OOD-Barrier: Build a Middle-Barrier for Open-Set Single-Image Test Time Adaptation via Vision Language Models
Authors: Boyang Peng
Sanqing Qu
Tianpei Zou
Fan Lu
Ya Wu
Kai Chen
Siheng Chen
Yong Wu
Guang Chen
Venue: NeurIPS 2025 poster ()
Keywords: Open Set Learning
OOD Detection
Vision Language Models
Primary area: general_machine_learning
TL;DR: We propose Open-IRT, a vision-language-based test-time adaptation method for single-sample open-set streams, achieving state-of-the-art performance via polarity-aware OOD filtering and intermediate domain learning.

Abstract: In real-world environments, a well-designed model must be capable of handling dynamically evolving distributions, where both in-distribution (ID) and out-of-distribution (OOD) samples appear unpredictably and individually, making real-time adaptation particularly challenging. While open-set test-time adaptation has demonstrated effectiveness in adjusting to distribution shifts, existing methods often rely on batch processing and struggle to manage single-sample data stream in open-set environments. To address this limitation, we propose Open-IRT, a novel open-set Intermediate-Representation-based Test-time adaptation framework tailored for single-image test-time adaptation with vision-language models. Open-IRT comprises two key modules designed for dynamic, single-sample adaptation in open-set scenarios. The first is Polarity-aware Prompt-based OOD Filter module, which fully constructs the ID-OOD distribution, considering both the absolute semantic alignment and relative semantic polarity. The second module, Intermediate Domain-based Test-time Adaptation module, constructs an intermediate domain and indirectly decomposes the ID-OOD distributional discrepancy to refine the separation boundary during the test-time. Extensive experiments on a range of domain adaptation benchmarks demonstrate the superiority of Open-IRT. Compared to previous state-of-the-art methods, it achieves significant improvements on representative benchmarks, such as CIFAR-100C and SVHN — with gains of +8.45\% in accuracy, -10.80\% in FPR95, and +11.04\% in AUROC.

OOD-Barrier: Build a Middle-Barrier for Open-Set Single-Image Test Time Adaptation via Vision Language Models
Boyang Pengl. 1.2
Sanqing Qu Siheng Chen
Tianpei Zou' Yong Wu
Fan Lu Guang Chen 1,2+
Ya Wu?
Kai Chen
Shanghai Innovation Institute CNNC Equipment Technology Research (Shanghai) Co., Ltd.
Tongji University
Shanghai Jiao Tong University
Abstract
In real-world environments, a well-designed model must be capable of handling dynamically evolving distributions, where both in-distribution (ID) and out-of- distribution (OOD) samples appear unpredictably and individually, making real- time adaptation particularly challenging. While open-set test-time adaptation has demonstrated effectiveness in adjusting to distribution shifts, existing methods often rely on batch processing and struggle to manage single-sample data stream in open-set environments. To address this limitation, we propose Open-IRT, a Test-time adaptation framework tailored for single-image test-time adaptation with vision-language models. Open- IRT comprises two key modules designed for dynamic, samlle adaptation in open-set scenarios. The first Polarity-awe -waare Prompt-based OOD Filter module, which fully constructs the ID-OOD distribution, considering both the absolute mantic alignment and relative semantic polarity. The second module, Intermediate Romain-based Test-time Adaptation module, constructs intermediate domain and indirectly decomposes the ID-OOD distributional discrepancy to refine the separation boundary during the test-time. Extensive experiments on a range of domain adaptation benchmarks demonstrate the superiority of Open-IRT. Com- pared previous state-of oite--f-the-ann art methods, it achieves significant improvements on representative benchmarks, such as CIFAR- 100C and SVHN with gains of +8.45% in accuracy, -10.00% FPR95, and +11.04% AUROC.
novel open-set
Introduction
In real-world environments, the model's ability to perform real-time adaptation is particularly crucial for handling the emergence of unknown category distributional shifts. This capability is essential safety-critical applications such as autonomous driving, where failure to adapt can have serious consequences. Despite these demands, most approaches in computer vision [1-3] assume closed-set paradigm, where training and testing data come from the same distribution. In contrast, real-world scenarios frequently encounter open-set environments [4-6]. where models must handle unknown distributions and unseen categories, as shown in Fig. 1. This fundamental deviation from the closed-set assumption introduces fundamental challenges in maintaining model performance.
This transition from wooled-world to open-set learning necessitates requiring innovative strategies to effectively filter, classify, and adapt both distributional and semantic shifts without explicit supervision [7]. However, traditional approaches based on predefined categorics are not equipped
"Corresponding Author
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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to handle the emergence of unknown categories in open-set environments, particularly in real- time inference and single-sample adaptation constraints. As result, accurate and efficient OOD detection has emerged as critical research focus [7-9] in dynamic, open-set scenarios.
With the advent of vision- language models (VLMs), re- cent studies [10, 11] have leveraged their strong gener- alization for OOD detection tasks. Models such as CLIP [10] facilitate adaptation to unseen categories in open- set situation by learning rich cross-modal representations. Furthermore, recent research [12] have demonstrated that VLMs can perform zero-shot reasoning based on image- text associations and further adapt to single image infer- ence, which enhances both their zero-shot generalization and OOD detection perfor- mance.
ID
(a) Original Feature Space
(b) Feature Space after Open-IRT
Open World Single- segle-llage Stream
Generalization Area
Build Middle Domain
OOD
FM
ID
ID
MID
OOD
Overlap
ID
Separate ID and 0OD
OOD
FM
obD
MID
OOD
Confusiin Overlap Space
Harrier and Separate
Figure 1: The objective of Open-IRT. In open-set environments, models encounter individual sample streams, which may include unknown categories. Existing test-time adaptation methods mainly focus on closed-set settings or batch image processing, neglecting Sanglessampe data streams. (a) In such cases, the boundary between ID and OOD data can become unclear. (b) Our goal is to improve domain separation via a middle domain (MID) strategy as shown in Fig. 4, and use test-time adaptation with VLMs to reduce ambiguity.
Test-time adaptation [13-16] offers a promising approach for adapting models to incoming data streams during inference, without relying on labeled data. Recent developments have extended this paradigm to single-sample test-time adaptation [17, 18), which is particularly valuable in domains such as security surveillance and industrial inspection, where real-time adaptation to dynamic environments is critical. However, most existing single-image test-time adaptation methods mainly focus closed-set assumptions, whereas open-set batch processing strategies struggle to effectively adapt to changes in individual samples, particularly in real-time scenarios where only a single image is available for adaptation, illustrated in Fig. 1.
