DualCnst: Enhancing Zero-Shot Out-of-Distribution Detection via Text-Image Consistency in Vision-Language Models
Authors: Fayi Le
Wenwu He
Chentao Cao
Dong Liang
Zhuo-Xu Cui
Venue: NeurIPS 2025 poster ()
Keywords: Out-of-distribution Detection
Primary area: general_machine_learning

Abstract: Pretrained vision-language models (VLMs), such as CLIP, have shown promising zero-shot out-of-distribution (OOD) detection capabilities by leveraging semantic similarities between input images and textual labels. However, most existing approaches focus solely on expanding the label space in the text domain, ignoring complementary visual cues that can further enhance discriminative power. In this paper, we introduce DualCnst, a novel framework that integrates text-image dual consistency for improved zero-shot OOD detection. Specifically, we generate synthetic images from both ID and mined OOD textual labels using a text-to-image generative model, and jointly evaluate each test image based on (i) its semantic similarity to class labels and (ii) its visual similarity to the synthesized images. The resulting unified score function effectively combines multimodal information without requiring access to in-distribution images or additional training.
We further provide theoretical analysis showing that incorporating multimodal negative labels reduces score variance and improves OOD separability. Extensive experiments across diverse OOD benchmarks demonstrate that DualCnst achieves state-of-the-art performance while remaining scalable, data-agnostic, and fully compatible with prior text-only VLM-based methods.

DualCnst: Enhancing Zero-Shot Out-of-Distributio Detection via Text-Image Consistency in Vision-Language Models
Fayi Le!
Wenwu 22t
Chentao Cao
Dong Liang*t
Zhuo-Xu Cui
'School of Computer Science and Mathematics, Fujian University of Technology
Fujian Provincial Key Laboratory of Big Data Mining and Applications
Department of Computer Science, Hong Kong Baptist University
4 Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences fayi Mayi@smail..m jut edu. hwwhbb0163. com, cat.raacccoo224@gmaaia {dong liang, zx. wwwcccuiaaiiin cn
Abstract
Pretrained vision-language models (VLMs), such as CLIP, have shown promising zero-shot out-of-distribution (OOD) detection capabilities by leveraging semantic similarities between input images and textual labels. However, most existing approaches focus solely on expanding the label space in the text domain, ignoring complementary visual cues that can further enhance discriminative power. In this paper, we introduce DualCnst, a novel framework that integrates text-image dual consistency for improved zero-shot OOD detection. Specifically, we generate synthetic images from both ID and mined OOD textual labels using text-to-image generative model, and jointly evaluate each test image based on (i) its semantic similarity to class labels and (ii) its visual similarity to the synthesized images. The resulting unified score function effectively combines multimodal information without requiring access to in-distribution images or additional training. We further provide theoretical analysis showing that incorporating multimodal negative labels reduces score variance and improves OOD separability. Extensive experiments across diverse OOD benchmarks demonstrate that DualCnst achieves state-of-the- art performance while remaining scalable, data-agnostic, and fully compatible with prior text-only VLM- methods. The code is publicly available at: https: //github. com/ wwt./ow..///.eeeee. /T///ITSSAAAAAAAAAAA
Introduction
Out-of-distribution (OOD) detection aims to identify whether an input sample lies outside the training data distribution of machine learning model during inference [1]. This capability is critical for
preventing erroneous predictions when models encounter novel anomalous inputs. It is especially vital in high-stakes applications such as medical imaging [2, 3, 4] and autonomous driving [5, 6, 7] where failures in OOD detection may lead to severe consequences like misdiagnoses safety hazards.
Traditional OOD detection methods primarily rely on representations learned from the image modality. While effective some extent, such approaches often neglect the rich semantic structure embedded in textual descriptions. The emergence of large-scale vision-language models (VLMs), such as CLIP [8),
Corresponding authors: Wenwu He (hwwhbb@163.com com), Dong Liang (dong, liang@siatt ac cn), Zhuo- Xu Cui (zx. cui@siat..... cn)
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Haigh similarity) [0.28]
[wild "zebra"
y
Label Space
31
Demiiyy
Actual Image
flo similarity -[0.14]
sd
X
Synthetic Image
FPR95: 67.77%, AUROC: 81.1%
Example: Measurement of semantic similarity between image-Iext pairs and visual similarity between image pairs
(b) Text Only: Utilizing only the
Hased On Given Space
)
Stable Diifasion XI-Turbo
Denilv
X id
Deadrr
A id
0.33%, AUROC:
PRUS:: AUROC: 99.25%
(c) Visual Ground Truth: Using visual features images (not accessible)
(d) Ours: Incorporating visual features synthesized from the label set
Synthetic ID Images
Figure 1: Motivating the use of visual similarity in OOD detection. (a) Semantically similar but visually distinct classes (c.g., wild horses and zebras) present major challenge for text-only OOD detection. (b) Using only semantic similarity (c.g., CLIP text embeddings) results in significant overlap between ID and OOD score distributions. (c) Incorporating visual features from real ID images helps distinguish these ambiguous OOD samples by better capturing visual structure. (d) Even without access real ID images, synthesizing class conditionl image exemplars via text-to-image models enables similar gains, narrowing the gap between ID and OOD scores. CIFAR-100 [10] is used the ID dataset, and iNaturalist [11] the OOD dataset.
has enabled the alignment of visual and textual modalities in shared embedding space, thereby facilitating zero-shot OOD detection by measuring the similarity between test images and class names in natural language. For example, methods like MCM [9] perform inference by computing cosine similarity between test image embeddings and textual label embeddings, classifying inputs with low similarity scores OOD without requiring any additional training.
However, relying solely on semantic similarity has inherent limitations. Challenging OOD ex- amplls-parrte particulally those lying near the decision boundary- share semantic attributes with in-distribution (ID) classes, thereby causing significant overlap between IDD and OOD score distribu- tions (Figure 1(b)). Interestingly, such samples often remain visually distinguishable despite semantic ambiguity. For example, "zebra" and "wild horse" share strong semantic overlap but are visually distinct due to the zebra's unique striping (I)).. This observation motivates key question:
Can visual similarity between test samples and ID/OOD exemplars improve the separability of semantically similar but visually distinct samples?
We empirically confirm this hypothesis in Figure 1(c), where access actual ID images allows more effective separation of ambiguous OOD cases. Unfortunately, real ID images are typically unavailable open-world settings due to privacy, licensing, or infrastructure constraints. To overcome this limitation, we propose synthesizing visual exemplars using text-to-image diffusion models. As shown in Figure (d)) these generated samples can approximate the visual structure of class concepts, enabling reliable similarity computation even without access real ID data.
Building on this idea, we propose DualCnst, a novel zero-shot OOD detection framework that integrates semantic and visual consistency across the label space. Specifically, we synthesize images for both ID and mined OOD labels and define a scoring function that jointly considers: (i) semantic similarity between a test image and textual labels, and (ii) visual similarity between the test image and synthesized images. DualCnst offers several key advantages: (1) Training-free: It does not require fine-tuning any labeled ID data. (2) Data-agnostic: It generalizes across domains without assuming access to ID images. (3) Scalable: Its modular similarity function can be plugged into existing VLM-based pipelines with minimal overhead.

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ID labels
photo
T(Ixx+)
Encoder
OOD labels
(t.x+m3
Stable Diffusion
DualCnntt
I(x)
Image
Encoder
I(R1xtM)
Synthesized Images (Kurss))
(x)
(x))
Encoder Layer
Encoder Layer
(x)(
(m)))))
1 (Reinc
(x)
(x))
(x))
Test Sample
The framework of the proposed DualCnst is outlined as follows. Given a set of ID class labels ydd,, we first leverage NegLabel [12]) generate OOD labels yood These class labels are then input into Stable Diffusion [13] to synthesize both ID and OOD images. Subsequently, both the ID/OOD class labels and the synthesized images fed into the text and image encoders construct the textual and image classifiers. During the testing phase, given an input image, its visual features are extracted using the image encoder, and the semantic similarity with the class labels computed, along with the visual similarity to the synthesized images. Finally, the OOD score is derived by scaling and coupling these similarities using the proposed detection score function DualCnstt
Figure 2:
Our contributions can be summarized as follows:
We propose DualCnst, a dual-consistency framework that fuses semantic and visual similarities for zero-shot OOD detection (Section 3).
We introduce text-driven synthetic image generation pipeline to build visual exemplars for both ID and OOD labels without accessing in-distribution samples (Section 3).
We provide theoretical analysis showing that multimodal negative labels reduce similarity score variance, enhancing ID/OOD separability (Section 4).
Extensive experiments on standard OOD benchmarks show that DualCnst consistently outperforms previous zero-shot approaches. In particular, it achieves absolute gains of 3.95%, 3.9%, 9.9% in FPR95 ImageNet- K far-OOD, near-OOD, and robust-OOD tasks, respectively (Section 5).
Preliminaries
CLIP and Zero-shot OOD Detection: CLIP [8] is multimodal pre-trained model designed to align visual and textual modalities within shared embedding space. Trained on large-scale image- text datasets using contrastive learning, CLIP consists of image encoder and text encoder that generate embeddings for images and text, respectively. By computing cosine similarity between these embeddings, the model performs similarity- zero-shot capability: trained on diverse and extensive image-text pairs, it can directly applied to various vision tasks-includin image classification [14, 15, 16, 17]. object detection [18, 19, 20], semantic segmentation [21, 22, 23], and OOD detection without requiring additional labeled data fine-tuning.
matching. A key strength of CLIP is its remarkable
For zero-shot OOD detection, CLIP determines whether an input image belongs to one of the known categories represents an OOD sample. This is achieved by comparing the image's 's visual features

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with the semantic representations of known class labels encoded as text. Images with low similarity to all known labels are identified as OOD samples. This zero-shot paradigm offers high flexibility, allowing CLIIP to generalize across diverse domains without retraining, making it powerful tool for OOD detection in real-world applications.
Stable Diffusson: Stable Diffusion is generative model based on Latent Diffusion Models (LDMs) [13], designed for efficient text-to-image synthesis. Unlike conventional diffusion models that operate in pixel space, Stable Diffusion performs the diffusion process in a lower-dimensional latent space, significantly enhancing computational efficiency and scalability. The model employs a pre-trained Variational Autoencoder (VAE) [24] to encode high-resolution images into a compact latent representation. which serves as the imput for the diffusion process. Within this latent space, a U-Net-based [25] denoising network executes both forward and reverse diffusion: in the forward process, noise is gradually added to the latent representation until it converges a Gaussian distribu- tion, while in the reverse process, the model learns to iteratively denoise the latent representation, reconstructing into the original data distribution.
To enhance the fidelity and semantic alignment of generated images, Stable Diffusion incorporates CLIP as guidance mechanism during the reverse diffusion process [26]. CLIP provides similarity- based gradient signal that directs the latent representation toward alignment with the textual prompt, ensuring that the generated images faithfully capture both the semantic intent and fine-grained details. This method builds on previous CLIP- CLIP-guided generative models [27, 28], which utilize multimodal representations to improve the coherence and expressiveness of generated content [29]. By leveraging CLIP's semantic understanding, Stable Diffusion generates visually coherent and contextually relevant images, even for abstract or complex prompts. This significantly broadens the model's 's applicability in text-to-image synthesis [28].
3 Text-Image Dual Consistency-Guided OOD Detection
In this paper, novel approach is proposed to enhance zero-shot OOD detection performance by leveraging text-image dual consistency. Specifically, the method divided into two stages: (i) Synthesis Stage: To evaluate the visual similarity of test samples with ID and OOD images, text-to-image generative model, Stable Diffusion, is employed to synthesize image labels from the combined label space, )rd U yood (ii) Testing Stage: To integrate textual and visual information, a novel score function is proposed. This function simultaneously evaluates the semantic similarity between test images and textual labels and measures the visual similarity between test samples and the synthesized ID/OOD image labels. The overall framework of the proposed method is illustrated in Figure 2.
3.1 Synthesize Images from the Label Space
To broaden the scope of visual information, NegLabel [12] is employed to identify potential OOD labels, which serve as prompts for an image generator. These prompts guide the generation of semantically consistent visual representations for OOD images. The label space is defined as yid U yood 1/, 12........ УKK #I+1,....... VK+M, where K denotes the number of ID labels and M denotes the number of OOD labels.
To ensure semantic alignment between textual descriptions and generated images, the diffusion model's capacity for aligning textual and visual representations is utilized. For each label, a consistent text prompt, such as "A photo of a <label>," " constructed. These prompts are input into the diffusion model to generate synthetic images semantically aligned with the combined label space y U yood This process enriches visual information and addresses the limitations of relying solely on semantic information for image-text alignment.
The generated images are represented as & (x,), where each X; corresponds unique synthetic image associated with a specific label. These images not only capture the known ID data distributions but also simulate visual representations of OOD categories. By integrating this diverse set of synthetic images into the OOD detection process, the proposed method enhances the model's ability to differentiate ID from OOD instances. This is achieved by leveraging visual distinctions between ID and OOD images, leading to more accurate identification and rejection of OOD samples.

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3.2 Integrate Textual and Visual Metrics for OOD Detection
We calculate the visual similarity between the test sample x and the synthesized image set P, as well as the semantic similarity with the label set y, in the feature space encoded by CLIP's text encoder T-) and image encoder I(-).
Image-to-Image Similarity. In particular, both low-level and high-level visual features are incorpo- rated. We extract features from intermediate layers and the final output layer of the image encoder to calculate cosine similarity between the test sample and the synthetic images at multiple levels of representation. Distinct weights are assigned to each layer to balance their contributions. For instance, using ViT-B/16 as the visual encoder, we select the third, sixth, ninth, and final semantic layers compute cosine similarity between the test image and each synthetic image. A weight of 0.25 is assigned to the similarity score from each layer, and the overall visual similarity is calculated as the weighted sum of these scores.
The visual similarity between the input image x and the synthesized image set defined as:
(x) = (x) (x)l [I+
(x.)
x; E X.
kimg
where ZZ() (x) represents the feature embedding at layer L. The final similarity score ssning (x) is obtained by summing the weighted similarity scores across all layers:
Si.ing (x) Σ
Samig (x),
(2)
1=1
where represents the weight assigned to layer and is defined as:
<< L
Wy r.-r-(L. (L 1), l=L²
where L denotes the total number of layers in the visual encoder, and is the weight factor applied to intermediate layers, ensuring a balanced contribution across all layers.
Image-to-Tee Text Similarity. The semantic similarity between the test image x and the combined label space yrá U yood is computed as:
I(x)- T(t,))
8 (x) [I(x)] [I(
where t; = prompt < y; > and y E yid U and t; represents the textual description of the label y:. using a prompt format such as "A photo of <label>."
Fusion Similarity Scores. To fully utilize both image-to-image and image-to-text similarity information, we compute fused similarity score using a weighted sum-softmax method:
K
exp(x;(x))
SDuaCCun (x)
(4)
K+M jj=1 ex)(
where the fused similarity score (x) is defined as:
3 (x) St.img (x) + (1-a) (x),
(5)
where a is fusion hyperparameter that balances the contributions of image-to-image and image-to- text similarities. Details the choice of provided in Appendix G.6.
OOD Detection Framework. Based on 5DualCna(()). the OOD detector Gx( yid Siruu U yood, T,I) is
defined as a binary classification function:
ID OOOD
SpantCra (x)
Gx yid U youd X,T.I)
(6)
SpantCnst (x)
where À is threshold selected such that high fraction of ID samples (typically 95%) exceed this value. See Algorithm for the complete zero-shot OOD detection procedure.
5

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Theoretical Analysis of Multimodal Negative Label Aggregation for OOD Separability
We provide a simplified probabilistic analysis to illustrate how incorporating multimodal negative labels reduces the variance of similarity scores and improves ID-OOD separability. The key idea is to enrich the negative label space by augmenting existing textual labels with corresponding synthesized image labels across multiple feature levels. To better understand how such multimodal expansion improves OOD detection, we frame the problem from the perspective of multi-label classification and derive a theoretical formulation of detection performance.
Let the multimodal negative label set be defined as Y; (Vi11, -.2:-... ViNN yood where Ui,1 denotes the primary-modality negative label (e.g., text), and yi. Vi.N represent auxiliary- modality labels (e-g. synthetic images encoded through image encoder I at different layers: Øk.- = (x)1)(xx)) We compute the similarity between the test image x and each negative label Mi.y denoted by St.j- Specifically, $1, (x) corresponds the text- ext--basd similarity, and $t.j s (x)
for corresponds to image-basee based similarities (sce Eq. (1)).
To aggregate these scores across modalities, non-uniform weighting scheme applied as defined in Eqs. (2) and (5). Without loss of generality, we assign a fraction a/N (where N > a >> 1) to the primary modality and distribute the remaining weight evenly among the auxiliary modalities:
j=1
(primary modality weight)
W
j 2 (auxiliary modality weights)
N-1
The aggregated similarity score s; is then computed weighted sum:
N
8 = N
S1,1
si.j
N
Assuming that similarity scores st are independently and identically distributed (i.i.d) with expectation [[s y and variance Var(st,g) = o², derive:
N(N - 1) [ = μ4 Var(s;) = N(NN) where N'(N) = a² + N - 2a
2²
By the Central Limit Theorem (CLT), for sufficiently large N, the distribution of s; can be approxi- mated by
8; ~~N S (
This results in the probability expression:
p(N') P(s, 2 4)) =1 ④ (
VN(M)
where (b.) denotes the cumulative distribution function (CDF) of the standard normal distribution. The binary indicator of is then defined as L[ss 2 {)] which serves as the basis for the OOD detection score. The negative label match count then defined as c ΣM sj, representing the OOD score, which follows binomial distribution B(M, P(N)). Specifically, the match counts for ID and OOD samples are given by Gi BMM, Pı (N')) and Cour B(M, (2(")) where Pı (N') and P2 (N') represent the distributions of the aggregated similarity scores 8i for ID and OOD samples, respectively:
Pi (NV') =1 1/
(1--- V
P2(N')
112
N'
where (i) 0) and (122 .) denote the mean and standard deviation of similarity scores for ID and OOD samples, respectively. Since OOD samples typically exhibit higher similarity with OOD labels than ID samples, it follows that #22 > nu1 Consequently, the fundamental inequality Pa > Pı holds due to the monotonicity of the standard normal CDF (--).

