearance. This is because the core of our method relies on detecting changes to identify anomalies.\n\nFurthermore, we have carefully reviewed the suggested RoadAnomaly21, which contains only 100 testing images and does not provide normal images. In this dataset, the context changes of the road are more significant than that of the road itself. We believe that one feasible manner could be to first preprocess these images to remove backgrounds (i.e., the context of the road) and then apply our method to detect anomalies on the road surface.\n\n+ **sm3a-Q4**: All prompts must be processed by the encoder to extract their features, which could become expensive as the number of classes increases.\n\nWe apologize for any confusion this issue may have caused. Here, we provide some clarification that might address your concerns. For a class-specific query image, we can process prompt-query pairs online to perform anomaly segmentation without extracting offline features for normal image prompts. For a class-agnostic query image, we need to first extract offline features from a prompt pool, and then match the corresponding image prompt for the given query image to perform anomaly segmentation. In fact, in real-world industrial production lines, products are generally produced on a large scale according to specific models, so we could register normal image prompts according to actual needs. In addition, existing language-prompt-based anomaly detection methods (e.g., WinCLIP) also face similar issues, as they require offline computation of normal or anomaly textual features for all objects or textures.\n\n\n+ **sm3a-Q5**: It is unclear whether a single example can represent all normal examples for a class effectively. Searching for the best matching normal image for each query image is resource-intensive.\n\nAs shown in Table 1, our method requires only one normal image prompt to achieve competitive performance when faced with an open-world anomaly detection scenario. This indirectly supports that one normal image can essentially represent the normal pattern for a specific category. Undoubtedly, increasing the number of normal image prompts can further enhance performance, but this involves additional computational costs. \n\nIn our MetaUAS*, we have demonstrated that the optimal normal prompt yields better performance compared to a random image prompt, although the matching process does indeed bring computational cost. Exploring other efficient algorithms to obtain a better prompt is also worthwhile, such as using image-matching to pre-align the prompt image with the query image.\n\n\n+ **sm3a-Q6**: There are a few typos throughout the text, and the figures and tables could be placed differently.\n\nThank you very much for pointing out the few typos. We will carefully review our manuscript and correct these typos in the revised version.", "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": "4OL00Uiy5n", "reviewer": "Reviewer_bbUz", "summary": "This work presents a novel approach for universal anomaly segmentation (AS) by framing AS as Change segmentation. This change of perspective motivates them to create a synthetic training set based on change detection (e.g. by stitching additional objects from a different dataset). With this synthetic dataset they train a change segmentation model which they later transfer directly for anomaly segmentation without fine-tuning.", "questions": "Can the authors explain what are the differences between MetaUAS/*/*+ ?\n\nWhy is UniAD excluded from the comparison in Table1?\n\nCould all change segmentation methods be applied to anomaly segmentation \"off-the-shelf\"? If yes, why not include some existing methods as baseline? If no, please explain what are the particular differences that make MetaUAS applicable to AS while other change segmentation methods can not.", "limitations": "Limitations and social impact are well addressed.", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The idea of framing Anomaly Segmentation as Change Segmentation is interesting and worth exploring.\nThe results are encouraging, especially in the Gooods dataset.", "weaknesses": "The paper is confusing at times, with some important concepts not clearly defined:\n    - For instance, the different method variants i.e. MetaUAS/MetaUAS*/MetaUAS*+ should be explained more thoroughly in the methodology section. \n    - Also there should be consistency in the naming (MetaUAS vs MetaAS). \n    - The highlighted numbers (red and blue) in the tables are excluding UniAD in Table 1 (which is marked in gray) and I could not find out why.\n\nMissing comparison with change segmentation methods. The paper proposes that AS can be tackled as change segmentation between one normal (reference) image and the query image with the anomaly, which sounds reasonable. However, I would expect then that existing Change Segmentation methods could be applied to AS (just like authors do with their method). Thus, it would be sensible to include a comparison with one or two baselines of recent change segmentation methods applied to AS the same way that MetaUAS is applied.