To overcome this limitation, we focus on Open-Set Single-Image Test-time adaptation setting, where the model dynamically adapts to each input sample during inference without relying batch data. Specifically, we propose a novel Open-set Intermediate- IntermediaeeRRepeeentttiin bassed Test-time adaptation (Open-IRT) framework for single-sample test-time adaptation in OOD detection. The overall motivation is illustrated in Fig. 1. As illustrated Fig. lb, Open-IRT establishes a structured separation between ID and OOD samples in feature space. The framework consists of two key modules. First, we introduce a Polarity-awar Prompt-based OOD Filter (PPF) module in Fig. 2a. Here, the term "polarity" refers to utilization of the disparity between positive and negative prompts. PPF leverages the rich cross-modal information in vision language models to fully construct the ID-OOD distribution from both positive and negative prompts. It is guided by Semantic Contrast Hypothesis 1, which considers the absolute semantic alignment and relative semantic polarity. Next, we introduce Intermediate Domain-based Test-time adaptation (IDT) module in Fig. 2b based on Beermediate-Domaall Hypothesis 2, which indirectly decomposes the distributional discrepancy between ID and OOD representations by modeling an intermediate domain that bridges the gap between these two. As shown in Fig.4, the IDT explicitly models this middle domain, and the learning objective enforces divergence of both ID and OOD samples from the intermediate domain. Therefore. this strategy effectively enlarging the distance between ID and OOD distributions indirectly. In addition, IDT uses dynamic threshold strategy to generate bidirectional pseudo-labels, encouraging the model to reinforce positive feature representations and suppress intra-class noise.
The main contributions summarized as follows:
(i) We propose the PPF, an effective OOD filtering mechanism based on Semantic Contrast Hypothesis 1. The PPF captures both absolute semantic alignment and relative semantic polarity between an input and its paired prompts, enabling effective ID-OOD separation. (ii) We introduce the Intermediate- Domaan Hypothesis 2, which leads to the development of IDT module. This module construct intermediate domain strategy establish a real-time two-way repulsion constraint between ID and OOD feature distributions, enhancing ID-OOD separation indirectly. (iii) Open-IRT consistently outperforms prior sate-of-the-an methods

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on standard benchmarks, including ImageNet-C [19], Tiny-ImageNet [20], VisDA [21], CIFAR- 10C/100C [19]. and digit datasets [22-24]. For instance, on CIFAR-100C and SVHN, achieves +8.45% accuracy, -10.80% FPR95, and +1 1.00% AUROC, supporting our motivation and hypothesis.
2 Related Works
2.1
Out-of-Distribution Detection
-ut-of-distibbttion (OOD) detection aims to determine whether a given sample originates from the training distribution from unseen distribution. The OOD detection approaches can be broadly categorized into classification-bant
methods [7, 25, 26] and density-based methods [8, 27J. Classification-basee OOD methods relied maximum softmax probability to distinguish between ID and OOD samples. These include post hoc techniques such as ODIN [28]. which utilizes temper- ature scaling and input perturbation. In addition, there are classic approaches such as JointEnergy scores [26], Mahalanobis distance [29], and activation space-based techniques [30, 31] that enhance the separability of ID and OOD samples without altering the training process. On the other hand, density-based methods use probabilistic models, such as class-conditional Gaussian distributions [29] and flow-based models [32, 9]. identify OOD samples based on their likelihood. To address high likelihoods challenges of OOD, techniques such as likelihood ratio [33], likelihood regret [34], and SEM scores [35] have been proposed. However, traditional methods are predominantly designed for the training phase, limiting their ability to adapt to distribution and semantic shifts in real-time.
2.2 OOD Detection with Vision Language Models
Vision-language models have gained significant attention in recent years for their ability to integrate visual and textual information. Renowned vision-language models, such as CLIP [10] and MaPLe [11], achieve impressive results by training large-scale image-tett text pairs. To adapt vision- language models for downstream tasks (e.g.. OOD detection), additional lightweight modules have been introduced, including prompt learners [36, 37J, vision adapters [38, 39], and LoRA [40, 41].
Recent research OOD detection has begun to utilize vision-language models as auxiliary tools, starting with CLIP [42], which aims to distinguish samples that do not belong any ID class text provided by the user [43]. These approaches often employ techniques such as OOD label retrieval [44], generation [45], alignment [46]. Training-free methods such MCM [47] detect OOD using only ID labels, while auxiliary training- methods, such as CLIPN [48] leverage additional pre-training to enhance OOD detection. From the perspective of specific training methodologies, some approaches implement a specialized handling of prompt words [49, 50], while NegPrompt [51] further explores the use of negative prompt techniques. Moreover, ROSITA [52] consider this task in test-time, yet it remains constrained by its reliance on direct feature alignment. Unlike previous methods, Open-IRT is fundamentally guided by an intermediate domain located near the boundary of the feature space, which indirectly decomposes the distributional discrepancy between ID and OOD representations. introduces a bidirectional repulsion constraint, combining semantic alignment with relative semantic polarity, to increase the distributional separation.
2.3 Time Adaptation
Test-time adaptation, originating from domain adaptation [53-55], adapts pre-trained models to test data with distribution shifts, without requiring training data. Test-time adaptation is essential for real- world applications, such as autonomous driving in diverse weather conditions. Several methods have been proposed, such as adjusting partial model weights [13, 56] or normalization statistics [14, 15]. Specifically, TENT [15] adapts batch normalization layers by entropy minimization, TTT [56] updates classification layers during testing, and T3A [13] introduces an optimization adjustment. As technology for changing environments, test-time adaptation can be integrated with OOD detection (e.g.. RTL [57], UniEnt [58]). However, they typically address the task using batch processing methods, which limits their ability to handle dynamic scenarios effectively.
classifier
To reduce reliance on multiple test samples, some approaches prioritize single-sample adaptation. MEMO [59] enforces consistency through augmentations of the same test sample, while TPT [12] fine-tunes prompts for vasion-language models during testing. DiffTPT [60] enhances this by using pre-trained diffusion models to augment test data. TDA [61] addresses computational efficiency
3

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A photo a <ID classi-
Text Encoeler
(ai) PPF:Score Calculation
(a2) PPE:OOD Filtering
(b) IDT :nteermediati Domain-based TTA
Sim
Stitper Stina
Ficturcc
photo
<ID class?
Roow
Opena World Singlles Single--Teeg Invage Streum
OOD
Posilive Pantutppe
Simyyw
Scare
Middle domaln
Conf 'A
Simay
Negrève Protocypes
Liwi
Coof
ID
OOD
Embed
Sitter
GMM
Vision Encoder
Simlay
Moso
OOD Feanure Bank
KAN
OOD
M
Seree
if OOD
Freeze
Update
Style Transfer
OOD Sample Features
ID Sample Features
Scores
Figure The architecture Open-IRT comprises two key modules: (a) Polarity-aware Prompt-based OOD Filter and (b) Intermediate Domain-based Test-time Adaptation. (a) filters ID and OOD samples using positive and negative prompts, computing scores in Eq. 3 and utilizing GMM strategy for OOD filtering. (b) generates an intermediate domain in Fig. 4 and Eq. 7 by leveraging mean and variance from feature banks, enhancing the model's adaptability with contrastive learning loss Emiid in Eq. 10 and pesudo-label loss Lysd in Eq. 13 during the test phrase.
with a cache-based model. While these methods assume closed-set settings, Open-IRT enables single-image Test-time adaptation in open-set environments. Unlike continual test-time adaptation, it further considers semantic shift in addition distribution shift and refines the ID-OOD boundary in terms of the intermediate representation.