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For sufficiently large M, the CLT allows for the normal approximations
Gi (M
-P)),, Cout (A(pp2 Mp2(1 p2)).
To evaluate the separability between ID and OOD samples, the false positive rate FPR, is introduced performance metric of the OOD detector:
FPR = Fout (Fim (2))
where Fin and Faur are the CDFs of Gn and Cou: respectively, and À E [0. 1] represents the true positive rate (TPR), quantifying the proportion of correctly elassified IDD samples. This yields the expression:
FPRA = MMi(N') + (Mp((N'( - Pi (N') $ -0-(() - M
MMp2(N''( - P2(N"))
To examine the relationship between FPR, and the number of multimodal labels N, the partial derivative /FPR2/ON' /ON' is computed. Assuming #2 > μ1 and μ1 + o //'N'= > 122, the result shows that OFPR>/ØN" < O when Yl + a e//Vuu > v > 122- Since N' increases with N, it follows that FPR, decreases with N, demonstrating that incorporating more auxiliary modalities in negative labels enhances OOD detection performance.
5
Experiments
5.1 Experiment Setup
Datasets and Benchmarks. For our experiments, we use ImageNet-1k [30] as the primary ID dataset. OOD datasets include iNaturalist [11], SUN [31], Places [32], and Textures [33], which cover a wide variety of scenes and semantic categories. We also adopt the experimental setup from MCM [9], which leverages subsets ImageNet-IK to evaluate our method. Specifically, ImageNet- 10 and ImageNet-20 are alternately used as ID and OOD datasets. Furthermore, extend our evaluation to more generalized ImageNet variants, including ImageNet-R [34].
Implementation Details. Our framework is built upon CLIP [8] as the core model. Unless otherwise noted, we utilize the ViT-B/16 architecture as the image encoder and a Masked Self-Attention Transformer [35] as the text encoder. For image generation, we employ the Stable Diffusion. We set a = 0.2 and w = 0.15, and provide ablation experiments. Further details can be found in Appendix G. To improve inference efficiency, all synthetic images are pre-generated before the evaluation phase, eliminating the need for additional computational overhead during testing. Further details in Appendix F.3.
For evaluation, we use two primary metrics: (1) FPR95: The false positive rate (FPR) at true positive rate (TPR) of 95% for ID data. (2) AUROC: The area under the receiver operating characteristic curve. Additionally, we report the results in terms of AUPR Appendix F.2.
Baseline Methods. We benchmark our method against several state-of-the-ar zero-shot OOD) detection approaches, including Mahalanobis Distance [36], Energy Score [37], ZOC [38], MCM [9], and NegLabel [12]. Additionally, we compare our approach with OOD detection models that have been trained or fine-tuned using ID data, such MOS [39], MSP [1], CLIPN [40], VOS [41], and
NPOS [42].
5.2 Main Results
Far-OOD Detection ImageNet-Ik. We compare method with representative OOD de- tection approaches-includnng zero-shot methods (MCM, EOE, NegLabel) and supervised re- implementations that fine-tune CLIP ImageNet- ----neet-l--ummiiiiiee in Table I. Our approach attains the strongest overall performance on ImageNet- 1k: relative to the strongest prior method, NegLabel, it reduces average FPR95 by 1.75% and increases average AUROC by 0.14,. and it outperforms NegLabel on evaluated OOD datasets. Robustness under domain shifts is analyzed in Appendix E.
Near-OOD Detection on ImageNet Subsets. We further report near-OOD results on the ImageNet- 10/mageNet-20 subsets in the main text (Table 2). When ImageNet-10 is treated as the in-distribution
7

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Table 1: Performance Comparison of ImageNet-1k Far OOD Detection. The bold indicates the best performance on each dataset, and the gray indicates methods requiring an additional massive auxiliary dataset.
Method
INtturalist
SUN
Places
Textures
Average
FPR95, AUROCT
FPR95
AUROCT
FPR95,
AUROCT
FPR95,
AUROCT
FPR95,
AUROCT
(BiT) [39]
98.15
40.63
92.01
49.54
89,00
60.43
81.23
39.97
90.11
MSP [1]
40.89
88.63
65.81
81.14
67.90
80.14
64.96
78.16
59.89
82.04
CLIPN [40]
19.13
96.20
25.69
94.18
32.14
92.26
44.60
88.93
30.39
92.89
VOS [41]
28.99
94.62
36.88
92.57
38.39
91.23
61.02
86.33
41.32
91.19
NPOS [42]
16.58
96.19
43.77
90.44
45.27
89.44
46.12
88.80
37.93
91.22
Mahalanobis [36]
99.33
98.54
Energy [37]
81.08
85.09
79.02
84.24
75.08
83.38
93.65
65.56
82.21
79.57
[38]
$7.30
86.09
81.51
81.20
73.06
83,39
76.46
85.19
81.79
MCM [9]
30.91
94.61
37.59
92.57
44.69
89.77
57.77
86.11
42.74
90.77
NegLabel [12]
1.91
99.49
20.53
95. 49.49
35.59
91.64
43.56
90.22
25.40
94.21
DualCnst 0.98
99.70
18.13
95.66
31.77
34.91
91.73
94.72
Table 2: Performance Comparison of ImageNet Subsets on Near OOD Detection. The bold indicates the best performance each dataset, and the gray indicates methods requiring additional massive auxiliary dataset.
ImageNet-10 ImageNet-20
ImageNet-200 ImageNet-10
Method
Average
OOD
FPR951
AUROCT
FPR95
AUROCT
FPR95,
AUROC1
CLIPN [40]
7,80
98.07
13.67
97.47
10.74
97.77
MaxLogit
9.70
98.09
14,00
97.81
11.85
97.95
Energy [37]
10.30
97. 94
16.40
97.37
13.35
97.66
MCM
5.00
98.71
17.40
97.87
11.20
98.29
NegLabel [12]
5.10
98. 98.86
17.60
97.04
11.35
97.95
DualCnst
2.20
98.96
12.20
97.44
7.45
98.20
(ID) dataset and ImageNet-20 OOD, our method lowers FPR95 by 2.4% and improves AUROC by 0.1% over NegLabel. Conversely, when ImageNet-20 is ID and ImageNet- is OOD, our method achieves 5.4% reduction in FPR95 and 0.4% gain in AUROC. The subset splits and ID label configurations follow MCM [9]. For a fair comparison, we reproduce NegLabel and MCM under the same protocol.
Far-OOD Across Alternative ID Datasets To assess generality beyond ImageNet-Il and to under- stand how ID semantics affect far-OOD detection, we evaluate our method across seven datasets with diverse granularity and domain bias: CUB-200-200 (fine- grained birds) [43], Stanford Cars (fine-graing granegrained vehicles) [44], Food-101 (coarse-to-fime fine foods) [45], Oxford-III7 Pet (fine-grained pets) [46], and three ImageNet-derrved subsets (ImageNet-1O, ImageNet-20, ImageNet-1007 For each ID dataset, we evaluate on the standard far-OOD suite iNaturalist, SUN, Places, and Texture, reporting FPR95 and AUROC t (Table 3). Unless otherwise specified, backbone as prior work and do not tune the backbone on OOD data. We set a = 0.1, extract features from the 3rd, 6th, and 9th layers of the visual encoder, and use fusion weight of 0.15. All hyperparameters are selected once using ID-only validation and are shared across datasets to avoid per-dataset cherry picking.
the same CLIP
Across all seven ID datasets and four OOD benchmarks, our method surpasses the strongest base- line. On fine-grained IDs such Stanford-Car Cars and Oxford-IIIT Pet, performance approaches the numerical ceiling (near-zers zero FPR95 with AUROC se 100), indicating that our dual-constraint design preserves class discrimination while suppressing spurious activations. On more heterogeneous IDs (e.g., ImageNer- 100), our approach yields lower false-positive rates than NegLabel while maintaining high AUROC, suggesting improved calibration under broader intra-class variation. Notably, gains most pronounced on Texture and Places, which are known to challenge methods that overly rely on background style cues.
5.3 Ablation Study
Score Functions. To demonstrate the superiority the proposed OOD detection score SpualCnst present the average results the ImageNet-IK KK dataset in Figure 3 (a), comparing with other

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Table 3: Performance Comparison of Different ID Datasets Far OOD Detection. indicates the best performance on each dataset.
The bold
OOD Dataset
ID Dataset
Method
iNaturalist
SUN
Places
Texture
Averuge
FPR95]
AUROC
FPR95,
AUROCT
FPR95),
AUROC1
FPR95,
AUROCT
FPR95,,
AUROCT
MCM [9]
0.05
99.77
0.02
99.95
0.24
99.89
0.02
99.96
0.08
99.89
Stanford-Cars
NogLabel [12]
0.01
99.99
0.01
99.99
0.03
99.99
0.01
99.99
0.01
99.99
DualCost
0.00
100.00
0.00
100,00
99.99
0.00
100.00
0.01
100.00
MCM [9]
9.83
98.24
4.93
99.10
6.65
98.57
6.97
98.75
7.09
98.66
CUn-2000
NegLabel [12]
DualCnst
0.12
99.98
0.02
99.99
0.38
99.89
0.00
100.00
0.13
99.96
MCM [9]
2,85
99.38
1.06
99.73
2.11
99.56
0.80
99.81
1.70
99.62
Oxford.Pel
NegLahel [12]
0.01
99.99
0.02
99.99
0.17
99.96
0.11
99.97
0.07
99.9%
DuaCCust
0.00
100.00
0.00
100,00
0.15
99.97
0.09
99.98
0.06
99.99
MCM [9]
0.64
99.78
0.90
99.75
1.86
99.58
4.04
98, 62
1,86
99.43
Food-101
NogLabel
0.01
99.99
1.61
99.60
0.40
DualCust
0.00
100.00
0.00
100,00
0.01
100.00
1.52
99.57
0.38
99.89
MCM [9]
0.12
99.80
0.29
99.79
0.88
99.62
0.04
99.90
0.33
99.78
ImageNor-10
NogLabel [12]
0.02
99.83
0.20
99.88
0.71
99.
0.02
99.94 94
0.24
99 85
DualCast
0.01
99.97
0.09
99.93
0.57
99.75
0.02
99.96
0.17
99.90
MCM [9]
1.02
99.66
2,55
99.50
4.40
99.11
2.4.
99.03
2,60
99.32
ImageNet-20 NogLabel
0.15
99.51
98. 98.97
2.41
2.22
99.39 09.39
DualCnst
99.13
2.18
[9]
ImageNet- 100 NegLabel [12]
0.53
99.87
9.91
98.12
20.26
96.18
25.50
95.27
14.05
97.36
DualCast
0.41
99.90
8.68
98.34
18.72
96.44
23.51
95.72
12.83
97.60
scoring functions: SMAX- SEnergy and MMaxLogit All these functions are specifically designed for the Dual Consistency approach. Please refer to Appendix G.12 for the specifie forms and results more datasets. Results show that our SpunlCns achieves the best OOD performance. This verifies the superiority and importance of the proposed OOD detection score.
To explore the effectiveness pixel-level features from different layers of the visual encoder, we sample various pixel layers and assign different weights, as shown in Figure 3 (b). Specifically, we experiment by selecting the (Ist, 2nd, 3rd) layers, (4th, 5th, 6th) layers, (7th, 8th, 9th) layers, (9th, 10th, 11th) layers, and all pixel layers to combine with semantic layers. In Figure 3 (c), we further investigate the impact of different weight distributions for to identify the most suitable pixel-level feature weighting. For details on the selection of w, layers, and results, refer to Appendix G.5.
Different Layers of the Visual Encoder.
100
100
54.
18:64
06.20
F1905
AUROC
PURIIN
2.7
359
SMAX
8 Szawg
Satastogk
Spaacua
(12.3) (4.58)
(789) (9.1)1))
all laver
0.25
Figure 3: Ablation study on (a) score function, (b) Different Layers, and (c) Different Weight. ID dataset: ImageNet-10; OOD dataset: ImageNet-20.
5.4 Further Analysis
More Experimental Results. We conducted experiments on the CIFAR-IO/CIFAR-IOS [10] bench- mark further validate our method. The details of the ImageNet-A [47] and ImageNet-V2 [48] generalization datasets are also provided in the Appendix E.2. Additionally, we explored the impact of randomness introduced by Stable Diffusion when generating synthetic images with different random seeds, as demonstrated in Table 33 The results show that the effect of Stable Diffusion's 's randomness on our method negligible. It important to note that we did not manually select the most favorable random seed for Stable Diffusion. Instead, we generated 32-bit integer random seed by hashing the combination of each class label and synthetic image index. Fach synthetic image generated for class using this seed exhibits substantial randomness, further demonstrating that our method is not

---

ID Samples
OOD Samples
(b) Negl.abel
(b) DualCnst
Figure 4: T-SNE visualizations obtained by the classifier output. ID set: ImageNet-10; OOD set: ImageNet-20. We use distinct colors to represent different OOD classes. Our DualCnst method achieves better separability between ID and OOD classes compared to NegLabel.
influenced by the randomness of Stable Diffusion-generated images. We also conducted experiments with different CLIP visual encoders, and the results showed that stronger visual encoders, which capture more detailed information, are more beneficial our method. For more details, please refer to Appendix G.3.
Effectiveness of DualCnst. Figure 4 shows the T-SNE [49] visualization of the softmax outputs. We compare the results of NegLabel and DualCnst, using the ImageNet-10 dataset for ID and ImageNet-20 dataset for OOD. In this setup, there several semantically similar pairs of ID and OOD categories, such as: horse (ID) vs. zebra (OOD), Swiss mountain dog (ID) vs. timberwolf (OOD), warplane (ID) space shuttle (OOD), and garbage truck (ID) steam locomotive (OOD). In the presence of such datasets, methods that expand the label space, like NegLabel, often struggle to find labels with high overlap probability with true OOD labels, leading to suboptimal performance. As shown in (a) with the black bounding box, it is difficult to distinguish between ID and OOD samples, as they tend interweave. DualCnst, however, addresses this issue by leveraging visual information to differentiate between II) and OOD samples. As demonstrated in (b), we incorporate visual information into NegLabel, allowing for better differentiation based on unique visual features inherent to ID and OOD samples, such as the stripes on a zebra or the ears and fur of a timberwolf. These observations indicate that DualCnst enables significant improvement in the classifier's ability, making semantically similar ID and OOD samples more separable.
6 Conclusion
This work presented DualCnst, a training-free framework for zero-shot OOD detection that integrated both semantic and visual consistency. By generating synthetic exemplars from ID and OOD labels, it enabled robust similarity computation without requiring access to ID data additional training. unified scoring function fused semantic and visual cues, and theoretical analysis demonstrated that multimodal negative label aggregation reduced score variance and enhanced ID-OOD separa- bility. Extensive experiments across diverse benchmarks confirmed that DualCnst outperformed existing zero-shot approaches and achieved state-of-thh of-the-art performance. By unifying vision-lane lsngua- aligmment with generative visual synthesis a training-free -fee manner, DualCnst offered a scalable, plug-and- and- solution for OOD detection in open-world scenarios.
Acknowledgements
This work is in part supported by Fujian Province Natural Science Foundation of China under Grant (2024J01158) and Shenzhen Science and Technology Program under grant 11Y120220081315544002222 and the National Key R&D Program of China (2022YFA1004203. 2021YFF0501500 503), the China's National Foundation for Natural Sciences (62125111, 62331028. 62476268, 62206273).
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The abstract and introduction clearly outline the paper's contributions: I) proposing DualCnst, a framework that integrates text-image dual consistency for zero-shot OOD detection; (2) introducing a synthetic image generation pipeline for visual exemplars without requiring real ID data. The scope is well-defined, emphasizing assumptions (no access to ID images, compatibility with text-only methods like NegLabel) and limitations (dependency on text-to-image model quality). Guidelines:
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Question: For each theoretical result, does the paper provide the full set of assumptions and complete (and correct) proof?
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and G. Specifically, experiments on ImageNet-IN KK were conducted on a single NVIDIA RTX A6000 GPU using Python 3.8 and PyTorch 1.13. We employed the CLIP-B/16 model with the following key parameterizations. based augmentation; Feature Layers: Layer weights of 0.15 applied intermediate layers 8, 9, and the final layer for visual feature aggregation; Fusion Parameters: Image-text simi- larity fusion coefficient 0.2; Temperature Scaling: Temperature parameter = 0.01 for logit calibration.
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Appendix G.8 systematically analyzes the stochastic variations inherent in generative models during synthetic image synthesis and their implications on detection robustness.
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Appendix
Broader Impacts
22
B Related Works
22
Theoretical Justification for Section
23
C.I Fusion under weak dependence
23
C.2 Empirical Validation under Weak Dependence
25
Limitations
27
E Further Experiments
28
E.1 Robustness Domain Shift
28
E.2 Other OOD Detection Benchmarks
29
E.3
Structured Domains: Medical Imaging
29
Experimental Configuration and Details
30
F.1
Details of Mining Potential OOD Labels
30
F2
Evaluation Metrics
31
F3 Experimental Configuration
F.4
Compute Profile and Deployment Details
32
G
Additional Ablation Studies
33
G.1
Efficacy of DualCnst
................................
33
G.2 Effect of Prompt Engineering
33
G.3 Vision Backbone
33
................................
G.4 Generative Models
................................
35
G.5 Encoder Layer
35
G.6 Fusion Parameter a of Dual Consistency
35
G.7 Impact of Noise and Distribution Shifts in Synthetic Images OOD Detection
Performance
37
G.8 The Randomness of Stable Diffusson
37
G.9 Open- Vocabulary Scaling via OOD Label Clustering
38
G.10 Comparison with LMD on CIFAR
39
G.11 Controlled Semantic Mixing: Dog House Hybrids
39
G.12 Score Function
40
21