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "+ **bbUz-Q1**: What are the differences between MetaUAS, MetaUAS*, and MetaUAS*+?\n\nWe apologize for the confusion about MetaUAS, MetaUAS*, and MetaUAS*+. In fact, we have given some details of our MetaUAS and its two variants (MetaUAS* and MetaUAS*+) in the Sec. B of Supplementary Materials. Here, we provide some explanations, which might address your concerns.\n\nThe MetaUAS is capable of segmenting any anomalies only given just one normal image prompt. Note that the normal image prompt is randomly selected for each testing class in MetaUAS. Different from MetaUAS, the MetaUAS* searches the best-matched normal image as a prompt from the normal training set for each query image. The MetaUAS*+ builds on MetaUAS*, but introduces the visual prior from a CLIP model. The visual prior knowledge is obtained by computing the cosine similarity between the query feature and the corresponding prompt feature extracted from the vision encoder of the CLIP model, which is the same as WinCLIP+ and PromptAD. In addition, the MetaAS is a typo and the correct one should be MetaUAS. We will add these clarifications to the methodology section.\n\n+ **bbUz-Q2**: Why UniAD is excluded from the comparison in Table 1?\n\nUniAD is a powerful unsupervised anomaly detection method, which trains a unified reconstruction model to detect multi-class anomalies using full-shot normal images from the target domain. In contrast to our training-free one-shot method, UniAD requires re-training the model using full-shot normal images. As reported in Table 1, we observe that our one-shot training-free method still exhibits strong performance compared to the full-shot UniAD. We will add a description for UniAD in the caption of Tabel 1.\n\n\n+ **bbUz-Q3**: Could all change segmentation methods be applied to anomaly segmentation ``off-the-shelf''?\n\nIt is challenging to apply existing change segmentation models to anomaly segmentation. Current change segmentation models are primarily focused on two scenarios: remote sensing and street scenes. The main characteristics of these scenarios and models can be summarized in four aspects. \n\nFirst, the scale of training images in these scenarios is usually small, typically hundreds or thousands of images. \nSecond, the diversity of images is limited because they mainly consist of remote sensing images or street scene images. \nThird, these models primarily focus on semantic changes (such as buildings) while the background may undergo other changes due to factors like seasons and weather. \nFourth, the prompt and query images are generally coarsely aligned in the change segmentation, which is different from anomaly detection scenarios where there are often large geometric variations.\n\nThese characteristics limit the development of a general change detection model. Additionally, we evaluated the [off-the-shelf ChangerEx with s50 backbone](https://github.com/likyoo/open-cd/tree/main/configs/changer) on MVTec, and the corresponding results are reported in the following table. We can see that its generalization performance is poor compared to our method.\n\n\n|Methods  |Backbone  |Training Data   |I-ROC  |I-PR  |P-ROC  |P-PR  |P-PRO|\n| :---  |    :----:   |  :----:  |  :----: |  :----: |  :----: |  :----: |  :----:  |\n|ChangerEx |S50  |LEVIR-CD          |59.8  |74.1   |65.5  |3.0    |0.8   | \n|ChangerEx |S50  |S2Looking\t        |63.4  |79.6   |68.3  |10.5  |13.1 |\n|Ours\t   |E-b4 |Synthesize\t        |91.3  |96.2   |94.6  |59.6  |82.6 |", "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:
We thank all reviewers (DDkp, gb5F, sm3a and bbUz) for your insightful comments. The reviewers believe that the proposed universal anomaly segmentation framework is **novel and interesting** (DDkp, gb5F and sm3a), **simple, effective and efficient** (DDkp and sm3a) and **compatible** (DDkp), the ablation study is **well** (gb5F and sm3a), our method achieves **good and encouraging results, or powerful generalization ability** (DDkp, gb5F, sm3a and bbUz). 

Next, we respond to the concerns of each reviewer one by one.

Meta-review: This paper proposes to frame anomaly segmentation as a change segmentation task. It proposes a meta-learning approach, trained on a change segentation dataset where image pair changes are artificially created. All Reviewers provided positive feedback for this paper, praising the framing of the problem and the results achieved by the proposed method. Given the unanymous positive feedback received by this paper, I recommend acceptance.
Decision: Accept (poster)