3 Methodology
In open-world environments with real-time perception demands, models frequently encounter single- image data streams that contain OOD samples. To address this challenge, we propose Open-set Intermediate- Interneddiae-RRppeeeetttiio based Test-time Adaptation (Open-IRT) in Fig. 2. Open-IRT ITT is an open-set, single-image test-time adaptation strategy composed two key modules: Polarity-aware Prompt based OOD Filter module and Intermediate Domain-basee Test-time adaptation module.
3.1 PPF: Polarity-aware Prompt-based OOD Filtering Mechanism
As shown in Fig, 2a, we introduce the Polarity-aware Prompt-based OOD Filter (PPF) module, which improves ID- -OD separability by exploiting the dual-polarity semantics of lision-languag models. Specifically, we design positive prompts Pa (e.g., "a photo of a [CLS]") to represent ID prototypes, and negative prompts pl. (e.g.. "a photo of [CLS]") to encode inverse semantics [48]. Given vision feature yn (2)) after L22 normalization, we compute its cosine similarity with both the positive
_ Pc
prompt Pe and the negative prompt p'. for class c. simpos = sim Pe) = |I
|I
simneg =
sTv T simass ssimpos simneegl
sim(f.pb
Hypothesis 1 (Semantic Contrast Hypothesis). For ID samples Ve [C], f P, and 00D Po:
Ey-p,(sim(((PD) 2 Th [sm(s(sim(f To E (sim Pc) sim(f.ro) 2 Ar Fs sim(s.r') Do
(1)
where thresholds satisfy T7 To and A Ao.
First, we propose the Semantic Contrast Hypothesis. For ID samples, the alignment with positive prompts strong, i.e., sim(ffpp) high, and the polarity gap [sim([ -
dim(S.1/. (f,()] is also large, leading to a confidently larger overall sum. For OOD samples, the alignment with positive prompts weaker, and the polarity gap is less marked due to semantic ambiguity, leading to suppressed overall sum. The proposed scoring function S(f) jointly captures absolute semantic alignment and

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relative semantic polarity between an input and its paired prompts. This promotes effective ID- ID-OOD separability by maximizing the distributional contrast between them.
S(f) = sup (fm + [sim(f. -f.mm.imm).).) cE|C]
(3)
where e is min-max normalization operator ensure the score lies within the range [0, 1]. and controls the contrast intensity. As shown in Fig. 3, while individual sample have unique semanties, the resulting aggregate score distribution exhibits clear bimodal pattern, demonstrating consistent statistical regularity across large-scale data. Furthermore, the introduction of the polarity gap term significantly enhances ID-OOD separation, thereby validating the Hypothesis 1. This dual-polarity design leveraging alignment with positive prompts and contrast with negative prompts prompts-offers oofers an effective mechanism for ID-OOD distinction. Detailed theoretical analyses are provided in appendix.
Then, we introduce the score bank B and feature bank B', both updated using a sliding window strategy, where it functions equivalently individual samples and is divided into two components: ID (13f) and OOD (B%) score banks, based on the Gaussian Mixture Model described in Eq.
FIFO queue. The score bank B stores the scores of
P(z) (x))[(z 11), + (1 (z)) 16, .
PPF Scores Histogram wio polarity gap item
Here, (Ix denotes the probability that ((x) belongs to the ID class, and fi,o- Tijo" are the mean and variance of the ID/OOD compo- nents. The probability <(=) is computed using the poppectaiionMaxximzaan
400-
ool
Frequency
200
algorithm.
Upon the arrival of new sample its score ((x) is appended to both B and B. and Eq. is used to classify it as either ID (6 = 1) or OOD ( = 0). To mitigate the impact of limited samples during the early stages of learning, we employ bootstrapped resampling, which helps prevent overfitting to sparse observations. The classified features are then inserted into the cor- responding feature banks Bi,0 for use in the subsequent module.
0
1.0
PPF Scores Histogram with polarity gap item
Frequency
0.0
0.2
0.8 1.0
Scures
Figure 3: PPF score distribution histogram with
CIFAR-10C [19] as ID and MNIST [22] as OOD.
3.2 IDT: Intermediate Domain- Test-time adaptation
We now introduce the Intermediate Domain-based Test-time adaptation (IDT) module, which lever- ages the feature banks B3 and B to further refine adaptation. To address the challenge in Fig. 1, first propose Hypothesis that models the intermediate domain (FM).
Hypothesis (Intermediate Domain Characterization). Let F, & {flf -P) and Fo & {flf Pot denote the ID/OOD feature distributions respectively. There exists measurable transformation ' F, Fo + FM such that for the induced intermediate feature space Fas that satisfies the following approximate equality:
dx(Fi,FM)) + du (Fo, RS (F,,F
(5)
where du represents the Hilbertt-Shmiii independence criterion-based dissimilarity.
Rather than directly maximizing the ID-OOD distance Fo), which is challenging due to limited knowledge of OOD distribution, we instead construct an intermediate domain FM to act as a bridge. By encouraging both ID and OOD samples move away from this intermediate domain (i.e., increasing dx FM) and (Fo FM)). we indirectly enhance the overall discrepancy du (Fr, This aligns with the intuition of margin-based separation in representation space.
Contrastive Learning-based Middle Domain Loss. We first model the intermediate domain feature fm per Hypothesis 2. To normalize the sample features fe, compute their mean and variance
5

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based on augmented features, which is derived the fa as Eq. 6. The method for calculating f. has been validated for its rationality [62], where is computed across spatial dimensions independently for each feature channel. The feasibility of this approach lies in preserving channel-specific style information. The variance for each channel emphasizes fine- fine-graimg grained structural characteristics that feature dimension, such as color, texture intensity, etc., without mixing with other channels, thereby achieving refined style representation and alignment.
(-()
fa
- o(f)
(6)
Then, we extract the style features stored in the feature memory banks Bi to compute 1410 and T1o- The design of intermediate style feature fn is theoretically motivated by domain adaptation principles [62], which suggest that constructing intermediate feature space through style-transfer- like transformations can effectively position the new domain between source and target domains, enabling more controlled feature interpolation. fm is then re-assigned follows.
{ fo o + 11
Sa o + mo if b = 1; Conf(x) > 14T
fm fm==(
if b = 0; Conf(æ) < μо
The confidence Conf(x) is defined in Eq. 11. To further enhance inter- class discriminability and suppress intra-class noise, we introduce two thresholds, μq and po. They repre- sent the mean confidence scores of the accumulated 512 ID/OOD Conf(z). respectively, updated by sliding win- dow mechanism. If Conf(z) we treat the input as a confident ID more
T.SNE with ID, 0OD and Middle Domain Features
1.0
ID
OOD
n.8-
MID
0.4
0.2
pt.