---

A Broader Impacts
Our framework could enhance the reliability of systems in safety-critical domains like medical imaging, where undetected OOD samples may lead to diagnostic errors. By enabling synthetic data-driven OOD detection without real patient data, also mitigates privacy risks associated with medical dataset sharing.
Positive Impacts:
Malicious actors could exploit our text-to-image synthesis pipeline to generate adversarial OOD samples that evade detection. Additionally, energy-intensive image generation processes may contribute to environmental costs scaled without optimization.
Negative Impacts:
Mitigations: We recommend (1) adversarial robustness testing against synthetic OOD benchmarks, (2) controlled access to synthetic image generators in high-stakes applications.
B Related Works
OOD Detection. Early methods for OOD detection include classification-based approaches that rely on well-trained ID classifier, such as MSP [1]. Density-based methods, such as likelihood ratios [50] and likelihood regret [51], estimate the likelihood of data points to identify OOD samples. Reconstruction--aat methods [52, 53, 54] leverage reconstruction errors from generative models, including VAEs and autoencoders, to detect OOD instances, Post-hoc methods. including ODIN [55] and energy-based scoring [37]. enhance pre-trained models without modifying their parameters. More recently, multimodal vision-language models such as CLIP and its variants [56] have enabled zero-shot OOD detection by leveraging text-image embeddings, marking shift toward more versatile and scalable solutions.
Zero-shot OOD Detection. Recent advancements in zero-shot OOD detection take advantage of the powerful pretraining capabilities of models like CLIP, allowing for efficient OOD detection without the need for large external OOD labels. ZOC [38] introduces CLIP-based framework for zero-shot OOD detection, where potential OOD labels are generated for input instances using image captions, aligning images and text for zero-shot classification. MCM [9] performs OOD detection by utilizing scaled softmax values of the maximum logits as confidence scores, but it relies solely on ID class labels and does not fully exploit open-world textual information. CLIPN [40] improves the model's 's ability to reject mismatched inputs by introducing learnable "negative" prompts and a dedicated "negative" text encoder. EOE [57] utilizes the expert knowledge and reasoning abilities of large language models (LLMs) to generate potential anomalies, enabling more effective OOD detection. Neglabel [12] proposes a novel method that enhances the distinguishability between ID and OOD samples by mining potential OOD labels from corpus. However, these methods do not fully consider the visual effectiveness of images. In contrast, DualCnst addresses this limitation by making semantically similar ID and OOD samples more distinguishable. Moreover, it can be seamlessly integrated into existing OOD frameworks.
Stable Diffusion for OOD Detection. Stable Diffusion has been explored for OOD detection in several studies. [58] introduced a semantic mismatch-guided variant by masking input regions and measuring semantic inconsistencies between original and reconstructed samples, which addresses the limitation of pixel-level error metrics in traditional reconstruction methods. Diffusion based neighborhood analysis [59] injects noise perturbations to generate sample variants and quantifies latent feature distribution divergence for enhanced sensitivity to local anomalies. LMD [54] introduces diffusion- based approach for image inpainting, where the input image is reconstructed, and the reconstruction error is used as an indicator for OOD detection. In contrast, DualCnst employs Stable Diffusion for image generation, offering more efficient solution in world scenarios. Unlike LMD, DualCnst reduces the computational burden on the inference process, making it a more practical and scalable approach for OOD detection in dynamic environments.
Systematic Comparative Analysis. To better characterize methodological distinctions, we conduct a systematic comparison across three key dimensions: (1) Enhancement strategy spectrum (seman- integration.
tic/image space), (2) Training requirement compatibility, and (3) As Table 4 demonstrates, existing approaches exhibit notable limitations: classification baselines lack multimodal awareness, zero-shot methods neglect visual space enhancement, while generative approaches impose substantial training overhead. DualCnst uniquely combines amanttcc-txxtul align-
22

---

Algorithm Zero-shot OOD detection with text-image dual consistency
1: Input: ID class labels )rid, test sample x, text encoder T, image encoder I, Stable Diffusion
(SD), NegLabel, fusion coefficient layer weight w, threshold X; Synthesis stage:
Synthesize OOD class labels
2: Given yid, ) = Negl ())a))
I/ Synthesize ID/OOI image labels
3: Given yid U yood X = (proomp < Qui u yood >);
Testing stage:
// Calculate image-to-image similarity
(x) (8.)
4: iiing (x)
(x)I [x:]]] &, X::
5: st.ing (x) 11-1 simgg (x);
// Calculate image-to-text similarity
6: t, = promptssy y1 E yod yood,
T(t.)
7: si, [XIITTT
// Integrate text and visual information
8: (x) = a Si,ing( (x) + -aaa a) 88 (x);
I/ Calculate OOD detection score
9: DunlCnst x) DE 2/111 exp(s, (x))
(x)(
10: Output: ID DuatCins x) > X, else OOD.
ment with visual-space consistency learning, achieving full compliance with all evaluation criteria-the only method supporting both training-free free deployment and multimodal generative synergy.
Table 4: Comparative Analysis Method Characteristics
Fahuncement Strateyy
Multimodal Utllization
Generative Model
Category
Representative Methods
Training-Fooe
Application
Semantic Space
Image Space
MSP [1]
x
Classification Benchmarks
ODIN [55]
Energy [37]
x
X
X
[38]
V
V
7ero Zero-Shot Shot Methods
MCM [9]
x
CLIPN
NogLabel [12]
X
Yang [58]
X
Generative Approuches
LMD [54]
Diffusion
(Ours)
Notation: =Suppoored, x=Not Supported. Text parentheses indicates implementation specifies.
C Theoretical Justification for Section
C.1 Fusion under weak dependence
To analyze the dependence of the false positive rate FPR, on the number of multimodal labels N, consider its reformulation in terms of the error function erf(-):
1
PI - PP(()) erf (2X - 1)+ V2p2(N') (1P('"()))P)(()))
(
FPR, 2 2
+ erf
P2
23

---

where the error function is defined as erf(x)
J 50 e e
dd, dt, which satisfies ((z)
+ erf
z
Define:
(1 p1)
erf 1(2 1), B VM P2)
A =
p2 p2)
2p22 P2)
where
- (kk V N),
P2 - Φk v NN),
and
v MM
h2 v u2
hn
Since (4 < U2 and v/ > μ2, it follows that 1i > k2 0. Now,
0A
pi (1 - P1)
erf erf (2X 12X - 1)
ON
ON
P2 P2)
Letting Q
(1(1pp)) obtain p2(1-p2)'
aQ
8p AN (1 2p1)
P2(1 P2) P1 p1)(1 2p22)
Opp ON
ON
[p2(1 P2)]
From the definition of Pı and p2. the derivatives are given by
0p1
b
Op2
lo
ō(k VNY
6(k2V2'))
ON
2VN"
ON
2/N"
where (xx) = /2 e =212 is the standard normal probability density function (PDF). Since u <
M1 + v then 0 < 12 < k1
the function ((k//N)-A is increasing with respect to k. Thus, VNT
we have
0p1
0p2
0.
ON
ON
Moreover, since ub > u22 and
>> pr. it follows that
P2(1 (1) 2p1) > (1 P1)(1 2p2)
Thus, we conclude that
0A
< 0.
ON
On the other hand,
aB
V M
Opt ON
0pz ON
V P2
P2) -(11 P22)
2/ ON"
1 1-2p2
0pz
ON
22
P2(1 - p2)
By the monotonicity of @(kv N follows that
Öp NN
Op2
(12(1-200 <
NN
Furthermore, since p2 > pı and
1/2, we obtain
1 2p2
Op2
(p1 p2)
0.
2 2/p2(1 - -p2) ON
Thus, it follows that
AB
ON <0.
By the definition of FPRA.
OFPRA
(A+
a + B)
< 0.
ON
ON

---

C.2 Empirical Validation under Weak Dependence
complement the theoretical analysis by empirically quantifying cross-modal and cross-layer dependencies and by examining how multimodal score fusion reduces variance. To this end, we compute pairwise correlations and covariances among similarity scores extracted from the text and image branches, well from multiple vision encoder layers. We additionally visualize these relationships in Figures and 6 as heatmaps to illustrate the dependence structure across all layers.
Cross-modal and cross-layer correlations. Table shows that the Pearson correlation between text-based and image-based similarities is nearly zero (r = 0.01), indicating that the two modalities convey largely independent information. Table and the corresponding correlation heatmap in Figure 5 reveal that inter-layer dependencies are also weak: most off off-diagonal correlations are close to zero and none exceed 0.2 in magnitude. This observation empirically supports our weak- dependence assumption different layers capture complementary but only mildly correlated cues.
Table 5: Pairwise Pearson correlations between modalities.
iig_sim
text_sim
img_ sim
1.00
0.01
text sim
0.01
1.00
Table 6: Pairwise Pearson correlations across layers.
text_siim sim
layerl sim
layer5_sih _im
layer9_sine
text sim
1.00
0.00
-0.12
0.04
layerl sim
0.00
1.00
0,06
-0.10
layer5_sie sim
-0.12
0.06
1.00
0.19
layer9_sime sim
0.04
-0.10
0.19
1.00
Covariance matrices. The covariance analysis in Tables 7-8 and Figure 6 further substantiates this
independence trend. Cross-modal covariance is extremely small (1.11 x 10-9), indicating negligible shared variance between the text and image similarities. Across layers, off-diagonal covariances remain several orders of magnitude lower than diagonal terms (Table 8), implying that fluctuations between different similarity sources are largely uncorrelated.
The covariance heatmap in Figure 6 visually emphasizes this structure- bright diagonal dominance with gear-zero oif-diagonal intensity- caannstt-ddmonstratiiin that each layer contributes distinct but weakly dependent information. This empirical finding supports the theoretical premise that aggregating such weakly dependent scores will monotonically reduce overall variance and yield more stable OOD decision boundary.
Table 7: Covariance matrix between img_sim sim and text_ rext_sim.
img_sim
text_sim
img sim
4.20e-04 1.11e-05
1.1 4.20e-04
timt_sim
Table 8: Covariance matrix across layers.
text_sim
layerl_sine
layer5_sine sim
layer9_sim
text text_sim
4.20e-04
-8.0e-077
-1.88e-04
2.77e-06
layerl_sim
-8.02e-07
6.08e-04
1.08e-04
-9.57e-05
layer5_sin sim
-1.88e-04
1.08e-04
5.10e-03
5,16e-04
layer9_sim
2.77c-06
-9,57e-05
5.16e-04
1.50e-03
25

---

Iast4mage
1.000
-0.02
-2.024
-1041
9.7
-0.28
-2027
Layer
4.000
1,500
2.338
1.29
0209
6.001
4999
4.202
Layor
-0004
0.356
2,00
1.803
0.833
0.359
7.244
-0214
-1.19
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4.64
0:248
2.776
1.000
0.631
0.312
2.236
6.11
0.007
0.164
4,99
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4231
Layer
0.207
0.200
2.638
0.025
1.003
0.770
2.423
2.39
0151
0.330
4.433
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4154
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4.128
0.281
2.333
63322
0.777
1.700
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5337
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0.075
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4.00
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0234
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5.49
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0.053
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0.31
0.397
5.439
Layer
-0.02
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0.13
Layor
4.003
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-314
Bortimape
Lawe
Layer
Laner
Laner
Lawer
Lawer
Lawer
Laner
Law
Layar
Layer
Layn
Figure 5: Heatmap of Pearson correlations across text and vision encoder layers. Off-diagonal elements remain close to zero, confirming weak cross-layer dependence.
Variance reduction under fusion. Finally, Table 9 quantifies the empirical variance the ag- gregated S
weakly dependent signals are fused. We observe clear monotonic decline-from 0.711 x 10-3 using only the text score to 0.257 x 10-3 when six intermediate layers integrated- integrated-ddemonsteeee -emonstrating that fusing multiple partially independent cues efffectively smooths stochastic fluctuations in similarity estimation. This empirical trend aligns with the theoretical prediction that Var(S) 1/N under weak dependence, confirming that multimodal and multilayer fusion yields more stable and discriminative OOD scores.
Table 9: Empirical variance (x (x10-3) number of fused signals.
Setting
Empirical variance
Only Text score
0.7110
Only Image score
0.3691
Fusion score intermediate layer)
0.3553
Fusion score (2 intermediate layers)
0,3366
Fusion score intermediate layers)
0.3274
Fusion score (4 intermediate layers)
0,3038
Fusion score intermediate layers)
0,2717
Fusion score (6 intermediate layers)
0.2567
26

---

1013
Mi
623e-01
0.0011
0.6022
6.300
2.006
5.78e44
3.5500
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-520e01
421e-01
0,6033
0.3695
8.52ed4
-1.8600
-22760
4.30e04
5.30e-01
-515e01
Layer 4-
1.0004
3.26-04
0.0032
0.0023
0.3003
0.001
1236-94
5.09605
3,22604
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-3.0.044
1.800
1.09:00
010018
0.6621
0:3041
3,0311
0.004
0.013
536004
6.48044
1416-44
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1. 1.
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10015
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9-3e-01
Layers
5.00a45
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2,023
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2,0211
Toat Stat-maape
Layer
Layer
Laver
Laper
Layers
Laper
Laper
Layer
Laper
Laver
Layer 12
Layer 12
Figure 6: Heatmap of covariance values text and vision encoder layers. Strong diagonal dominance and weak off-diagonal terms indicate low inter-layer variance coupling.
D Limitations
Limitation Dependence on synthetic image quality. The proposed method relies on pre-generated synthetic images to capture visual exemplars for both in-distribution and OOD labels. Consequently, its performance may degrade if the generative model fails to produce high-quality images. In particular, artifacts such severe noise, blur, unstructured patterns may introduce misleading visual cues that hinder effective similarity computation. To address this, we recommend employing robust text-to-image models such Stable Diffusion XL-Turbo, which substantially reduces generation failures. Notably, as shown in Table 31, even when the synthesized images are stylistically mismatched with the test samples (e.g., rendered as oil paintings instead of natural photos), they still offer marginal performance gains over the text-only baseline.
I Limitation II. Computational cost of image synthesis. The framework incurs additional computa- tional overhead during the image synthesis stage. As reported in Table 18, generation time scales with the number of candidate OOD labels, which may pose challenges for large open-set label spaces. To mitigate this, we adopt the following optimizations: (1) using accelerated generative models such Stable Diffusion XL-Turbo; and (2) pre-generating all synthetic exemplars offline before inference. Since image synthesis is performed once per label set and reused across test samples, the runtime cost during inference remains negligible, making the approach scalable to large datasets.
Limitation III. Independence assumption (weak dependence in practice). theoretical analysis assumes i.i.d. similarity scores si,j for clarity. This assumption can be relaxed: practice, similarity signals across modalities and encoder layers are typically weakly dependent. Provided that the aggregated score satisfies Lindeberg- -type condition more generally, arises
Section 4,
27

---

from bounded-variance sequence with limited dependence (e.g.. m-dependent or exchangeable variables) central-limit approximation remains valid. Crucially, the key insight persists: increasing the number N of (appropriately weighted) modalities/layers reduces the variance of the fused score and improves separability; under weak dependence, the variance of the weighted sum continues to decay on the order of 1/N, to constants determined by the dependence structure. Empirically, we observe (i) small cross-modal and cross-layer Pearson correlations and (ii) a monotonic decrease in the empirical variance of the fused score as more signals are aggregated (Tables 5-9), supporting the practical validity of this relaxation.
E Further Experiments
E.1
Robustness to Domain Shift
To assess the generalization ability of our method under domain shifts, we conducted experiments using the ImageNet Domain Shift dataset, with ImageNet-R serving as the dataset. Table 10 presents the results based on CLIP-B/16 with 0.1, selecting the 3rd, 6th, and 9th layers of the visual encoder, and assigning weight = 0.15. Our method demonstrates stronger generalization performance compared to NegLabel. ImageNet-R [34] consists of 30,000 images spanning 200 ImageNet categories, with representations in diverse artistic styles, including art, cartoons, graffiti, embroidery, graphics, origami, paintings, patterns, plastic objects, plush objects, sculptures, sketches, tattoos, toys, and video game renditions. Table presents an evaluation of DualCnst's s robustness using the ImageNet-A [47] generalization dataset the ID dataset, while iNaturalist [11], SUN [31], Places [32], and Textures [333] serve as OOD datasets. We compare DualCnst against state-of-the-ar methods. DualCnst outperforms NegLabel across all datasets, achieving improvement of 2.09% in FPR95 and 0.25% in AUROC on average.
In Table 12, we further investigate the robustness of DualCnst under the same experimental setup using another generalization dataset, ImageNet- V2 [48]. The experimental results demonstrate that our proposed method exhibits superior performance in handling domain shifts.
Table 10: Robustness results on ImageNet-R dataset. The black bold indicates the best performance.
OOD Datasel
Method
iNaturalist
SUN
Places
Texture
Average
FPR95
AUROCT
FPR95
AUROCI
FPR95
AUROCT
FPR95
AUROCI
FPR95,
AUROCT
Energy [37]
99.91
30.36
99.33
33.20
98.84
34.74
99.56
23.09
99.41
30.35
MaxLogit
86.53
81.58
82.11
81.48
78.16
79.86
91.24
69.45
84.51
78.09
MCM [9]
51.59
92.24
52.88
89.97
52.04
88.01
56.45
85.65
53.24
88.97
LegLahel [12]
1.60
99.58
15.77
96.03
29.48
91.97
90.60
94.54
DualCast 0.59
99.86
8.92
98.19
19.27
95.20
14.13
95.50
10.73
97.19
Table 11: Robustness results on ImageNet-A dataset. The ID class labels are the same as ImageNet. The black bold indicates the best performance.
OOD Dataset
Method
iNaturalist
SUN
Places
Texture
Average
FPR95,
AUROC
FPR95
AUROCT
FPR95,
AUROC*
FPR95
AUROCT
FPR95,
AUROC*
Energy
09.48
Maxl ogit
92.88
74.14
81.54
80.55
78.51
79.06
90.00
69.41
85.73
75.79
MCM 80.41
77.02
76.12
78.92
76.90
74.10
77.36
76.88
77.45
NogLabel 4.09
98.80
44.38
89.83
60.10
82.88
64.34
80.25
43.23
87.94
DunlCnst 3.54
98.99
32.41
92.79
48.66
87.04
47.77
89.54
33.09
92.09
Following the near-OOD evaluation protocol in OpenOOD v1.5, we conducted robustness evaluation using ImageNet- and ImageNet-R as in-distribution (ID) datasets, with SSB-Hard, NINCO, iNatu- ralist, and Texture as out-of-distribution (OOD) benchmarks. As summarized in Table 13, DualCnst. demonstrates stronger robustness than baseline methods under this more challenging near-OOD experimental setup.
28

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Table 12: Robustness results on ImageNet- V2 dataset. The ID class labels are the same as ImageNet. The black bold indicates the best performance.
OOD Dataset
Method
INaturalist
Places
Texture
Average
FPR95
AUROC
FPR95
AUROCT
FPR95
AUROC
FPR95
AUROCT
FPR95,
AUROCA
Energy
99.85
32.93
99.12
34.45
98.02
39.51
99.57
21.52
99.14
32.10
Maxl ogit
81.79
64.34
MCM 44.89
92.14
51.17
89.69
56.73
86.44
69.57
81.51
55.10
87.56
Neglabell 2.47
99.40
25.69
94.46
42.03
90.00
48.90
88.46
29.77
93.08
DualCnst
1.49
99.60
21.90
94.92
36.71
90.62
50.62
88.18
27.68
93.33
Table 13: Robustness experiments on ImageNet- Ik and ImageNet-R, The black bold indicates the best performance.
OOD Dataset
Method
SSB-Hard
NINCO
iNaturalist
Texture
Average
FPR95
AUROC1
FPR95
AUROCT
FPR95
AUROCT
FPR95
AUROCT
FPR95,
AUROCT
Energy [37]
96.62
51.92
87.39
67.17
94.54
61.56
94.89
45.94
93.36
$6.65
MaxLogit
$7.92
79.69
70.72
65.41
87.29
72.04
80.84
77.03
74.19
MCM [9] 94.00
57.42
88.98
63.40
75.39
83.18
67.94
81.80
81.58
71.45
NegLahel [12]
70.38
74.74
1.56
99.59
42.59
50.04
82.63
DualCnst 82.33
69.65
67.57
76.41
1.07
99.68
42.55
88.93
48.38
83.67
E.2
Other OOD Detection Benchmarks
In Table 14, we present the performance evaluation results using CIFAR-10 and CIFAR-100 [10] as the ID datasets, along with four OOD datasets: iNaturalist [11], SUN [31], Places [32], and Textures [33]. Compared to the Lagl method, our approach demonstrates significant performance gains. Specifically, on CIFAR- 1000 DualCnst achieves an average improvement of 23.59% in FPR95 and 9.34% in AUROC. On CIFAR- 10, yields improvements of 7.56% FPR95 and 1.39% in AUROC. Although DualCnst does not achieve the best performance on CIFAR-10 individually, it outperforms existing methods terms of overall average performance across both CIFAR-IO and CIFAR-100, highlighting its effectiveness in OOD detection across diverse datasets.
Additionally, in Table we follow the fine- fine-grained dataset setup proposed by EOE [57] and conduct experiments on CUB-200 28--000-011 [43]. STANFORD- CARS [44], Food-101 [45], and Oxford-IIIT Pet [n6e.Under this experimental setting, the four datasets are randomly split into two equal subsets, with one serving as the II) dataset and the other as the OOD dataset. Since Negl Label identifies the most semantically distant candidate labels as potential OOD categories during the OOD label mining process, its performance in fine-grained experiments is relatively suboptimal. In contrast, DualCnst demonstrates superior performance, achieving 1.48% reduction in FPR95 and an 8.56% improvement in AUROC.
E.3 Structured Domains: Medical Imaging
We evaluate a highly structured domain by using CheXpert [60] as ID data and sampling 10,000 OOD exemplars from PubMed [61]. Medical images present strong, localized morphological cues (e.g., lung fields, cardiomediastinal contours, lesion textures) that can be complementary textual semantics. Hence, the dual-consistenge design is expected to sharpen separability when semantically related categories remain visually distinct.
Results and interpretation. As shown in Table 16, DualCnst achieves near-ceiling performance (FPR95= 0.00, AUROC= 99.97), outperforming the text-only NegLabel baseline (FPR95= 0.11, AUROC= 99.92). The improvement, albeit on an already saturated operating point, indicates that incorporating visual similarity to synthesized exemplars reduces residual false positives that persist under text-only scoring. In domain where subtle visual patterns (e.g., striations, opacities) carry decisive information, the added image-space constraint likely filters spurious semantic matches, consistent with our variance-reduction analysis under weak dependence.
Implications and caveats. First, the gains arrive without access to real ID) images, preserving data privacy while still leveraging visual priors via synthetic exemplars. Second, because AUROC is

---

Table 14: Additional empirical results with CIFAR-10 and CIFAR-100 as ID datasets. The bold indicates the best performance on each dataset.
OOD Dataset
Dataset
Method
INaturalist
Places
Texture
Average
FPR95,
AUROCT
FPR95.
AUROC1
FPR95
AUROCT
FPR95)
AUROCT
FPRY5
AUROC
Energy
60.70
82.12
53.14
86.00
58.29
82.86
62.52
77.89
58.66
82.22
MaxLogit
97.85
11.81
97.36
16.74
95.55
11.54
97.60
12.27
97.09
CIFAR-I0
MCM
17.87
96.75
30.78
93.17
36.57
90.78
16,38
96.44
25.40
94.29
aeela
0.55
99.84
23.31
95.50
38.70
91.53
19.33
96.65
20.47
95.88
DualCnst
0.42
99.83
15.23
97.07
25.46
94.17
10.55
98.00
12.91
97.27
Energy
82.74
74.47
67.16
81.69
68.20
80.96
81.19
66.51
74.82
75.91
MaxLogit
67.77
81.41
63.26
80.72
65.73
80.81
62,94
82.00
64.93
81.24
CIFAR-100
MCM
97.95
67.50
97.69
60.71
98.40
61.34
90.23
73.58
96.07
65.78
NegT ahel
13.95
96.47
86.61
69.04
91.50
62.08
70.60
80.26
65.66
76.96
DualCnst
2.88
99.23
49.35
84.25
60.68
79.06
55.35
82.65
42.07
86,30
Energy
71.72
78.30
60.15
83.84
63.25
81.91
71.86
72.20
66.74
79.06
MaxLogit
38.38
89.63
37.54
89.04
41.24
88.18
37.24
89,80
38.60
Average
MCM
57.91
82.12
64.24
76.94
67.49
76.06
53,31
85.01
60.73
80.03
NogLabel 7.25
98. 15
54.96
82.27
65. 10.10
76.81
44.96
88.45
43.07
86.42
DualCast
32.29
95.75
91.78
Table 15: Zero-shot fineggraindd OOD detection results. The black bold indicates the best perfor- mance. The gray indicates that the comparative methods require training or an additional massive auxiliary dataset.
ID
CUB-100
Stanford-Curd -aatserd---C--888 Stanford Cars-98
Food-50
Oxford-Pot-18 Oxford-Pett
Method
Average
OOD
CUB-100
Food-51
FPR95
AUROCT FPR95,
AUROCT
FPR95
AUROCT
FPR95 | AUROCT FPR951
AUROCT
CLIPN
73.54
74.65
53.33
82.25
43.33
88.89
53.90
86.92
56.05
83,18
Energy
76.13
72.11
73.78
73.82
44.95
89.97
68.51
88.34
65,84
81.06
Max
76.89
73.00
72.18
74.80
41.73
90.79
65.66
88.49
64.11
81.77
MCM
83.58
67.51
83.99
68.71
43.38
91.75
63.92
84.88
68.72
78.21
NegLabel
82.48
DualCnst
77.99
72.58
78.87
70.38
36.18
92.85
66.46
88.45
64.88
81.07
Table 16: Performance on medical imaging.
Method
FPR95 AUROCT
DualCnst
0.00 / 99.97
NegLabel
0.11 / 99.92
already near 100, absolute headroom is small; the main benefit reducing high-TPR false alarms (captured by FPR95). Finally, although medical images stylistically constrained, domain shifts (scanner, protocol) or prompt/style mismatch in generation could attenuate the benefit; our fusion reduces sensitivity to such shifts by aggregating weakly dependent cues across modalities and layers (Sec. C.2).
Experimental Configuration and Details
F.1 Details Mining Potential OOD Labels
Before generating synthetic images, it is crucial to identify effective OOD labels by leveraging ID
labels as reference. Specifically. we define the of ID labels y = {y pool of nouns and adjectives from open-world resources (e.g., WordNet [62], ConceptNet [63], and Wikipedia Categories') as candidate OOD labels, denoted by y (ū), represents the total number of candidates.
and collect
(c). where C
To assess the semantic relationship between candidate OOD labels and ID labels, we utilize CLIP's text encoder to extract text embeddings for both sets. The embedding of candidate OOD label
'nttps://dump" wikimedia. org/
30

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is given by é T (prompt(ūe)), while the embedding of an ID label is represented as (prompt(ye))... By default, we employ the prompt format "A photo of <label>" generate these embeddings.
Following the methodology outlined in NegMining [12), quantify the semantic distance between each candidate OOD label and the ID labels using negative cosine similarity. Specifically, for given candidate OOD label, we compute its negative cosine similarity with all ID label embeddings, resulting in K similarity scores. The overall semantic distance of an OOD label to the ID label set is then determined as the 17- pereeenttlle (default 17 = 0.05) of these scores:
de = percentile,, y) (( cos (é:: ex ex)
After computing distances for all candidate OOD labels, we select the top M = 10, 000 labels with the greatest distances. The selected OOD label set is defined as:
ynod
TopK (Ide)e=1,)") M).
(8)
During the generation phase, DualCnst utilizes yood yid as the label space for synthetic image generation. To ensure semantic consistency, it employs stable diffusion generate images that align with these labels, thereby providing meaningful visual representations to enhance the inference process.
F.2 Evaluation Metrics
In this study, we adopt the most widely used evaluation metrics in the OOD detection domain, includ- ing FPR95 and AUROC [64]. To further assess the effectiveness of the proposed dual consistency method under additional evaluation criteria, we also report AUPR results for CLIP-B/16 in Table 17. The results demonstrate that our dual consistency method achieves superior performance across all evaluation metries.
Table 17: Performance terms of AUPR. dataset: The experiments are zero-shot OOD detection results with ImageNet- KK as the ID dataset. The black bold indicates the best performance. The gray indicates that the comparative methods require training or an additional massive auxiliary dataset.
OOD
Average
Method
iNaturalist
SUN
Places
Texture
CLIPN
98.59
98.22
98.38
98.59
Energy
96.84
96.50
96.16
94.66
96.04
MaxLogit
97.12
96.65
MCM
98.86
98.28
97.49
98.04
98.17
Negl abel
99.80
98.79
97.76
08
98.61
DualCnst
99.93
99.06
98.04
98.79
98.96
F33 Experimental Configuration
This paper introduces dual consistency (DualCnst) method, implemented using Python 3.8 and PyTorch 1.13 library [65], with all experiments conducted on a single NVIDIA RTX A6000 GPU. Prior to experimentation, the proposed method generates synthetic images, with each image requiring approximately 3 seconds for generation. To mitigate redundant computational overhead across multiple runs, we precompute and store the visual features of the generated images.
We systematically benchmark computational efficiency through two key aspects: (1) Temporal cost analysis for synthetic image batch generation across five scales (0, 1k, 10k), with comparative inference latency evaluation against NegLabel [12] (Table 18). DualCnst achieves efficient batch generation 0.1 seconds (excluding directory initialization overhead), while maintaining competitive inference efficiency (12m 42s vs. 9m 36s for full ImageNet- 1k evaluation) with enhanced detection robustness. (2) Performance comparison under varying OOD label quantities. To ensure fair comparison with Label, we adopt its optimal configuration (10,000 OOD labels) for benchmarking. As evidenced Table 19, our method demonstrates consistent superiority over NegLabel regardless of its OOD label selection size.
31