0.0
sample. If Conf(x) < fO, likely to be a noisy sample or an OOD) sample6. The effectiveness of fm construction is visually confirmed in Fig. 4, where the T-SNE visualization shows a clear separation between ID and OOD features, with a distinct intermediate cluster. This indicates that the constructed interme- diate representations indeed satisfy the desired relational constraints in the feature space, thereby validating the theoretical motivation of Hypothesis Then, we select the K nearest neighbors Ni and No. for the sample feature f from the feature banks BY and By respectively. When the sample is confidently distinguished as ID (6 and Conf(x)) > p1), the loss C, based on InfoNCE loss [63] is constructed as follows.
0.0
0.6
1.0
Figure 4: T-SNE visualization of MNIST [22] (ID) and CIFAR-1OC [19] (OOD) and their Middle Middle-Domain (MID)
(log exp(sim(ffzt)
L1 K+
EtN Σ. eNo exp(sim 25,nn)//))/>))))
(8)
exp(sim(Jns =+7/))
log Ex-ENo exp(sim(ffm)) z )/T) )a(y = 0p)
where K+ = >>ttN 10y+ -pp) denotes the number of positively matched neighbors with the pseudo label Up. For confident ID samples, the objective of L, is to align with ID while minimizing similarity to OOD and distancing from the intermediate feature fm-
Lo
1 K
Σ ENo
log Σ. EN, exp(sim(f, )/7)
exp(sim f
A Aog
exp(sim 2+)/T)
Σ. ENi exp(sim( 2- 1/T)
For confident OOD samples (b 0 and Conf(r) < po), the objective of Lo to improve sensitivity to OOD samples by pulling OOD features away from both ID and the intermediate domain feature.

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The differences between Eq. 8 and Eq. 9 arise from the distinction between ID and OOD categories in the standard OOD detection task. For ID data, the model needs learn fine- aine-grained semantic consistency and achieve precision for each class, which is why we set (vy = Up) in Eq. 8.
{
C1 Lo 0
i = 1; Conf( x)) > μr
Liid
= 0; Conf (x) < μo
(10)
otherwise
Threshold-basee Bidirectional Pseudo Loss. To facilitate positive knowledge while mitigating intra-class noise, we design an effective pscudo-label strategy. First, we calculate the confidence Conf(2:) using the maximum cosine similarity between feature 9r (2)) and classifier weights C:
Conf(z)) (2) C²),
Up arg (ax C*)
(11)
We analyze Conf(x) from two perspectives: For high-confidence scenarios, we align predictions with Up. For low-confidence scenarios, Dp may introduce noise, we stop relying on it and use reverse optimization to push predictions away from it, mitigating the risk of misclassification
N
CCE( 9)
Σ
(ú log(p.) + (1 9.) log(1 (1g(111 -p))
(12)
If (o)f(x)) > μ4, we treat the input confident sample and optimize the model align predictions with 8, using LoF, enhancing the inter-class discriminability. Conf((r) < μo, we deviate the prediction results from the low-quality pseudo-label Üp by LeR, suppressing the intra-class noise.
LCE(x, 0p) LCE( (x,
if b 1; Conf(x) > μ4
Epsd
if i = 0; Conf(z:) n0
(13)
otherwise
Total Test-time Adaptation Loss. The total loss function LrT combines the pseudo-label loss fepd and the intermediate domain loss Lmidd- Lesd aims to enhance inter-class discriminability and suppress intra-class noise, while Liid indirectly enlarge the ID-OOD distance by increasing both dx (F1, F FAr) and (Fo in Hypothesis 2. This combination enables dynamic adjustment of the model's adaptation strategy.
LTT = Lpsd + Lmid
(14)
During test time, each incoming sample is used to update the model parameters via backpropagation with the test-time adaptation loss LTT Subsequently, the same sample is used to compute the evaluation metrics. This online uppate-then--eaaluate approach simulates a realistic scenario where the model continuously adapts to distributional shifts without access ground-truth labels.
4 Experiments
4.1 Implementation Details
Datasets.
We utilize representative ID and OOD datasets to ensure a complete evaluation. For ID datasets, we leverage CIFAR-10C/100C [19], ImageNet-C [19], and VisDA [21]. The OOD datasets include MNIST [22], MNIST-M [23], SVHN [24], Tiny-emageNet [20], and CIFAR-10C/100 [19].
Metrics. We utilize the Area Under the Receiver Operating Characteristic Curve (AUROC), the False Positive Rate at 95% True Positive Rate (FPR95) and Áсch main metries. Here, AcC M denotes the harmonic mean of Accj and Acco. where Acco is the precision of ID-OOD binary classification, and Ace; is the general accuracy to correctly identify ID categoriess.
Baselines. We utilize the CLIP [10] and MaPLe [11] as models, which are based on the ViT-B16/ViT- B32 [64] architectures. We adopt Z.S-Eval Eval [52], TPT/TPT-C [12], PAlign/PAlignnC [46). TDA [61], DPE [65], UniEnt [58], OWTTT [66] and ROSITA [52] as baselines.
Details. All experiments are reproduced based on publicly available code, with ImageNet-C experi- ments conducted on an NVIDIA A6000 GPU and other experiments on an NVIDIA 3090 GPU. In main experiments, the test size for both ID and OOD datasets is 10,000, except for the VisDA in
7

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Table 3, which is 50,000. In Table 5, the OOD test size is ratio X 10, 000. The text encoder is kept fixed. while the vision encoder is updated with SGD optimizer with learning rate of 1.5e-3, and batch size is set to for all experiments. The size B of both score bank B and feature bank B are set to 128, the number of nearest neighbors K in Eq. 8, 9 configured to 5. The ar in Eq. 3 set to 0.2, and À in Eq. 8, 9 are set to 0.1, respectively. Details on the baselines and analysis of hyper-parametters are provided in appendix.
MNIST
SVHN
Tiny-Iman Tiny-ImageNet
CIFAR- TOOC
Method
ALCT
FPR
HM
AUC
FPR
HI
AUCT
FPR
HM
ADC
FPR 1
HIM
75- [5]]
91.91
85.22
75.60
89.94
64.25
74.1
91.33
27.13
74.24
K2.57
67.96
68. 68.92
TPT [12]
91.90
85,70
75.78
89.93
64.54
74.30
91.31
27.26
74.98
82.57
68. 08.09
69.13
TPT-C [12]
83.21
67.03
75.05
60.83
69:47
50.63
74.12
57.34
48.88
63.76
93.05
51.98
CLPP
ROSITA [52]
99.43
3,25
83.95
94.94
31.22
79.12
96.37
12.69
80.07
83.01
64.54
69.64
OWTTT [66]
98.05
12.50
83.27
80.74
50.33
70.10
87.09
52.29
73.98
62.55
91.68
56,46
TDA [6]]
92.94
71.11
77.06
92.02
52.68
76.64
91. ..68
25.37
75.94
83,54
66.06
70.13
UniEnt [58]
91,98
85.20
75.62
89.97
64.38
74,18
91.
26.96
74.73
82.59
68.