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For the selection of negative label parameters, we adopt the optimal configuration recommended in NegLabel. All experiments in this study are conducted within the CLIP framework. Unless otherwise specified, we utilize CLIP-B/16 for zero-shot OOD detection. The default hyperparameter settings are as follows: We set w = 0.1 and extract untermediate-layy features from the 9th, 10th, and 11th layers of the visual encoder, which are then fused with the final semantic features. The sum-softmax score employed, with the fusion parameter set to a = 0.1 and the temperature parameter to 7 = 0.01.
Table 18: Computational cost of DualCnst and NegLabel on ImageNet- 1k.
Ok-ood-image
Ik-ood-image Gen, Time
10k-ood-image
Inference Time
Method
Gen. Time
Gen. Time
DualCnst
4m 10s
9m 42s
51m 52s
12m 42s
NegLaaell
Table 19: Performance Comparison Under Varying OOD Label Quantities. The black bold indicates the best performance.
OOD Datasel
NumberiNumber of OOD lahels)
iNaturalist
SUN
Places
Average
FPR95
AUROCT
FPRRE
AUROCT
FPR95
AUROCT
FPR95
AUROCT
EPRI5L
AUROCCT
NegLaheig(kk)
30.91
94.61
$7.59
92.57
44.69
89.77
57.77
86.11
42.74
90.77
DuaCCnstOk)
28.56
35.23
913.38
43.99
59.79
-3872
85.82
90.94
(1))
33.76
85.73
42.28
DualCinttt Lk)
20.75
95.96
42.09
91. 40
50.61
87.48
47.20
87.43
401.29
90.57
NegLabeli L0k)
1.29
99.05
17.00
95. 89
31.91
92.13
42.15
90.51
23.24
94.55
DualCnat1Ok,
0.98
F.4
Compute Profile and Deployment Details
Offline pre-generation. All synthetic exemplars generated offline per label set and reused at inference, so the online overhead is negligible. Using accelerated backbones (e.g.. SDXL- generating images for 10,000 OOD labels takes =55 minutes 0.33 s per label on average. Because this cost is paid once per label set and cached thereafter, latency serving time is dominated by single forward pass of the encoders.
single RTX A6000, i.e., about
Memory footprint. We report GPU memory usage under identical configurations and separate inference from one-time generation.
Table 20: GPU Memory Overhead During Inference and Generation (RTX A6000).
Method
Inference (MiB) Generation (MiB)
DualCnst
33245
9465
NagLabel
26353
MCM
17827
Results and interpretation. From Table 20, DualCnst uses 33245 MiB at inference, which is +26.2% increase over the text-only NegLabel baseline (26353 MiB), and +86.6% over MCM (17827 MiB). This overhead is expected: DualCnst keeps both text and image similarity branches active and aggregates multi-layer signals. trading extra activation/feature memory for improved robustness. Crucially, the generation footprint (9465 MiB) applies only to the offtine synthesis stage; it does not affect online serving because exemplars are reused. Thus, for deployment on a single A6000, generation comfortably fits within memory with headroom for modest batching, while inference-time costs remain stable once the exemplars are cached.
Practical deployment notes. (i) Amortization. Since generation is one-off off per label set, the cost
amortizes across all future queries and model updates that reuse the same labels. (ii) Latency. Online latency is unchanged relative to Neg Lbbel/MMMM aside from the added fusion step, which is negligible compared to encoder forward passes. (iii) Memory controls. Mixed precision (FP16/BF16), gradient disabling at serve time, and per-layer feature caching reduce inference memory without accuracy loss;
32

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exemplar retrieval can be streamed from pinned host memory when VRAM is tight. (iv) Scalability. For larger label vocabularies multi-tenan settings, shard exemplar banks across GPUs or persist them on disk with an in-memory LRU cache to keep the working set small.
G Additional Ablation Studies
G.1 Efficacy of DualCnst
Our method is not limited to applications with NegLabel. To address this concern, we have conducted ablation studies using the DualCnst method combined with MCM (an OOD detection approach that does not require negative labels) As shown in Table 21, the results demonstrate that incorporating synthetic image labels effectively enhance OOD detection performance labels/images. In addition, we have conducted ablation studies using real OOD samples (Table 22). The results demonstrate that while the proposed method achieves better performance with real samples compared to synthetic ones, the improvement margin is not substantial, demonstrating synthetic samples' effectiveness.
without negative
Table 21: Performance comparison of integrating DualCnst with MCM for OOD detection on ImageNet- Ik (ID dataset), The black bold indicates the best performance.
OOD Dataset
Method
ssb-hard [66]
NINOC [67]
iNaturalist
Texture
Averuge
FPR95,
AUROCI
FPR95
AUROCT
FPR95
AUROCT
AUROCT
FPR951
AUROCI
MCM MCM-DualCnst
89.43
64.15
82.70
69.85
30.91
94.61 94.77
$7.77
86.11 85.82
65.20
78.68
88.69
63.66
78.96
74.05
28.56
58.72
63.73
79.58
Table 22: Experimental comparison between real OOD samples and synthetic OOD samples, with ImageNet- 1k as the ID dataset. The black bold indicates the best performance.
OOD Dataset
Source OOD images
iNaturalist
SUN
Places
Texture
FPR95
AUROCT
FPR95_
AUROCT
FPR95L
AUROC1
FPR95
AUROCT
FPR951
AUROCT
Synthetic OOD samples
1.29
99.65
17.60
95.89 98.67
31.91
92.13
42.15
90.51
23,24
94.55
Real OOD samples
1.73
99.28
6.55
18.51
95.69
22.64
95.35
12.36
97.25
G.2 Effeet of Prompt Engineering
We evaluate NegLabel with different prompt template on the ImageNet- 1kk benchmark, as shown in Table 23. The first four rows selected from the 80 prompt templates proposed by CLIP [8]. The last two rows are NegLabel designed prompt Cemplates.Acooreee to the results in the table, DualCnst significantly outperforms NegLabel in OOD detection performance across multiple prompts.
G.3
Vision Backbone
This section explores the performance of DualCnst using different CLIP vision encoders.
Table 24 presents the results for ImageNet- 1K (ID) with various CLIP vision encoders, including ViT-B/32 ViT-L/14 RN50*, RN50x4. RN50x16, and RN101. Across all tested encoders, DualCnst achieves the highest performance. Specifically, compared to VIT-B/16, using ViT-L/114 results in an improvement of 2.33% FPR95 and 0.37% in AUROC. Furthermore, DualCnst outperforms both zero-shot and fine-tuning methods in OOD detection, achieving the best results in terms of FPR95 and AUROC when utilizing ViT-L/14.
patch32
http://aee....eeeeeeee/////eeaaa aaaa patch44
http://ggtbub..omm/ppa//////1
33

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Table 23: Performance Comparison of Different Prompts. The bold indicates the best performance on each dataset.
Prompt
iNaturalist
Testure
Averuge
FPR95]
AUROCT
FPR95)
FFR55]
NogLabe
94.33
50.63
36.60
photo diabeb>
DualCre:
19.67
96.02
09.54
91.52
53.74
$7.56
71.29
79.20
49.81
38.72
a photo of many <label>
DialCre
NegLabel
71.03
56.51
90.30
79.96
79.23
86.67
77.04
76.48
83.77
71.73
80.52
70,95
85.41
photo of the clabels
aeyLabel DualCist
45.83
92.06
43.81
91.83
55.91
88.13
74.73
78.32
55,00
87.99
39.37
92.54
43.30
92.02
53.08
87.99
63.32
83.64
49.77
89.15
low resolution photo of <lahets
NegLabel DualCns:
38.07
$1.39
93.23
90.35
66.37
69.74
85.76
98.25
70.13
75.01
85.36
84.37
71.83
88.58
72.28
80.07
61.80
71.18
$6.85
83.81
<labets
NegLabel DualCrstt
28.5
52.77
91.15
41.93
92.40
49.13
89.65
68.65
62.61
53,12
88.90
NogLabel
1.91
DualCrs
0.98
99.70
18.13
95.66
34.77
91.81
91.73
21.45
94.72
Table 24: Prompt ensembling for text input using different backbones. The ID dataset is ImageNet- IK.
The black bold indicates the best performance.
OOD Dataset
Method
iNaturalist
Average
FPR95
AUROCT
EPR95
AUROCT
FPR95
AUROCT
FPR951
AUROCT
FPR95 AUROCT
Energy (VGT-B/16)
79.75
83.75
79.81
83.21
70.28
83.95
88.23
71.51
79.52
80.60
Maxl ngit (WT-R//1))
67.24
MCM (VTT-//16)
40.33
92.75
35.43
92.78
44,08
89.60
54,41
87.10
43,56
90.56
NegLabel (VII-B/16)
1.91
99.49
20.53
95.49
35.59
91.64
43,56
90.22
25.40
94,21
DualCust (VTT-B/16)
1.29
99.65
17.60
95.89
31,91
92.13
42.15
90.51
23.24
94,55
Energy (VIT-B/32)
87.20
79.77
MoxLogit (VIT-B/32)
79.45
83.75
68.89
84. 85
52.30
88.
79.88
75.29
70.13
83.12
MCM (VIT-B/32)
49.81
91.37
40.31
91.80
42.94
90.08
59.33
85.32
48.
89.64
NegLabel (VIT-B/32)
3.73
99.11
22.48
95.27
34.94
91.72
50.51
88.57
27.92
93.67
DuilCnst (VTT-B/32)
3.10
99.27
18.93
95.87
32.43
92.13
53.56
88.10
27.01
93.84
Energy (VIT-I/14)
79.20
85.29
76.83
84 68
65.62
87.59
87.23
70. 14
77.22
81.93
MaxLogit (VIT-L/14)
63.06
89.02
60.26
$8.20
52.51
80.66
73.96
64.12
85.23
MCM (ViT-L/14)
31.63
94.43
23.64
94.99
30.99
92.79
57.77
85.19
36.01
91.85
NegLabel (VTT-//14)
1.77
99.53
22.33
95.63
32.22
93.01
42.92
89.71
24.81
94.47
DualCnst (VIT-I/14)
1.33
99.70
19.54
96.06
26.55
93.72
42.48
89.87
22.48
94,84
Energy (RN50)
94.75
75.56
$1.39
78.68
92.98
90.10
MaxLogit (RN50)
86.45
81.21
74.56
84.31
78.15
81.10
86.45
74.61
81.40
80.31
MCM (RN50)
45.42
91.50
43.33
91.40
$5,92
86.73
55.92
86.68
50.15
89.08
NegLabel (RN50)
2.88
94.54
30.70
DualCust (RN50)
1.11
99.51
20.75
95.39
35.10
91.13
51.19
88,90
27.21
93.73
Energy (RN50x4)
85,55
81.25
80.13
84.81
68.84
85. .0.40
92.09
69.28
81.65
80.19
MaxLogit (RN50x4)
74.51
85.14
65.51
87.61
$8.86
87.26
84.47
74.81
70.84
83.70
MCM (RN50x4)
48.00
90.86
93.14
89.93
52.16
87.44
44.22
90.34
NegLabel (RN50x4)
2.14
99.49
17.61
96.25
30.67
92.59
50.71
88.72
25.28
94,26
DualCnst (RN50x4)
Energy (RN50x16)
73.44
86.95
65.15
58.97
73.74
83.97
84.43
76.11
74.19
$4.00
MaxLogit (RN50x16)
62.10
89.05
$2.35
90.45
64.74
85. $5.69
75.66
79.37
63.71
14
MCM (RN50x16)
43.02
91.69
34,24
93.27
46.96
89.27
51.93
87.94
44.04
90.54
NegLabel (RN50x16)
DualCnst (RN50x 16)
1.22
99.66
19.42
95.80
34.51
91.73
39.34
91.17
23.62
94.59
Energy (RN101)
97.82
87.81
$1.10
85.43
77.92
95.96
62.32
91.75
73.
MaxLogit (RN101)
92.65
77.38
74.77
84.67
75.96
81.30
90.90
68.66
83.57
78.00
MCM
NegLaaee (RN101)
2.35
99.42
21.84
95.45
41.98
90.08
53.95
87.68
30.03
93.16
DunlCnst (RN10I)
2.56
99.36
95.88
90.89
56.03
28.76
34

---

G.4 Generative Models
To evaluate the effectiveness of DualCnst, different generative models are employed. Table 25 compares the performance of Stable Diffusion v1.5* and v2.1 and Hunyuan-DiT' and sdxl-turbo" using ImageNet-Il Ik as the II) dataset. The performance improvements vary depending on the quality of generated synthetic images. Notably, sdxl-turbo achieves the highest synthetic image quality, leading to the most significant performance gain, whereas Stable Diffusion v1.5 yields the lowest-quality generations with minimal improvement compared other models. Nevertheless, both still surpass our baseline. In terms of computational efficiency, Hunyuan-DiT exhibits the longest generation time, while sdxl-turbo demonstrates the fastest inference speed. Stable Diffusion v1.5 and v2.1 show comparable computational latency.
Table 25: The impact randomness under different random seeds is examined, with ImageNet-Ik as the ID dataset. The black bold indicates the best performance.
OOD
Averuge
Generative Models
iNaturalist
SUN
Places
Texture
FPR95
AUROCT
FPR95,
AUROCT
FPR95,
AUROCT
FPR95L
AUROCT
FPR95,
AUROCT
stable diffusion v1.5
1.27
99.65
17.30
95.94
31.61
92.15
42.91
90.32
23.27
94.51
stable diftusion v2.1
1.42
99.63
17.93
95.86
32.22
92.13
39.11
90.97
22.67
94.65
Hunyuan-DiT
17.55
95.95
31.24
92.18
42.46
90.37
23.19
94.53
stable ditfusion XL-Turbo
1.25
99.66
16.91
95.94
30.73
92.17
39.20
90.98
22.02
94.69
G.5 Encoder Layer
An ablation study was conducted to evaluate the effectiveness of DualCnst using different layers of CLIP's ViT-B/16 encoder. As demonstrated Table 26, we present the results of SDXL-Turbo under various layer combinations: (Ist, 2nd, 3rd), (4th, 5th, 6th). (7th, 8th. 9th), (9th, 10th. 1 1th). (3rd, 6th, 9th), and layers. For each combination, multiple weighting coefficients w (0.05, 0.1, 0515, 0.25) were systematically investigated to identify the optimal balance between pixel-level fidelity and semantic consistency.
The results indicate that an equal weight distribution is not necessarily optimal across different layers. For instance, when using the (Ist, 2nd, 3rd) layers, setting w 0.05 yields the best performance, as the lower layers primarily capture edge-related features, requiring stronger semantic guidance. In contrast, for the (9th, 10th, 1 1t) layers, which encode more localized details such as the fur and ears of wolf or the stripes of zebra assigning a higher weight to visual features leads to improved performance within the DualCnst framework.
G.6 Fusion Parameter of Dual Consistency
This section presents a comprehensive ablation study on the fusion parameter in the dual consistency method. Experiments are conducted using ImageNet- 1k. CIFAR-IO 10, and CIFAR-100 ID datasets, with NNtuurllst, SUN, Places, and Textures serving as OOD datasets. Additionally, experiments are performed by alternately designating ImageNet-10 10 and ImageNet-20 as ID and OOD datasets.
All experiments utilize the ViT-B/16 visual encoder with selected layers (7th, 8th, 9th) and a fixed weight parameter of w 0.15. As shown in Table 29, the optimal value varies across different OOD datasets for ImageNet- lk. Specifically, the best results are obtained with a = 0.1 for SUN and Places, and 0.2 for iNaturalist and Textures. In the main results, select 0.2 for presented results. Notably, when a = 0, DualCnst reduces to Negl.bell
Table 27 and Table 28 present the results for the CIFAR datasets, where DualCnst consistently outperforms NegLabel. Furthermore, as shown in Table 30, when the and OOD datasets exhibit semantic similarities, integrating DualCnst leads to notable performance improvements.
2-Diffusers
35

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Table 26: Using SUXL-Turbo different encoder layers and weights. The ID class labels the same as ImageNet-lk. The black bold indicates the best performance.
OOD Dataset
Average
Layer
INaturalist
SUN
Places
Texture
FPR95
AUROC
FPR95
AUROCT
FPR95
AUROCT
FPR95
AUROCT
FPR98,
AUROCT
0.05
1.29
99.67
17.49
95.86
31.36
92.05
40.82
90.83
22.74
94.60
0.10
1.29
99.67
17.46
95.82
31.51
92.01
41.52
90.74
22.95
94.56
(1st. 2nd. 3rd)
17.59
90.63
0,25
1.30
99.65
18.44
95.56
33.26
91.67
43.42
90.25
24.11
94.28
0.05
1.24
99.67
17.49
95.90
31.23
92.11
39.75
90.96
22.43
94.66
(4th, 5th, 6th)
0.10
1.21
99.68
17.42
95.89
31.09
92.13
39.38
90.98
22.27
94.67
0,25
1,17
99.65
18.33
95.67
32.68
91.89
39.73
90.65
22.98
94,46
0.05
1.25
99.67
17.23
95.91
30.86
92.11
39.29
91.02
22.16
94.68
(7th, 8th, 9ch)
0.10
1.23
99.67
17.33
95.92
30.91
92.14
38.94
91.14
22, 10.10
94.72
91.73
0.05
1.27
99.66
17.03
95,92
30.76
92.12
39.61
90.94
22.17
94.66
11th)
0.10
1.25
99.66
16.91
95.94
30.73
92.17
39.20
90.9%
22.02
94.69
0.15
1.23
99.66
16.79
95.96
30.74
92,20
39.13
91.00
21.97
94.70
0.25
1.24
0.05
1.24
99.67
17.35
95,90
31.10
92.11
39.80
90.94
22.37
94.65
(3rd, 6th, 9th)
0.10
1.23
99.67
17.26
95.92
31,06
92.14
39.77
90.97
22.33
94.66
0.15
1.22
99.67
17.01
95.91
31.12
92.15
39.36
90.97
22.18
94.68
0.25
0.01
1.25
99.67
17.36
31,07
92.09
39.80
90.95
22.37
94.64
0.02
1.24
99.67
17.43
95.90
31,16
92.11
39.91
90.96
22.44
94.66
layer
0.05
1.19
99.68
17.05
95.88
31.13
92.11
39.54
90.
22.23
94.66
Table 27: An ablation study the fusion parameter for cifar10. The black bold indicates the best performance.
OOD Dataset
Average
Q
NNaturalist
SUN
Places
Texture
EPR95L
AUROCT
FPR95
AUROCT
EPR95L
AUROCI
FPR95L
AUROCT
FPR951
AUROCT
0.55
99.84
23.31
95.5
38.7
91.53
19.33
96.65
20.47
95.88
0.1
0.35
99.85
17.57
96.46
30.33
93.10
12.75
97.59
15.25
96.75
0.2
0.33
99.85
15.38
96.88
26.43
93.82
10.80
97.93
13.23
97.12
0.3
0.42
99.83
15.23
97.07
25.46
94.17
10.55
98.00
12.91
97.27
25.33
12.92
97.29
0.5
0.67
99.75
15.75
97.10
25.55
94,31
11.01
97,84
13.25
97.25
0.6
0.89
99.67
16.18
97.04
26.17
94.24
11.49
97.71
13.68
97.17
0.7
1.39
99.55
16.74
96.95
26.89
94.14
11.95
97.59
14,24
97.06
0.8
1.97
99.38
17.14
96.86
27.49
94.03
12.27
97.47
14.72
96.94
0.9
3.13
99.16
17.71
96.76
27.87
93.91
12.68
97.37
15.35
96.80
4.74
98.88
18.27
96.66
28.26
93.79
13.14
97.26
16.10
96.65
Table 28; An ablation study on the fusion parameter a for cifar100. The black bold indicates the best performance.
OOD Datasel
iNaturalist
SUN
Places
Texture
Average
EPR95L
AUROCT
AUROCT
EPR95L
AUROCT
AUROCT
EPR951,
AUROCT
13.95
96.47
86.61
69.04
91.5
62.08
70.6
80.26
65.66
76,96
0.1
11.63
97.18
83.36
70.68
89.27
64.39
61.79
83.27
61.51
78.88
0.2
9.55
97.72
80.42
72.56
87.11
66.52
$7.20
84.67
58.57
80.37
0.4
6.40
98.42
72,84
76.14
79.90
70.26
55.48
84.72
53.65
82.39
75.92
0.6
4,40
98.85
63.12
79.45
72.40
73.70
55.12
83.74
48.76
83.94
0.7
3.80
99.00
58.51
80.94
68.65
75.30
$4.96
83.38
46.48
84.66
0.8
3.33
99.11
54.69
82.25
65.38
76.75
54.73
83.10
44.53
85.30
0.9
2,97
99.18
51.49
83.35
62.70
78.00
$5.12
82.86
43.07
85.85
86.30
36