68.99 68.98
DPE [65]
46.97
99,10
27.00 60
84.15
85. 24
68.52 52
89.92
31.30
69.90
79.18
75.06
62.34
CIFARIO-O
Open-IRT
99.73
1.28
84.55
96.52
18.34
80.62
97.07
10.09
80,95
82.65
61.69
69.20
ZS-Eval [52]
98.16
5,50
82,43
98.35
7.82
83.58
90.86
27.53
76.01
86. 15
$2.00
71.68
TPT
98.16
69.35
81.74
98.34
7,88
82.67
90.86
27.55
75.40
86, 15
52.
70.84
TPT-C [12]
98.22
5.15
83.34
98.35
7.85
83.55
90.91
27.44
75.84
86.20
$1.96
71.60
PAlign [46]
98.16
5.62
82:57
98.34
7,88
83.44
90.86
27.55
76.03
86, 15
52.10
71.50
MAPLE
PAlign-C [46]
98.61
3.45
83.91
98.35
8.13
83.45
91.17
26.95
76.12
86.53
50.64
71.11
ROSITA [52]
99.45
3.84
87.71
98.02
11.45
84,56
91,76
25.23
77.60
86.92
18.12
72.79
OWTTT [66]
98.34
9.63
86.52
71.01
78.78
68.70
71.20
85.81
68.29
62.35
88.44
61.89
TDA [6]]
98.42
4.13
81.97
98.60
6.20
83.95
91.27
27.00
76.84
86.72
51.40
72.61
UniEnt [58]
98.17
5.49
82.64
98.35
7.85
83.65
90.90
27.41
76.08
86.
$1.91
71.72
DPE [65]
83,82
92.73
55.52
97.42
12.95
79.41
89.10
31.13
74.32
73.57
73.67
53.64
Open-IKT 99.51
2.85
88.11
97.62
15.92
85.01
91.83
24.38
77.80
87.42
46,40
73.20
Table 1: Open-set Single- Image- Test-time adaptation results with CIFAR-1OC as ID, MNIST, SVHN, Tiny-Imagete and ZIFAR-100C, as OOD. The metrics include AUROC (AUC), FPR95 (FPR), and AcC (HM) as defined Section 4.1. Results in bold represent the best performance, while underlined results indicate the second-best ones.
4.2 Main Result
Cifar Benchmark. The cifar benchmark leverages CIFAR-10C/100C/ [19] as ID) datasets, and MNIST
[22], SVHN [24], and Tiny-ImageNet [20] as OOD datasets. CIFAR-IOOC/IOT also treated as an OOD case with small distribution shifts, applying label offsets to distinguish from ID data. As shown in Table 1, Open-IRT has demonstrated significant performance advantages. For example, in the CIFAR-IOCE - SVHN case with CLIP, Open-IRT achieved notable improvements by + +1.58% AUROC, 12. 88% FPR95, and +1 +1.50% ACCHAs. Additionally, for the CIFAR-1000 + SVHN case with CLIP, Open-IRT achieves an +8 +8.45% in ACCи 10.80% in FPR95, and +11.04% in AUROC, as detailed in appendix. OpenIRT also achieves better results in MaPLe, a multi modal fine-tuning framework for CLIP, due to its adaptability across fine-tuning methods through direct feature space operation. Furthermore, Open-IRT exhibits obvious improvements in the FPR95 (up -2.88%) highlighting its effectiveness in reducing the misclassification of OOD samples. This can be attributed to Open-IRT's ability to model the ID-OOD distribution effectively (see Fig. 3).
CLIP
MAPLE
Method
IN-C MNIST
IN-C SSVHN
IN-C -NNIST
IN-C SVHHN
AUC
FPR 4
HM
ALC
FPR 1
HM
AUI 1
EPR 4
HM
AUC1 1
FPR
HM
ZS-Eval [52]
93.34
57.34
41.41
85.72
74.34
40.84
81.24
93.97
41.29
83.05
73.63
42.44
TPT [12]
91.89
59.54
41.02
85.03
48.98
40.16
80.31
93.54
39.13
82.67
73.57
39.90
TPEC [12]
57.84
98.92
6.37
10.31
99.59
7.29
82.88
87.95
41.13
82.17
72.10
41.37
OWTTT [6G]
95.76
10.43
42.95
87.75
26.23
38.50
98.58
3.35
48.69
77.17
39.74
38.10
TDA [61]
90.54
76.23
43.66
86.76
75.45
43.07
76.76
99.02
42.98
82.46
91.75
44.63
UniEnt [58]
94.19
46.98
41.53
87.56
67.03
41.10
81.53
93.45
41.50
83.41
70.84
42.78
DPE [65]
87.92
91.94
42.87
82.96
77.90
11.93
73.97
99.59
41.39
80.06
87. 37.10
44.05
ROSITA [52]
98.97
8.55
45.74
91.90
45.66
97.19
9.56
48.28
91.86
29.21
44.47
Open-IRT
99.44
1.06
49,49
98.45
9.37
48.19
97.54
10.97
50.07
95.54
23.11
48.77
Table 2: Open-set Single-Image Test-time adaptation. The ID data is ImageNet-C, while the OOD data comprises MNIST and SVHN.
8

---

Eyps
Liiid
CIFAR-ICG
CAARR-OO--MMMMA -NNST
CIFAR
TOC -SVH
VISDA -MNIST
VIRDA ViRDA-SVHN
A
FPR 4
HM
AUC
FPR L
HM
AUCT
FPR 4
HM
AUCT
FPR L
HMM
91.91
84.91
78.07
39.94
64.03
75.14
93.55
65.83
84.34
90.45
64.98
79.02
CLIP
x
99.60
1.90
78.91
94.58
32.42
75,46
99.62
2.51
89.73
99.10
5.35
89.29
X
98.
1.74
85,52
93.52
37.04
78.57
95.66
35,40
87.74
95.04
33.70
85.67
A=
99.70
83.52
95.92
24,29
79.24
99.79
1.53
89.83
97.58
4.26
89.81
99.71
1.28
84.52
96.52
18.35
80.61
99.84
1.27
90.85
99.20
3,05
90.39
Table 4: Ablation Experiments, The ID data is a combination of CIFAR-1OC and VisDA, while the OOD data comprises MNIST and SVHN.
CIFARIO-C, Open-IKT
CIPAR
Open-IRT
ADC JIM for CHEAR-OOC MINSTT
139855 CHFAR-TOC MNIST
25:01
GAMM
00.00
250m
LDA
21.77
10A
CATTT
14.78
11.45
25.92
1.07
251
11.00
MN57
SVHN
Tay
MNIST
SWHN
Tire
MC-002
Xoho-90
VIT-0116
NST-072
Ra
(a) Analyze of Open-IRT with GMM and LDA.
(b) Backbone ViT-B/16, ViT-B/32 and ResNet50.
Figure 5: Comparison of GMM and LDA in PPF and different backbone architectures.