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Table 29: An ablation study on the fusion parameter for ImageNet-Ik, The black bold indicates the best performance.
OOD Dataset
iNaturalist
Places
Texture
Average
FPR95)
AUROCT
FPR95L
AUROCT
FPR95)
AUROCT
FPR95
AUROCT
FPR95
AUROCI
0
1.91
99.49
20.53
95.49
35.59
91.64
43.56
90.22
25.40
94,21
0.1
1.23
99.66
16.79
95.96
30.74
92.20
39.13
91.00
21.97
94.70
0.2
0.98
99.70
18.13
95.66
31.77
91.81
34,91
91.73
21.45
94.72
0.3
1.00
99.68
21.01
95.13
33,80
91.17
34,06
91.80
22.47
94.45
0.4
1.27
99.63
24.70
94.42
36.79
90.39
34.31
91.64
24.27
94,02
0.5
1,63
99.35
28.39
93.61
39.76
89,34
35.76
91.34
26.39
93.51
0.7
2,80
99.32
35.10
91.87
44.95
87.82
39.27
90.50
30,53
92.38
0.8
3.63
99.18
38.41
90.98
46.84
86.98
41.03
89.99
32.48
91.78
0.9
4.33
99.02
40.90
90.13
48.76
86.16
42.23
89.44
34.05
91.19
5.12
98.85
43.32
89.32
50.37
85.38
43.63
88.86
35.61
90.60
Table 30: ablation study on the parameter a, alternating ImageNet10 and ImageNet20 as ID and OOD datasets. The black bold indicates the best performance.
ID
ImageNet-10 ImageNet-20
TeageNet-20 IeageNet-10
Average
0OD
FPR95,
AUROCTT
FPR95
AUROCT
FPR95,
AUROC
0.0
5.10
98.86
17.60
97.04
11.35
97.95
0.1
2.20
98.77
13.60
97.61
8.15
98.31
0.2
0.3
4,00
98.89
18.60
97.06
11.30
97.98
044
98.72
0.5
5.30
98.54
18.00
97.12
11.65
97.83
0.6
6.40
98.33
17.80
97.16
12.10
97.75
0.7
8.60
16,80
12.70
97.64
0.8
10.30
97.78
15.40
97.41
12.85
97.60
13.10
97.14
48.00
97.65
30.55
97.40
G.7 Impact of Noise and Distribution Shifts in Synthetic Images on OOD Detection Performance
While synthetic images may contain noise, style variations, or artifacts that could potentially mislead OOD detection models, our score function incorporates multi-level features from pixel to semantic levels. Thus, even when pixel-level features affected by such deviations, language-level features remain effective. To validate this, we generated oil painting-style images simulate style shifts. As shown in Table 31, our method maintains robust performance despite this variation.
Table Experimental comparison under generated image style shifts. ID dataset: ImageNet-Ik 1k. The black bold indicates the best performance.
0OD Dataset
Average
Image Style (Method)
INaturalist
SUN
Places
Texture
FPR95
AUROCC
FPR951
AUROCT
FPR95)
AUROCT
FPR95),
AUROCT
FPR95),
AUROCT
Natural Images (DualCast)
1.29
99.65
17.60
95.89
31.91
92.13
42.15
90.50
23.24
94.55
Oil Painting Images (DualCnst)
1.31
99.65
17.73
95.79
32.18
92.00
44.10
90.04
23.83
94,37
NegLabel
G.8 The Randomness of Stable Diffusion
An ablation study is conducted to assess the impact of Stable Diffusion's randomness on the effec- tiveness of DualCnst in generating synthetic images. Specifically, synthetic images are generated using three distinct random seeds with SD1.5 and SDXL--Turo, followed by performance evaluation on ImageNet- Ik. The experimental configuration employs the ViT-B/16 visual encoder with the (9th, 10th. 11th) layer combination, alongside fixed hyperparameters w 0.1 and 0.1.
37

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As demonstrated in Tables 32 (SDXL-Turbo) and 33 (SD1.5), the detection performance exhibits remarkable consistency across varying random seeds, demonstrating that the generative randomness inherent in Stable Diffusion exerts negligible influence on the operational efficacy of DualCnst.
Table 32: Evaluate SDXL--Turbo's performance variations under different random seeds using ImageNet- Ik the ID dataset. The black bold indicates the best performance.
Dataset
Random
iNaturalist
SUN
Places
Texture
Average
FPR95
AUROC1
FPR95
AUROC1
FPR95
AUROC
FPR95
AUROC1
FPR95
AUROC
random 0.98
99.70
18.13
66
31.77
91.81
34.91
91.73
21.45
94.72
random
1.25
99.66
16.62
95.96
30.84
92.19
39.29
91.02
22.00
94.71
random 1.26
99.56
16.74
96.00
30.82
92.23
39.70
90.89
22.13
94.69
Table 33; Evaluate Stable Diffusion v1.5's performance variations under different random seeds using ImageNet- Ik the ID dataset. The black bold indicates the best performance.
OOD Dataset
Averuge
Random
iNaturalist
SUN
Places
Texture
EPR95
AUROCT
FPR95
AUROCT
EPR95
AUROC
AUROCT
FPR95,
AUROCT
random 1
1.27
99.65
17.30
95.94
31.61
92.15
42.91
90.32
23.27
94.51
random 2
1.29
99.65
17.60
95.89
31.91
92.13
42.15
90.51
23.24
94.55
mandom 3
1.26
99.66
18.16
95.64
32.26
91.90
41.90
90.33
23.39
94.38
Table 34: Additional ablation studies on score functions. The bold indicates the best performance on each dataset.
OOD Datasel
Average
Score Funtion
NNaturalist
SUN
Places
Texture
FPR95
AU AUROCT
FPR95
AUROCT
FPR95
AUROCT
FPR951
AUROCT
FPR951
AUROCT
SMAX
100.00
83,00
100.00
82.16
100.00
80.62
100.00
80.28
100.00
81.51
Stargy
1.98
99.42
20.26
95.52
35.54
91.49
45.69
89.96
25.87
94,10
SMudenii
6.26
98.58
29.54
43.18
89.55
50.78
87.95
32.44
92.47
SparChs
0.98
99.70
18.13
95.66
31.77
91.81
34.91
91.73
21.45
94.72
G.9
Open- Vocablocayylrry Scaling via OOD Label Clustering
We simulate open-vocabulary conditions by clustering 10,145 fine- ine-grained OOD categories into 136 coarse clusters and compare performance with the unclustered (10,000-label) setting. Heavy clustering reduces label granularity, which is expected to weaken the representational diversity of negative exemplars.
As shown in Table 35, clustering (coarsening) the OOD label vocabulary leads to a pronounced degradation across all benchmarks: the average FPR95 nearly doubles (from 21.45 to 41.78), while AUROC drops by more than points on average. The decline especially severe on SUN and Places. where visual and semantic diversity among categories is high and coarse labels (c.g.. "indoor scenes")) fail to capture subtle inter-class distinctions.
Table 35: Effect of clustering OOD labels (FPR95L AUROC1), The black bold highlights the best performance in each column.
OOD Dataset
Average
Method
iNaturalist
SUN
Places
Texture
EPR95
AUROCT EPR95]
AUROCT
FPR95,,
AUROCT
EPR951 AUROCT EPR951 AUROC
DualCnst (10,000 labels)
0.98
99.70
18.13
95.66
31.77
91.81
34,91
91.73
21.45
94.72
DunlCnst (clustered: 136 clusters)
12.13
97.58
54.02
88.48
57.06
87.02
43,92
89.40
41.78
90.62
These results empirically validate that fine-grained negative labels are critical for robust open- vocabulary OOD detection. Each specific label contributes localized semantic anchor and distinct
38

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visual prototype. enriching the negative space used for comparison. When label granularity is reduced, semantic coverage shrinks, increasing the chance that semantically adjacent OOD samples are misclassified as ID. This observation aligns with our dea-ddeenneence analysis in Section C.2, where diverse yet weakly correlated signals jointly reduce detection variance. Practically, this motivates maintaining sufficiently large and fine-grained OOD label bank for synthetic-exemplar based zero-shot detection.
G.10 Comparison with LMD CIFAR
We further compare DualCnst (training-fret with LMD, a training-based diffusion reconstruction approach, on CIFAR benchmarks using ROC-AUC as the evaluation metric. Results are summarized in Table 36.
Table 36: ROC-AUC on CIFAR datasets. Bold indicates the higher ROC-AUC for each ID/OOD configuration.
Method
ID
OOD
ROC-AUCT
LMD
CIFARIO
CIFAR100
0.607
DualCnst (ours)
CIFARIO
CIFARI00
0.900
LMD
CIFARI00
CIFARIO
0.568
DualCnst (ours)
CIFAR100
CIFARIO
0.661
DualCnst substantially outperforms LMD on these CIFAR OOD settings, with particularly large gain of +29.3 percentage points when CIFARIO is ID and CIFARI00 is OOD (Table 36). This suggests that DualCnst's s explicit multimodal consistency-linkigg text-derived semantics and vi- sual exemplars- produces stronger discriminative cues than the implicit generative reconstruction objective used in LMD. Moreover, since DualCnst requires no training or fine-tuming its superior performance demonstrates the effectiveness of leveraging pre-trained multimodal models for zero- shot OOD detection. In contrast, LMD's 's diffusion reconstruction tends to blur semantic boundaries and overfit to ID image priors, reducing generalization to unseen categories.
G.11 Controlled Semantic Mixing: Dog-House Hybrids
To stress-test the visual branch, synthesize controlled hybrids that mix IDD semantics (dog) and OOD semantics (house). Table 37 reports average text and image similarities to the canonical II) dog exemplar.
Table 37: Similarity statistics under dog-house composition. Values are average cosine similarities to the ID "dog" exemplar.
Setting
Text Sim (ID dog)
Image Sim (ID dog)
ID dog
0.245
0.575
OOD house
0.178
0,461
house with dog
0.235
0.493
Interpretation. The hybrid "house with a dog" shows an intermediate behaviour: its text similarity to the 'dog' label (0.235) is close to the pure ID 'dog" (0.245), reflecting semantic overlap, while its image similarity (0.493) is substantially lower than the ID dog (0.575) but higher than the pure house (0.461). This cetrration-highar text similarity but reduced visual
-illustrssss why multimodal fusion helps: text-only scoring risks classifying hybrids as ID (semantic match), whereas adding image-space constraints lowers their aggregated score and allows the detector to flag them as OOD. In other words, fusing weakly dependent cues recovers robustness semantic mixing and reduces aalse-positive acceptance of visually atypical but semantically related inputs.
39

---

G.12
Score Function
We present the specific form of the score function designed in the ablation study. They are SMAX. Eneegy and MaxxLggi Firstly, we review the definition of the fused visual-texs cosine similarity & as:
8,(x) = a Si.,ng (x) + (1 - a.) 8i,uex (x)
(9)
where
Si.ing (x) Σ
Wi S iiim
(x)
1=1
with
I (x) (1 ( (x,)
kiim (x)
8, E X
(10)
[|](|(x)|| -x,(()
and
I(x) -T(t.)
8 Si.tex (x)
(11)
|[((x]] (1,(ll)
The specific form of SMAX as follows:
K
max 8 (E(K
max -_[K+
85
SMax(x; 3id U good XTT,I)
(12)
2,00
max (E(1,K]
max [E(,
max JE[K+I,K+M
83-
SMAX indicates that if the šy (j E [ of an input sample is larger than the & (i E [L. K]). this sample is recognized to be an OOD sample. This implies that the maximum similarity observed between the input sample and any OOD visual-text similarity exceeds the similarity between the input sample and any ID visual-text similarity. Otherwise, the input sample is evaluated based on the maximum softmax probability.
Similarly, Saarrgy and SMaxt agin are modifications of the Energy and MaxLogit metrics, respectively, incorporating visual-text similarity into their secondary components.
K
K+M
SEnagy (x; Sid yooo P.T.I) -T log
M6x/T
log
Σ c K+1
(13)
SMaxLogie (x; yoi y 2t T, max (x)
max
(z(x).
(14)
=E(K+L,
Table 34 presents the detailed experimental results ImageNet- 1k (ID).
40