VisDA and ImageNet Benchmarks, The VisDA [21] and ImageNet-C [19] serve as ID datasets, while MNIST [22], MNIST-M [233, and SVHN [24] serve as OOD datasets. The complexity of VisDA and ImageNet-C introduces additional challenges for OOD detection. In Table 2, Open-IRT achieves experiment with CLIP, Open-IRT achieves gains of +0.47% AUROC, -7.49% FPR95, and +3.75% AcCH Simi- larly, in Table 3, Open-IRT achieves gains of +1.58% AUROC, -7.46% FPR95, and +1.09% AcCH in VisDA + MNIST-M with CLIP. Moreover, compared to Table 1, when the ID dataset becomes more complex (e.g., ImageNet-C), the model's performance in OOD detection tasks does not fully saturate,
notable improvements over existing baselines. For example, in MmageNet-C-MMan
resulting in a relatively lower АсCнм.
In such cases, Open-IRT's improvement in Ассни is more obvious, such a +2% increase the ImageNet-C + SVHN experiment with CLIP. Ad- ditionally, Open-IRT achieves gains of 16.86% FPR95 and +6.55% AUROC. Since Open-IRT operates primarily in the feature space and enhances the sep- aration of the ID-OOD boundary (see Fig. 4), its effectiveness becomes in- creasingly apparent as the feature dis- tribution grows more complex.
VisDA -NIITT
VIsDA -MIITTM
Method
AUC1
FPR
FPR.
ZS-Eval [22]
93.55
65.86
78.30
87.25
67.10
74.84
TPT [12)
93.55
66.11
78.44
87.25
67.19
75.05
TPT-C [12]
81.81
85.12
75.09
87.44
62.31
77.32
ROSITA [52]
99.63
2.99
90.59
97.10
15.14
86.88
Open-IKI
99.35
1.27
90.38
98.68
7.68
87.97
ZS-Eval [52]
93.07
66.
80,29
92.31
45.66
78.83
TPT [12]
93.07
66.03
80.35
92.30
45.70
78.87
MAPLE
TPT-C [12)
59.35
80.35
92.48
78.93
PAlign [46]
93.07
66.03
80.62
92.29
45.70
79.17
PAlign-C [46]
95.61
27.65
81.93
94.13
32.97
81.48
ROSITA [52]
99.80
1.40
90.84
98.90
5.79
89.40
Open- IKT
99.87
1.01
90.82
99.15
4.99
89.56
Table 3: VisDA (ID), MNIST/MNISTTM (OOD) Results.
4.3 Experiment Analysis
Score Ablation in PPF. We assess the effectiveness of our PPF mechanism in Fig. 5a, comparing PPF with GMM and LDA [67]. The results indicate that the PPF with GMM performs better results, such as 2.55% FPR95 improvement in CIFAR-IOC: -> Imagele task. This performance gain can be attributed to GMM's ability to model complex and non-linearly separable feature distributions, which more accurately capture the characteristics of real-world domain shifts. In contrast, LDA relies on the assumption of linear decision boundaries, which limits its capacity to handle such complexities.
Loss Ablation in IDT. We analyze the effectiveness of each component of IDT in Eq.14, 14, as shown in Table with the first row representing the zero-shot evaluation scenario. The results demonstrate that Lmid significantly improves FPR95 and AUROC, enhancing ID- binary classification by increasing the separation between ID and OOD samples in feature space. On complex datasets like VisDA, Lmid outperforms Lysd by +1.99% and +3.62% in ACCHæ, respectively. However, on simpler datasets like CIFAR-1OC, Lpsd performs better in Acchar as helps reduce intra-class noise, which is more beneficial for simpler features. Furthermore, setting À = O to evaluate the internediate-domall strategy shows that introducing the intermediate-domman improves performance by up to -5944% in FPR95, supporting our Hypothesis 2.

---

Analyze Backbones. In Fig. 5b, we utilize ViT-B/I6, ViT-B/32 and ResNet50 as the backbones in CIFAR-100C - MNIST task with CLIP. The results demonstrate that Open-IRT achieves the highest Mссии (e.g., 59.56% in ViT-B/16, 58.62% in ViT-B/32) and the lowest FPR95 (e.g.. 11.29% in ViT-B/16, 3.74% in ViT-B/32). These results highlight the robustness of Open-IRT, as it directly optimizes the feature space, independent of the backbone type, demonstrating its broad applicability.
Varying OOD Ratios. To examine the ro- bustness of Open-IRT, we conduct CIFAR- MNIST experiments on CLIP under different OOD ratios with additional exper- iments are appendix. As shown in Table 5, Open-IRT consistently achieves better results across all OOD ratios with Ассни fluctuate by only 1.01% %. This stability sug- gests that Open-IRT is better suited han- dle the uncertainties associated with fluctu- ating OOD proportions.
ratio
0.2
0.4
0.6
0.8
1.0
ZS-Eval [52]
75.55
75.60
75.59
75.59
75.60
TPT
75.77
75.78
75.81
75.76
75.78
TPT-C [12]
73.05
74.29
74.75
75.05
75.05
DPE [65]
65.67
66.12
56.38
29.98
27.60
OWTTT [66]
62.31
68.85
81.70
82.90
83.27
TDA [61]
72.45
75.04
77.54
77.91
77.06
ROSITA [52]
83.21
84.68
83.90
83.89
83.95
Open-IRT
85.10
85.53
85.08
84.63
84.52
Table 5: Experiments (Acснм) on CLIP with different
OOD ratios with CIFAR-10G (ID), and MNIST (OOD).
60
0.2
03
Figure 6: T-SNE Visualization with gray points as OOD.
T-SNE Visualization. We conduct T-SNE experiments of CIFAR-IOC MNIST. The T-SNE visualization in Fig. reveals that Open-IRT achieves better separation and more compact clustering within the ID classes, while also establishing clearer boundary between ID and OOD classes.
5 Conclusion
In this paper, we address the challenges of real-time adaptation in open-set environments with single- sample stream and propose the Open-set Intermediate -etrtttee-ereeeeeetnnn----eeee (Open-IRT) framework. Its Polarity-aware Prompt-based OOD Filter module leverages the rich cross- modal information of vision language models, corporating both absolute semantic alignment and relative semantic polarity. The Intermediate Domain-basess Test-time adaptation module constructs an intermediate domain and indirectly decomposes and enlarge the ID-OOD distributional discrepancy in real-time. Experiments across various benchmarks underscores the Open-IRT's potential to enhance the robustness and adaptability in dynamic real-world. Future work can focus on extending its application to real-world object detection or semantic segmentation. Moreover, tension exists between the method's 's requirement for sizable memory bank and its premise of single-image test- time adaptation. Future work should aim to reconcile this, achieving robust performance under strictly single-image setting.
Test-time adaptation
Acknowledgements: This work was supported by the National Key Research and Development Pro- gram of China (No. 2024YFE021 1000), in part by the National Natural Science Foundation of China (No. 62372329), in part by the Shanghai Scientific Innovation Foundation (No. 23DZ1203400), in part by the China Postdoctoral Science Foundation (No. BX20250383, GZB20250385, 2025M771530, 2025M771539) in part by Tongji-Qomon Autonomous Driving Commercial Vehicle Joint Lab Project, and in part by Xiaomi Young Talents Program.