Reviews:
[{"review_id": "QjoTW55yN0", "reviewer": "Reviewer_XUrX", "summary": "This paper introduces DualCnst, a training-free framework for zero-shot out-of-distribution (OOD) detection using vision-language models (VLMs). The core idea is to augment text-based OOD detection (e.g., CLIP similarity to ID/OOD labels) with visual consistency derived from synthetic images generated for these labels via Stable Diffusion. It fuses semantic and visual signals to improve detection accuracy, achieving state-of-the-art results on benchmarks like ImageNet and CIFAR.", "questions": "1. Generating images for 10K OOD labels takes ∼55 min (SDXL-Turbo). How would DualCnst handle open-vocabulary settings with exponentially larger label sets? Could clustering or diversity sampling reduce the synthetic image burden without degrading performance?\n2. The theoretical analysis assumes i.i.d. scores across modalities, but features from different encoder layers are likely correlated. How does this correlation affect variance reduction claims? Could you validate this empirically (e.g., measure score covariance)?\n3. Experiments on remote sensing/UCM show promise, but medical/satellite imagery may have larger domain gaps. Have you tested on highly structured OOD data and how does style transfer (Table 24) extend to such cases?\n4. How does DualCnst quantitatively compare to training-free diffusion methods (e.g. LMD) in accuracy and inference speed? Table 2 claims advantages but lacks metrics.", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths\n1. Rigorous experiments across diverse benchmarks (far/near OOD, domain shifts) demonstrate consistent SOTA results. Ablation studies (e.g., layer selection, generative models, fusion parameters) are thorough.\n2. Practical advance for zero-shot OOD detection, with non-trivial gains (e.g., 3.95% FPR95 improvement on ImageNet). The training-free, plug-and-play design enhances accessibility.\n3. Novel integration of text-image dual consistency for OOD scoring. Multi-layer visual similarity aggregation is a clever extension of CLIP.\n\nWeakness\n1. Heavy reliance on synthetic images risks performance degradation if generation fails (e.g., artifacts, style mismatches). Theoretical analysis assumes i.i.d. similarity scores across modalities, which may not hold for correlated encoder features.\n2. Moderate novelty over NegLabel (text mining) and prior generative OOD methods (e.g., LMD). Computational costs (Section F.3) may limit real-time deployment despite optimizations.", "was_revised": true, "final_justification": "Thanks for the rebuttal — most of my concerns are addressed, and I will keep my positive score.", "rebuttal": "Response to Reviewer XUrX\n\nWe thank the reviewer XUrX for the valuable feedback. We addressed all the comments. Please find the point-to-point responses below. Any further comments and discussions are welcomed!\n\n**W1:&#x20;**&#x48;eavy reliance on synthetic images risks performance degradation if generation fails (e.g., artifacts, style mismatches). Theoretical analysis assumes i.i.d. similarity scores across modalities, which may not hold for correlated encoder features.\n\n**Reply:&#x20;**\n\nWe appreciate this insightful comment.\n\n1. To mitigate sensitivity to synthetic quality, we adopt a dual-pronged strategy. First, DualCnst integrates multi-level visual features (Eq. 2) through adaptive weighted summation ($w_l$), enhancing robustness to noise while preserving semantic fidelity. Second, we utilize high-quality generative models—specifically, Stable Diffusion XL-Turbo—which reduce generation artifacts via optimized latent diffusion. These limitations and mitigation strategies are discussed in Appendices D.1 and D.3.\n\n2. Regarding the i.i.d. assumption, we agree it represents an idealized case. As discussed in Appendix D (Limitation III), we show that as long as the similarity scores are weakly correlated (a condition under which the Central Limit Theorem (CLT) still holds), the variance reduction trend remains valid. Furthermore, we empirically measured pairwise Pearson correlations between the similarity scores from different modalities and observed that the correlations are consistently low (e.g., < 0.3), which supports the applicability of the weak correlation assumption in our setting.\n\nTable A: Pairwise Pearson correlations between the similarity scores from different modalities.\n\n|           | img\\_sim | text\\_sim |\n| --------- | -------- | --------- |\n| img\\_sim  | 1.00     | -0.02     |\n| text\\_sim | -0.02    | 1.00      |\n\nTable B: Pairwise Pearson correlations between the similarity scores from different layers.\n\n|             | text\\_sim | layer1\\_sim | layer5\\_sim | layer9\\_sim |\n| ----------- | --------- | ----------- | ----------- | ----------- |\n| text\\_sim   | 1.00      | 0.00        | 0.01        | 0.20        |\n| layer1\\_sim | 0.00      | 1.00        | 0.27        | 0.13        |\n| layer5\\_sim | 0.01      | 0.27        | 1.00        | 0.30        |\n| layer9\\_sim | 0.20      | 0.13        | 0.30        | 1.00        |\n\n**W2:** Moderate novelty over NegLabel (text mining) and prior generative OOD methods (e.g., LMD). Computational costs (Section F.3) may limit real-time deployment despite optimizations.\n\n**Reply:&#x20;**\n\nWe thank the reviewer for their constructive feedback. We address the concerns regarding novelty and computational cost below：\n\n**On Novelty:&#x20;**&#x44;ualCnst introduces a new paradigm for zero-shot OOD detection by unifying semantic-textual alignment with visual-consistent synthesis. Its core innovations include:\n\n* **Multimodal Consistency Framework:** Our method integrates semantic similarity (textual) and visual similarity (image-based) between test samples and synthesized ID/OOD labels. This hierarchical fusion of modalities balances pixel-level and semantic-level representations, overcoming the limitations of prior unimodal approaches such as NegLabel and LMD.\n\n* **Theoretical Analysis:** We provide a theoretical justification showing that leveraging multimodal label spaces (i.e., text + generated images) can reduce false positive rates under mild conditions. This demonstrates that incorporating auxiliary modalities is not just empirically effective but also theoretically grounded.\n\n**On Computational Cost:**\n\n&#x20;  We acknowledge the computational burden of generative models, and we take concrete steps to mitigate it:\n\n* We adopt **accelerated generative backbones** such as Stable Diffusion XL-Turbo.\n\n* All synthetic exemplars are **pre-generated offline** prior to inference. Since image generation is done once per label set and reused across all test instances, the inference-time overhead is negligible. This makes our approach practical and scalable to large datasets.\n\nThese optimization strategies and limitations are discussed in **Appendix D** of the paper.\n\n**Q1:&#x20;**&#x47;enerating images for 10K OOD labels takes ∼55 min (SDXL-Turbo). How would DualCnst handle open-vocabulary settings with exponentially larger label sets? Could clustering or diversity sampling reduce the synthetic image burden without degrading performance?\n\n**Reply:**\n\nWe thank the reviewers for their valuable feedback. To simulate open-vocabulary settings, we conducted cluster sampling on out-of-distribution (OOD) labels, aggregating 10,145 original categories into 136 clustered categories. Benchmarked against the baseline, Table C demonstrates that the nearly 75-fold reduction in category granularity led to a marked performance deterioration. In practice, our methodology consistently employs sampling of 10,000 OOD labels regardless of the in-distribution (ID) dataset used.\n\nTable C: Comparison of clustering performance of OOD labels.\n\n| Method             | iNaturalist   | SUN            | Places         | Texture        | Average       |\n| ------------------ | ------------- | -------------- | -------------- | -------------- | ------------- |\n|                    | FPR95/AUROC   | FPR95/AUROC    | FPR95/AUROC    | FPR95/AUROC    | FPR95/AUROC   |\n| DualCnst           | 1.23 / 99.66  | 16.79 / 95.96  | 30.74 / 92.20  | 39.13 / 91.00  | 21.97 / 94.70 |\n| DualCnst （cluster） | 12.13 / 97.58 | 54.02 / 88.48  | 57.06 / 87.02  | 43.92 / 89.40  | 41.78 / 90.62 |\n\n**Q2:** The theoretical analysis assumes i.i.d. scores across modalities, but features from different encoder layers are likely correlated. How does this correlation affect variance reduction claims? Could you validate this empirically (e.g., measure score covariance)?\n\n**Reply:**\n\nWe thank the reviewer for highlighting this important point. Indeed, correlated modalities may reduce the effective variance reduction gain compared to the ideal i.i.d. case. In response, we have conducted an empirical study where we:\n\n(1) Compute the covariance matrices of similarity scores across modalities (Table D) and the Pearson coefficient (Table A); and\n\n(2) compare the empirical variance of the aggregated score under different modality combinations (Table F).\n\nOur results show that, although some degree of correlation exists, the aggregated score still demonstrates monotonically decreasing variance as the number of modalities increases. This empirical observation supports the robustness of the variance reduction behavior even under moderate correlation.\n\nTable D: covariance matrices of similarity scores across modalities.\n\n|           | img\\_sim  | text\\_sim |\n| --------- | --------- | --------- |\n| img\\_sim  | 1.68e-02  | -8.53e-05 |\n| text\\_sim | -8.53e-05 | 1.33e-03  |\n\nTable E: covariance matrices of similarity scores across layers.\n\n|             | text\\_sim | layer1\\_sim | layer5\\_sim | layer9\\_sim |\n| ----------- | --------- | ----------- | ----------- | ----------- |\n| text\\_sim   | 1.33e-03  | 2.03e-05    | 3.92e-04    | 5.40e-04    |\n| layer1\\_sim | 2.03e-05  | 1.39e-02    | 3.16e-03    | 1.14e-03    |\n| layer5\\_sim | 3.92e-04  | 3.16e-03    |  2.21e-02   | 3.34e-03    |\n| layer9\\_sim | 5.40e-04  | 1.14e-03    | 3.34e-03    | 2.51e-02    |\n\nTable F: empirical variance of the aggregated score under different modality combinations.\n\n|                                     | empirical variance (×1e-3) |\n| ----------------------------------- | -------------------------- |\n| Only Text score                     | 0.7110                     |\n| Only Image score                    | 0.3691                     |\n| Fusion score (1 intermediate layer) | 0.3553                     |\n| Fusion score (2 intermediate layer) | 0.3366                     |\n| Fusion score (3 intermediate layer) | 0.3274                     |\n| Fusion score (4 intermediate layer) |  0.3038                    |\n| Fusion score (5 intermediate layer) | 0.2717                     |\n| Fusion score (6 intermediate layer) | 0.2567                     |\n\n**Q3:** UCM gaps: DualCnst generalization to medical/satellite/structured OOD validated?\n\n**Reply:**\n\nWe appreciate the reviewers' suggestions. Using Chxpert\\[1] as the ID data and sampling 10,000 examples from PubMedVision\\[2] as OOD data, our approach maintains strong performance as demonstrated in Table G.\n\nTable G: Performance comparison on medical imaging data.\n\n| Method   | Media data   |\n| -------- | ------------ |\n|          | FPR95/AUROC  |\n| DualCnst | 0.00 & 99.97 |\n| NegLabel | 0.11 / 99.92 |\n\n\\[1] Irvin et al. Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison. In AAAI, 2019.\n\n\\[2] Chen et al. Huatuogpt-vision, towards injecting medical visual knowledge into multimodal llms at scale. In arXiv,2024.\n\n**Q4:** Quantitative acc\\&spd: DualCnst vs. training-free (LMD)? Table 2 claims lack metrics.\n\n**Reply:&#x20;**\n\nWe appreciate the reviewer's comment. We have now supplemented the comparison of accuracy with the LMD method based on experimental results from the LMD paper（Table H). However, LMD is a trainable OOD detection approach requiring significant computational resources. Each training run takes approximately 224 hours. Due to time constraints and the lack of official checkpoints, we are currently unable to include detailed computational cost analysis. We will include this information in the appendix once the reproduction process is complete.\n\nTable H: Performance comparison with LMD on CIFAR dataset.&#x20;\n\n| Method        | ID       | OOD      | ROC-AUC |\n| ------------- | -------- | -------- | ------- |\n| LMD           | CIFAR10  | CIFAR100 | 0.607   |\n| DualCnst(our) | CIFAR10  | CIFAR100 | 0.900   |\n| LMD           | CIFAR100 | CIFAR10  | 0.568   |\n| DualCnst(our) | CIFAR100 | CIFAR10  | 0.661   |"}, {"review_id": "sFZh4UxYua", "reviewer": "Reviewer_k9F4", "summary": "The paper introduces DualCnst, a novel framework for zero-shot out-of-distribution detection in vision-language models. Traditional OOD detection methods primarily rely on semantic similarity between input images and textual labels, often ignoring visual cues. DualCnst addresses this limitation by integrating text-image dual consistency, combining semantic similarity with visual similarity to synthesized images generated from both in-distribution (ID) and OOD labels using Stable Diffusion. The framework is training-free and does not require access to real ID images, making it suitable for open-world scenarios. Extensive experiments on diverse benchmarks demonstrate state-of-the-art performance, with significant improvements over existing methods.", "questions": "N/A", "limitations": "N/A", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 4, "significance": 4, "originality": 3, "strengths_and_weaknesses": "Strengths\n1. Proposes a dual-consistency framework that leverages both semantic and visual similarities, a unique approach in zero-shot OOD detection.\n2. Using similarity between images is a significant supplement for clip-based ood detection to dig the pretrained knowledge\n3. This is a solid work with comprehensive experiments.\n\nWeaknesses\n1. While Table 24 demonstrates robust performance under significant distribution shifts, this primarily evaluates far-OOD cases where the distribution gap between ID and OOD data is inherently large. To better assess the method’s sensitivity to subtle distribution shifts, it would be valuable to include experiments on near/hard-OOD scenarios. This would provide a more comprehensive understanding of the framework’s generalizability, particularly in challenging cases where ID and OOD samples share high semantic overlap.\n2. As shown in Appendix G.6, the fusion parameter $\\alpha$ is sensitivity, with optimal values varying across datasets. This suggests that the method’s performance may depend on careful hyperparameter tuning, which could limit its plug-and-play applicability in diverse real-world settings. \n3. Table 22 reveals that visual similarity (image-to-image) plays a more significant role than semantic similarity (image-to-text) in the fused scoring function. This observation warrants further discussion.", "was_revised": true, "final_justification": "Authors have addressed my concerns, so I keep my score.", "rebuttal": "Response to Reviewer k9F4\n\nWe thank the reviewer k9F4 for the valuable feedback. We addressed all the comments. Please find the point-to-point responses below. Any further comments and discussions are welcomed!\n\n**W1:&#x20;**&#x57;hile Table 24 demonstrates robust performance under significant distribution shifts, this primarily evaluates far-OOD cases where the distribution gap between ID and OOD data is inherently large. To better assess the method’s sensitivity to subtle distribution shifts, it would be valuable to include experiments on near/hard-OOD scenarios. This would provide a more comprehensive understanding of the framework’s generalizability, particularly in challenging cases where ID and OOD samples share high semantic overlap.\n\n**Reply:**\n\nWe thank the reviewers for their suggestion. We have supplemented the robustness evaluation against near-out-of-distribution (near-OOD) samples under style shift conditions. As evidenced in Table C, our method maintains superiority over baseline approaches.\n\nTable C: Experimental comparison of near-OOD under generated image style shift.&#x20;\n\n| Method                           | ID / OOD | ImageNet-10 / ImageNet-20 | ImageNet-20 / ImageNet-10 |\n| -------------------------------- | ----- | ---------------------- | ---------------------- |\n|                                  |       | FPR95/AUROC            | FPR95/AUROC            |\n| Oil Painting Images (DualCnst) ） |       | 2.70 / 99.08           | 13.00 / 96.82          |\n| Natural Images (DualCnst)        |       | 2.20 / 98.96           | 12.20 / 97.44          |\n| NegLabel                         |       | 5.10 / 98.86           | 17.60 / 97.04          |\n\n**W2:&#x20;**&#x41;s shown in Appendix G.6, the fusion parameter $\\alpha$ is sensitivity, with optimal values varying across datasets. This suggests that the method’s performance may depend on careful hyperparameter tuning, which could limit its plug-and-play applicability in diverse real-world settings.\n\n**Reply:**\n\nWe acknowledge the reviewer's comments regarding parameter $\\alpha$. We emphasize that a fixed α value was used consistently for experiments sharing identical in-distribution (ID) datasets, with values naturally bounded within the compact interval \\[0,1]. As further demonstrated in Appendix G.6, our approach maintains superior performance over baselines even when $\\alpha$ is uniformly fixed at 0.1 across all experimental settings.\n\n**W3:&#x20;**&#x54;able 22 reveals that visual similarity (image-to-image) plays a more significant role than semantic similarity (image-to-text) in the fused scoring function. This observation warrants further discussion.\n\n**Reply:**\n\nWe appreciate the reviewers' comments. As formally defined in Equation 5 of our paper, parameter $\\alpha$ specifically denotes the weight assigned to visual similarity."}, {"review_id": "nl53qfHZlW", "reviewer": "Reviewer_ehFX", "summary": "### Background\n- Recent works use CLIP to achieve great performance for zero-shot OOD detection, by calculating similarities between input images and textual labels. \n- Most existing methods focus on expanding the label space in the text domain, ignoring complementary visual cues.\n### Method\n- The authors introduce DualCnst\n\nBased on NegLabel, DualCnst also constructs a set of negative textual labels. \n\nThen, the authors generate synthetic images from both ID and mined OOD textual labels using a text-to-image  model.\n\nEach test image is evaluated based on its semantic similarity to class labels and its visual similarity to the synthesized images, and OOD samples are identified based on the similarities.\n\n### Result\n- DualCnst achieves state-of-the-art performance across diverse OOD benchmarks.", "questions": "Please see the weaknesses section.", "limitations": "Yes.", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "## Strengths\n- The paper is well-written and easy to understand.\n- The method is easy to implement.\n- The experiments are comprehensive, covering different OOD benchmarks and model backbones (in the supplementery materials).\n\n## Weaknesses\n### Major\n- There are a few hyperparameters to tune in DualCnst, such as which layers to use, the weight assigned to each layer, and the fusion weight of similarity scores. This may limit the practical value of DualCnst.\n- For each textual label, only one image is synthesized and the similarity between the test input and the synthetic image is calculated for OOD detection. \n\n(1) I'm wondering if one image is sufficient to represent a class. For example, if the class label is \"dog\", there should be many different images of a dog. One synthetic image is just one case of that, and may lead to low similarity to other dog images.\n \n(2) When using generative models to produce images, we cannot guarantee that the generated images contain only the intended category. For example, suppose the ID class is \"dog\" and the OOD class is \"house\". When generating OOD images, the model generates a house, but due to randomness or inherent bias, it might also include a dog next to the house. This can lead to incorrect predictions during visual similarity comparisons, as images containing dogs (ID) may appear visually similar and be mistakenly classified as houses (OOD).\n\n- The i.i.d. assumption in theoretical analysis sounds not reliable. If the assumption holds, fusing similarity scores from different modalities is essentially equivalent to averaging multiple samples, which of course, helps reduce the variance of the mean. However,\n\n(1. Not independent) Because the scores $s_{i,j}$ are similarities for different modalities or even from different layers of a model, the scores should be (probably highly) correlated to each other. For example, $s_{i,1}$ is the similarity to the text \"dog\", while $s_{i,2}$ is to an image of dog. \n\n(2. Not identically distributed). Besides, it is also very hard to say different modality similarity scores have the same expectation and variance. There are usually significant gaps between different modalities. If a new modality has a very large variance in similarity scores, adding it will mislead the method.\n\nThus, the theoretical analysis, although looks promising, might not be the case actually. \n\n### Minor\n- Compared to NegLabel, the proposed method achieves significant decrease in FPR95 (about 4) but a very small improvement in AUROC (around 0.5). Considering the number of hyperparameters to tune in the method, I feel the improvement is too small. Maybe if we change another set of hyperparameters, the advantage will be gone.\n- Lines 55-60, the format of this itemize list looks a bit strange.\n- Algorithm 1 might be unnecessary because Section 3 and Figure 2 are very clear. I recommend the authors to consider moving some experimental results from the supplementary materials to the main content, and move Algorithm 1 to the supplementary materials.", "was_revised": true, "final_justification": "Thank the authors for the detailed response!\n\nMost of my concerns have been addressed.\n\nThere are a few hyperparameters to tune in DualCnst and this may limit the practical value of DualCnst. Addressed.\n\nWhether the synthetic image is reliable for OOD detection. The authors provided additional experiments and more clarification. Addressed.\n\nThe i.i.d. assumption in theoretical analysis sounds not reliable. As the authors commented, the assumption \"is a simplification and does not strictly hold in practical settings\". But this point does not significantly diminish the value of this paper.\n\nThus, I will raise my score to 5 and argue for the paper to be accepted.", "rebuttal": "Response to Reviewer ehFX\n\nWe thank the reviewer ehFX for the valuable feedback. We addressed all the comments. Please find the point-to-point responses below. Any further comments and discussions are welcomed!\n\n**W1:&#x20;**&#x54;here are a few hyperparameters to tune in DualCnst, such as which layers to use, the weight assigned to each layer, and the fusion weight of similarity scores. This may limit the practical value of DualCnst.\n\n**Reply:**\n\nWe acknowledge the reviewers' concerns regarding hyperparameter sensitivity in DualCnst. To address this, we emphasize that all comparative experiments (Appendix E) employed a methodologically consistent configuration: identical layer selections (3th, 6th, 9th), fixed layer weights ($w_l$=0.15), and uniform fusion coefficient ($\\alpha$=0.1) across all datasets and benchmarks. Crucially, as demonstrated in Appendix G.6 (Table 22), DualCnst maintains performance superiority over baselines even when $\\alpha$ is systematically maintained at 0.1 without dataset-specific tuning. This consistency validates the framework's operational robustness in real-world scenarios where exhaustive hyperparameter optimization may be infeasible.\n\n**W2:&#x20;**&#x46;or each textual label, only one image is synthesized and the similarity between the test input and the synthetic image is calculated for OOD detection.\n\n**(1)** I'm wondering if one image is sufficient to represent a class. For example, if the class label is \"dog\", there should be many different images of a dog. One synthetic image is just one case of that, and may lead to low similarity to other dog images.**(2)** When using generative models to produce images, we cannot guarantee that the generated images contain only the intended category. For example, suppose the ID class is \"dog\" and the OOD class is \"house\". When generating OOD images, the model generates a house, but due to randomness or inherent bias, it might also include a dog next to the house. This can lead to incorrect predictions during visual similarity comparisons, as images containing dogs (ID) may appear visually similar and be mistakenly classified as houses (OOD).\n\n**Reply:**\n\nWe appreciate the reviewers' comments.\n\n**1.** Table 25 was specifically designed to address this concern. Experiments with synthetic images generated under different random seeds demonstrate negligible impact on results, providing concrete proof that synthesized images represent consistent class prototypes.\n\n**2.** As rigorously validated in Table 25, varying random seeds during synthetic image generation yields minimal performance variance. To further address the reviewer's concern, we conducted controlled experiments using dogs (ID) and houses (OOD) as exemplar categories. We generated hybrid images with:\n\nExperimental Design: Using prompt, we synthesized images with deliberate semantic mixtures\n\na. Case A: 80% dominant-ID / 20% OOD semantics (e.g., \"a dog standing beside a small house\")\n\nb. 2Case B: 20% ID / 80% dominant-OOD semantics (e.g., \"a house with a dog in the corner\")\n\nQuantitative similarity metrics between ID/OOD images and synthesized hybrids (Table A) demonstrate our method's discriminative capability despite deliberate semantic deviations during generation.\n\nTable A: Controlled experiment on dog-house ID/OOD differentiation.\n\n| Similarity Metric                | Semantic Layer | Fusion Layer |\n| -------------------------------- | -------------- | ------------ |\n| cos(dog, 80% dog + 20% house)    | 0.8706         | 0.8792       |\n| cos(house, 80% dog + 20% house)  | 0.5442         | 0.6972       |\n| cos(house, 80% house + 20% dog)  | 0.6712         | 0.7897       |\n|  cos(dog, 80% house + 20% dog)   | 0.6640         | 0.6616       |\n\n**W3:&#x20;**&#x54;he i.i.d. assumption in theoretical analysis sounds not reliable. If the assumption holds, fusing similarity scores from different modalities is essentially equivalent to averaging multiple samples, which of course, helps reduce the variance of the mean. However,\n\n**(1. Not independent)&#x20;**&#x42;ecause the scores $s_{i,j}$ are similarities for different modalities or even from different layers of a model, the scores should be (probably highly) correlated to each other. For example, $s_{i,1}$ is the similarity to the text \"dog\", while $s_{i,2}$ is to an image of dog.\n\n**(2. Not identically distributed).** Besides, it is also very hard to say different modality similarity scores have the same expectation and variance. There are usually significant gaps between different modalities. If a new modality has a very large variance in similarity scores, adding it will mislead the method.\n\nThus, the theoretical analysis, although looks promising, might not be the case actually.\n\n**Reply:**\n\nWe appreciate the reviewer’s insightful comments. We fully agree that the i.i.d. assumption is a simplification and does not strictly hold in practical settings. Our theoretical analysis is intended to offer an intuitive understanding rather than a precise characterization. We address the two concerns below:\n\n1. **(Non-independence):** We acknowledge that the i.i.d. assumption represents an idealized scenario. As discussed in Appendix D (Limitation III), we note that the Central Limit Theorem (CLT) still applies under weak dependence. That is, as long as the similarity scores are only weakly correlated, the trend of variance reduction from fusion remains valid. To empirically assess this, we measured the pairwise Pearson correlations between similarity scores from different modalities and layers. The observed correlations are consistently low (e.g., < 0.3), supporting the applicability of the weak correlation assumption in our context.\n\n\n\n   Table B: Pairwise Pearson correlations between the similarity scores from different modalities.\n\n   |           | img\\_sim | text\\_sim |\n   | --------- | -------- | --------- |\n   | img\\_sim  | 1.00     | -0.02     |\n   | text\\_sim | -0.02    | 1.00      |\n\n   Table C: Pairwise Pearson correlations between the similarity scores from different layers.\n\n   |             | text\\_sim | layer1\\_sim | layer5\\_sim | layer9\\_sim |\n   | ----------- | --------- | ----------- | ----------- | ----------- |\n   | text\\_sim   | 1.00      | 0.00        | 0.01        | 0.20        |\n   | layer1\\_sim | 0.00      | 1.00        | 0.27        | 0.13        |\n   | layer5\\_sim | 0.01      | 0.27        | 1.00        | 0.30        |\n   | layer9\\_sim | 0.20      | 0.13        | 0.30        | 1.00        |\n\n2. **(Non-identical distribution):** It is indeed true that similarity scores from different modalities may vary in both mean and variance. However, our analysis does not require strict identicality. Even when relaxing the i.i.d. assumption, the trend of reduced variance with aggregated scores still holds as long as the aggregated score behaves as a weighted average. To validate this, we compared the empirical variance of the aggregated score under different modality combinations (see Table D). The results show a consistent decrease in variance as more modalities are fused. Moreover, our aggregation strategy incorporates a **modality-aware weighting scheme** (see Eq. 2 and Eq. 5), which assigns higher weights to more reliable modalities (e.g., textual similarity) and lower weights to auxiliary ones. As shown in Table 19 of our paper, this non-uniform weighting significantly enhances the overall effectiveness and robustness of the method.\n\nTable D: empirical variance of the aggregated score under different modality combinations.\n\n|                                     | empirical variance (×1e-3) |\n| ----------------------------------- | -------------------------- |\n| Only Text score                     | 0.7110                     |\n| Only Image score                    | 0.3691                     |\n| Fusion score (1 intermediate layer) | 0.3553                     |\n| Fusion score (2 intermediate layer) | 0.3366                     |\n| Fusion score (3 intermediate layer) | 0.3274                     |\n| Fusion score (4 intermediate layer) |  0.3038                    |\n| Fusion score (5 intermediate layer) | 0.2717                     |\n| Fusion score (6 intermediate layer) | 0.2567                     |\n\n**Minor W1:&#x20;**&#x43;ompared to NegLabel, the proposed method achieves significant decrease in FPR95 (about 4) but a very small improvement in AUROC (around 0.5). Considering the number of hyperparameters to tune in the method, I feel the improvement is too small. Maybe if we change another set of hyperparameters, the advantage will be gone.\n\n**Reply:**\n\nWe appreciate the reviewer’s valuable feedback. We agree that the AUROC improvement, while consistent, is modest. This is likely because the AUROC baseline is already high under the current setup, making further gains challenging. Regarding the hyperparameters, we emphasize that all results in Appendix E were obtained using a single fixed parameter configuration across all experiments. No task-specific tuning was performed, ensuring both the generalizability and fair comparability of our findings.\n\n**Minor W2:&#x20;**&#x4C;ines 55-60, the format of this itemize list looks a bit strange.\n\n**Reply:**\n\nWe gratefully acknowledge the reviewer's acute academic insight. In response to this valuable suggestion, we will make rigorous corrections in the final version of the paper.\n\n**Minor W3:&#x20;**&#x41;lgorithm 1 might be unnecessary because Section 3 and Figure 2 are very clear. I recommend the authors to consider moving some experimental results from the supplementary materials to the main content, and move Algorithm 1 to the supplementary materials.\n\n**Reply:**\n\nWe appreciate the reviewer’s insightful comment. We accept the suggestion and will move some experimental results from the supplementary materials to the main text. Algorithm 1 will be moved to the supplementary materials accordingly."}, {"review_id": "ITdiKRuWc3", "reviewer": "Reviewer_2pMP", "summary": "This paper aims to improve zero-shot out-of-distribution (OOD) detection by: (1) synthesizing images from both ID and OOD samples, and (2) computing a unified score based on image-to-text and image-to-image similarities. The proposed method is training-free and data-agnostic, and is supported by strong experimental results and a clear theoretical justification highlighting the benefits of expanding multimodal negative labels for OOD detection.", "questions": "Wrote them above", "limitations": "Wrote them above", "rating": 5, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**Strengths**\n1. The targeted problem is important, and the proposed method appears both reasonable and effective.\n2. I was particularly satisfied with the presentation of the paper, especially the motivation in Figure 1, which clearly illustrates the rationale behind the method. Even as a reader less familiar with this research domain, I found the introduction accessible and easy to follow.\n3. I also appreciate the comprehensive experimental section, which includes detailed ablation studies (not only in Section 5.3 but also in the supplementary material (Section G)). \n\n\n**Weaknesses**\n\nAlthough I am not deeply familiar with this specific area of research, I believe the paper is already in good shape, supported by extensive experiments. However, there are a few inherent limitations that could be more discussed:\n\n1) Since the method relies on synthetically generated images (e.g., from Stable Diffusion), its performance is naturally influenced by the quality of these synthetic samples. While this may not be a critical flaw given the demonstrated effectiveness, I would appreciate further analysis (such as failure cases or robustness checks) that examines how sensitive the method is to the quality of generated images.\n\n2) Additionally, because the approach is training-free but requires synthetic image generation at test time, it would be helpful to include a comparison of the computational overhead relative to prior baseline methods.", "was_revised": true, "final_justification": "I'm satisfied with the response so I'm happy to maintain my original positive score", "rebuttal": "Response to Reviewer 2pMP\n\nWe thank the reviewer 2pMP for the valuable feedback. We addressed all the comments. Please find the point-to-point responses below. Any further comments and discussions are welcomed!\n\n**W1:&#x20;**&#x53;ince the method relies on synthetically generated images (e.g., from Stable Diffusion), its performance is naturally influenced by the quality of these synthetic samples. While this may not be a critical flaw given the demonstrated effectiveness, I would appreciate further analysis (such as failure cases or robustness checks) that examines how sensitive the method is to the quality of generated images.\n\n**Reply:**\n\nWe appreciate the reviewer's valuable insight regarding sensitivity to synthetic image quality. To simulate potential synthesis failures, we systematically degraded generated images by introducing incrementally increasing Gaussian noise. As quantitatively demonstrated in Table A, performance degradation scales directly with noise severity. Crucially, our method maintains competitive performance against baselines even under severe noise corruption (e.g., at 80% noise intensity).\n\nTable A: Performance impact of varying noise levels.\n\n| Noise ratio (Method)  | iNaturalist   | SUN            | Places         | Texture        | Average       |\n| --------------------- | ------------- | -------------- | -------------- | -------------- | ------------- |\n|                       | FPR95/AUROC   | FPR95/AUROC    | FPR95/AUROC    | FPR95/AUROC    | FPR95/AUROC   |\n| No noise (DualCnst)   | 1.23 / 99.66  | 16.79 / 95.96  | 30.74 / 92.20  | 39.13 / 91.00  | 21.97 / 94.70 |\n| 30% (DualCnst)        | 1.26 / 99.59  | 16.85 / 95.93  | 30.27 / 92.44  | 40.36 / 90.62  | 22.19 / 94.64 |\n| 50% (DualCnst)        | 1.78 / 99.66  | 19.01 / 95.70  | 32.08 / 92.18  | 42.05 / 90.92  | 23.73 / 94.62 |\n| 80% (DualCnst)        | 2.04 / 99.64  | 20.80 / 95.66  | 33.90 / 92.23  | 44.24 / 90.58  | 25.25 / 94.23 |\n| NegLabel              | 1.91 / 99.49  | 20.53 / 95.49  | 35.59 / 91.64  | 43.56 / 90.22  | 25.40 / 94.21 |\n\n**W2:&#x20;**&#x41;dditionally, because the approach is training-free but requires synthetic image generation at test time, it would be helpful to include a comparison of the computational overhead relative to prior baseline methods.\n\n**Reply:**\n\nWe appreciate the reviewer's valuable suggestion regarding computational overhead. As detailed in Appendix F.3 and shown in Table B, we provide comparative benchmarking of GPU memory consumption on an NVIDIA RTX A6000 GPU under identical configurations. While our training-free approach demonstrates competitive inference speed, its memory footprint exceeds baseline methods by 18-24%. This increase is directly attributable to the online computation of image feature similarity required during inference.\n\nTable B: GPU Memory Overhead Comparison During Inference and Generation.\n\n| Method   | Inference GPU Memory Usage | Generation GPU Memory Usage |\n| -------- | -------------------------- | --------------------------- |\n| DualCnst | 33245MiB                   | 9465MiB                     |\n| NegLabel | 26353MiB                   | -                           |\n| MCM      | 17827MiB                   | -                           |"}]

Author rebuttal:

Meta-review: The reviewers initially acknowledged several strengths of this paper, including the importance of the problem (2pMP), comprehensive experiments and strong performance (XUrX, k9F4, ehFX, 2pMP), the novelty of Dual-Consistency (XUrX, k9F4), the simplicity of the method (ehFX), and clear writing (ehFX, 2pMP). At the same time, they raised a number of concerns, such as the dependence on synthetic images (XUrX, ehFX, 2pMP), heavy computation cost (XUrX, 2pMP), the unrealistic i.i.d. assumption (XUrX, ehFX), insufficient experiments and analysis (XUrX, k9F4), and limited novelty (XUrX).

The authors' rebuttal addressed several of these issues, but two concerns in particular remained important, and were revisited during the AC-Reviewer discussion:

- **1. Dependence on synthetic images (XUrX, ehFX, 2pMP)**: The method relies on generating synthetic images with SDXL-Turbo, which is a core component.

  - **1a. Heavy computation time**: While the authors argue that *all synthetic exemplars can be pre-generated offline*, this is only feasible if the class set is fixed in advance. In open-vocabulary scenarios, this assumption may not always hold. Moreover, the additional results presented in the rebuttal (Table C) showed significant performance drops when using a reduced class set, which itself still assumes prior knowledge of the class set.

  - **1b. Dependence on image quality**: The authors claim that multi-level features mitigate the effect of low-quality generated images, but no clear evidence was provided that this indeed improves robustness.

- **2. Unrealistic i.i.d. assumption (XUrX, ehFX)**: The theoretical foundation assumes i.i.d. conditions, which contradict the nature of the method. Although some idealization is acceptable in theory, the gap between the assumption and the actual methodology should be acknowledged and weighed carefully.

Regarding these issues, the reviewers did not maintain strong concerns in their final justifications. They indicated that the rebuttal was largely satisfactory, and that the i.i.d. assumption, while imperfect, does not critically undermine the contributions of the paper.

**The authors are strongly encouraged to incorporate the feedback obtained from discussions with the reviewers into the final version. The issues pointed out above remain unresolved and need to be carefully discussed or clearly stated as limitations.**
Decision: Accept (poster)