10

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Reviews:
[{"review_id": "dZj6D8jNbB", "reviewer": "Reviewer_c5VY", "summary": "This paper addresses the challenge of open-set single-image test-time adaptation and introduces Open-IRT that incorporates a Polarity-aware Prompt-based OOD Filter (PPF) with an Intermediate Domain-based Test-time Adaptation (IDT) module. Instead of directly maximizing the distance between ID and OOD feature representations, this module constructs a \"middle domain\" in the feature space and optimizes a loss function that pushes both ID and OOD features away from this intermediate barrier. Experiments show that Open-IRT outperforms strong baselines on existing benchmarks.", "questions": "1. What are the performances compared with the most recent baseline [1]?\n2. There are too many hyperparameters (bank size B, K, alpha, lambda, as well as mu_I and mu_O in eq13). How are they selected?\n3. Detailed analysis of the computational latency per sample?\n\n[1] Noisy Test-Time Adaptation in Vision-Language Models, ICLR 2025", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths\n1. The paper addresses the Open-Set Single-Image Test Time Adaptation problem with Vision Language Models, which is a challenging and practical scenario.\n2. The paper is well written and easy to follow.\n3. The paper provides extensive experiments, showing the effectiveness and versatility of the proposed method.\n4. The idea of constructing middle domain is interesting.\n\nWeaknesses\n1. Lack of important comparision with most recent baseline [1].\n2. The improvement is limited compare to the best baseline (Table 1 and 3), and is even worse than the best baseline in some cases.\n3. The proposed Open-IRT framework is quite complex, involving multiple modules, feature/score banks, GMMs, KNN searches, and several loss terms. \n\n[1] Noisy Test-Time Adaptation in Vision-Language Models, ICLR 2025", "was_revised": true, "final_justification": "Initially, I have concerns about performance improvement, complexity, and compared with the SOTA. The author addressed my concerns well. Therefore, I recommend borderline acceptance. The authors should include the additional experiments in the paper.", "rebuttal": "### **Thank you for your valuable time and effort in reviewing our manuscript. We appreciate your positive feedback on our idea, as well as your recognition of the clarity and thoroughness of our writing and experimental design.**\n\nWe truly appreciate your thoughtful concerns. We believe that your concerns can be addressed through clarifications and further explanations in paper details. Below, we provide our clarification in detail, organized to address each of your concerns. \nWe have highlighted the key points in **bold** to enhance your reading experience and make it easier to navigate through the **important points**.\n\n---\n\n\n### **\\[Weakness 1] Additional Experimental Comparison of AdaND**\n\nThank you for your suggestion. We have noted the work by \\[1]. It introduces a new setting, **Zero-shot Noisy Test-time Adaptation (ZS-NTTA)** , which is different from ours. It adapt to noisy samples during test-time, focusing on noisy sample detection and classification accuracy.\n\nOur setting does not involve noisy conditions, so we did not run this experiment in the main body. However, we have incorporated this framework into our dataset and conducted additional experiments during the rebuttal phase. Despite improvements in the latest baseline, our method remains superior, such as 7.18 average FPR improvement, because our method excels in traditional single image OOD Detection tasks. \n\n---\n\n| Method (ViT‑B32) |         | MNIST             |        |        | SVHN              |        |        | Tiny‑ImageNet       |        |        |\n|:----------------:|:-------:|:-----------------:|:------:|:------:|:-----------------:|:------:|:------:|:-------------------:|:------:|:------:|\n|                  |         | AUC    ↑           | FPR ↓  | HM ↑   | AUC   ↑            | FPR ↓  | HM ↑   | AUC  ↑               | FPR ↓  | HM ↑   |\n| CIFAR‑10         | ZS‑Eval | 96.74             | 18.87  | 73.28  | 92.12             | 43.98  | 71.37  | 91.59               | 24.66  | 72.28  |\n|                  | TPT [4]    | 96.51             | 19.49  | 73.88  | 92.24             | 44.36  | 71.89  | 91.56               | 24.78  | 73.66  |\n|            | AdaND  [1]  | 98\\.79     | 5\\.97    | 79\\.48     | 91\\.96     | 39\\.30     | 74\\.63     | 98\\.71        | 4\\.30     | 80\\.31     |\n|                  | ROSITA [2]  | 99.18             | 3.82   | 81.72  | 93.39             | 33.47  | 74.59  | 98.88               | 4.12   | 80.93  |\n|                  | Open‑IRT | **99.57**         | **1.66** | **82.35** | **93.95**     | **32.19** | **75.78** | **99.11**           | **3.56** | **81.57** |\n| CIFAR‑100        | ZS‑Eval | 89.27             | 60.99  | 46.18  | 78.25             | 79.92  | 44.64  | 72.66               | 61.23  | 45.77  |\n|                  | TPT [4]     | 89.39             | 60.27  | 46.12  | 78.25             | 80.14  | 44.74  | 72.63               | 61.17  | 46.29  |\n|            | AdaND [1]   | 95\\.21     | 16\\.91    | 57\\.64     | 89\\.50     | 49\\.96     | 48\\.55     | 92\\.38        | 28\\.81     | 55.\\.89     |\n|                  | ROSITA [2] | 94.39             | 23.97  | 57.18  | 90.28             | 45.25  | 51.76  | 91.27               | 30.19  | 56.12  |\n|                  | Open‑IRT | **99.38**         | **3.71** | **58.75** | **94.09**     | **27.21** | **52.28** | **93.29**           | **22.30** | **56.97** |\n\n\n---\n\n\n### **\\[Weakness 2] Main Improvements of the Experiments**\n\nWe observe that in some cases (e.g., Table 1 and Table 3), accuracy improvements are limited. This is due to current baseline methods (e.g., ROSITA \\[2], OWTTT \\[3]) already reaching near-saturated performance on specific datasets (CIFAR-10C, VisDA). However, our experiments consistently show superior performance in most scenarios, as evidenced in the main text and supplementary tables (**Table 7, Table 10, and Table 11**).\n\nOur proposed intermediate domain approach enhances the separation between ID and OOD samples, leading to notable improvements in **FPR95**. For example, in Table 1 (CIFAR-10C to SVHN with CLIP), our method improves **FPR95** by **12.88%** compared to the second-best method. This is attributed to **Hypothesis 2**, where the intermediate domain concept facilitates better ID-OOD distinction.\n\nIn **Table 2** and **Table 3**, apart from near-saturated results (with **FPR95** < 10), our method also shows significant **FPR95** improvements. For instance, in the ImageNet-C to MNIST experiment (Table 2), we observe both an accuracy increase and a **7.49** improvement in **FPR95**, further validating the effectiveness of  **Hypothesis 2**.\n\n---\n\n### **\\[Weakness 3] Structural Complexity and Completeness of the Method**\n\nThank you for considering the complexity of our approach. While Open-IRT involves multiple components, each is essential and optimized to realize our core innovation: The **score bank** and **GMM** implement **Hypothesis 1**, while the **KNN search** and **loss terms** realize **Hypothesis 2**.\n\nOur design achieves significant performance gains with minimal overhead, including a 7.19\\% average reduction in FPR compared to the second-best method (**\\[Weakness 1]**) and an inference latency of just 40.15ms (**\\[Question 3]**), demonstrating real-time viability. Ablation studies (**Line 311, 317**) further confirm the module's effectiveness and necessity.\n\nThis is an efficient trade-off: Modularity enables the novel intermediate domain mechanism, while latency remains suitable for single-image deployment.\n\n---\n\n### **\\[Question 1] Additional Experimental Comparison and Analysis Regarding AdaND**\n\nPlease refer to the table provided in **\\[Weak 1]** section for the experimental results.\n\n\n---\n\n### **\\[Question 2] Hyperparameter Robustness Experiment**\n\n**Hyperparameters B and K:** We validated the robustness of **B** and **K** in **Figure 10** and **Table 8** (Appendix), with an analysis in **Line 779**. We tested values of **B = 64, 128, 256, 512** and **K = 0, 1, 3, 5**, and found that accuracy fluctuations were less than **0.70%**, indicating that reasonable adjustments to these values do not significantly affect results.\n\n**Hyperparameters $\\alpha$ and $\\lambda$:** The robustness analysis for **$\\alpha$** and **$\\lambda$** is presented in **Table 9**. As noted in **Line 796**, changes in **$\\alpha$ (0.1 to 0.5)** resulted in accuracy fluctuations of less than **0.33%**, and for **$\\lambda$ (0.1 to 0.5)**, fluctuations were under **0.57%**.  It is worth noting that some results in **Table 9** even slightly outperform those in the main experiments (**Table 1**, **Table 7**), as we did not perform deliberate hyperparameter tuning. We firmly believe that a robust method should not rely on selecting the optimal hyperparameter configuration but instead be adaptable to a wide range of real-world deployment scenarios.\n\n\n**Thresholds $\\mu_I$ and $\\mu_O$:** **$\\mu_I$** and **$\\mu_O$** are not hyperparameters. Defined in **Line 216**, they are dynamic thresholds computed from confidence values, adapting to sample distributions, thus avoiding robustness issues related to fixed hyperparameters.\n\n---\n\n### **\\[Question 3] Computational Efficiency of the Method**\n\nFrom an implementation efficiency perspective, the experimental results, including both overall dataset samples and single-sample analysis. Our method demonstrates excellent computational efficiency. With **a single 3090 GPU**, the average execution time is **40.15ms**, making it suitable for lightweight deployment. These results are expected to improve further with advancements in computational power and hardware performance.\n\n---\n\n| ID        | OOD          | Samples | Total Time (s) | Time per Sample (ms) |\n|----------------|-------------------|---------|----------------|------------------------|\n| CIFAR‑100C     | MNIST             | 20000   | 823.12         | 41.16                  |\n| CIFAR‑100C     | SVHN              | 20000   | 763.93         | 38.20                  |\n| CIFAR‑100C     | Tiny‑ImageNet     | 20000   | 766.14         | 38.31                  |\n| CIFAR‑100C     | CIFAR‑100C        | 20000   | 765.17         | 38.26                  |\n| CIFAR‑10C      | MNIST             | 20000   | 901.36         | 45.07                  |\n| CIFAR‑10C      | SVHN              | 20000   | 779.13         | 38.96                  |\n| CIFAR‑10C      | Tiny‑ImageNet     | 20000   | 767.28         | 38.36                  |\n| CIFAR‑10C      | CIFAR‑100C        | 20000   | 789.26         | 39.46                  |\n| ImageNet‑C     | MNIST             | 20000   | 816.28         | 40.81                  |\n| ImageNet‑C     | SVHN              | 20000   | 807.21         | 40.36                  |\n| VisDA          | MNIST             |100000   |4096.92         | 40.97                  |\n| VisDA          | SVHN              |100000   |4107.22         | 41.07                  |\n| VisDA          | MNISTM            |100000   |4100.85         | 41.01                  |\n\n| Method                 | Mean Inference Time (ms) |\n|------------------------|---------------------------|\n| Open‑IRT               | 40.15                     |\n| ROSITA                 | 41.39                     |\n| TPT                    | 108.52                    |\n| ZS‑Eval (Inference only) | 12.05                  |\n| AdaND                  | 42.88                     |\n---\n\n### **Reference**\n[1] Noisy Test-Time Adaptation in Vision-Language Models, ICLR 2025\n\n[2] Sreenivas, Manogna, and Soma Biswas. \"Efficient Open Set Single Image Test Time Adaptation of Vision Language Models.\" arXiv preprint arXiv:2406.00481 (2024).\n\n[3] Li, Yushu, et al. \"On the robustness of open-world test-time training: Self-training with dynamic prototype expansion.\" Proceedings of the IEEE/CVF ICCV. 2023.\n\n[4] Shu, Manli, et al. \"Test-time prompt tuning for zero-shot generalization in vision-language models.\" NeurIPS 35 (2022): 14274-14289."}, {"review_id": "6YNjCpHtah", "reviewer": "Reviewer_YfPH", "summary": "This paper addresses the problem of open-set single-image test-time adaptation (TTA), where in-distribution (ID) and out-of-distribution (OOD) samples appear unpredictably. The proposed framework comprises two main components: (1) Polarity-aware Prompt-based OOD Filter (PPF) and (2) Intermediate Domain-based Test-time adaptation (IDT). The PPF computes a sample-wise score by combining absolute semantic alignment (between image features and positive prompts) and relative semantic polarity (between image features and corresponding positive/negative prompts). This score is then modeled via a Gaussian Mixture Model (GMM) to classify samples as either ID or OOD. The IDT constructs intermediate domain features and uses contrastive learning (via InfoNCE loss) to encourage separation between ID and OOD features from the intermediate representations.", "questions": "Refer to weaknesses above.\n\nAdditional question: How are the positive and negative prompts learned?", "limitations": "N.A.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 1, "significance": 2, "originality": 2, "strengths_and_weaknesses": "* Strengths\n1. The paper introduces a novel and practically relevant setting: open-set single-image test-time adaptation.\n2. The proposed method performs competitively on standard benchmarks.\n\n* Weaknesses\n1. Technical clarity and presentation\n\n1-1. The introduction lacks intuitive motivation. For example, the rationale behind using intermediate domain features to distinguish ID and OOD samples is not clearly justified. The term “polarity” is also introduced abruptly and is only clarified later in (3).\n\n1-2. Lines 144–148 just provide a description of the method, but the underlying motivation and the importance of the proposed design are not sufficiently justified.\n\n1-3. There are mismatches between terminology used in figures and text. For instance, the terms 'Sim_pos’, 'Sim_neg’, and 'Sim_diff’ in Figure 2 are not consistently reflected in the main text. `Prototypes’ in Figure 2 are mentioned as prompts in the main text.\n\n1-4. The sliding window strategy is not clearly defined. Is it equivalent to a FIFO queue? This should be clarified.\n\n1-5. $f_s$ is a multi-dimensional vector, so applying variance directly is questionable; covariance may be more appropriate unless the computation is done per-channel. This needs clarification.\n\n1-6. While (8) uses samples that satisfy ($y^+ = y_p$), this constraint is not applied to (9). The motivation behind this inconsistency should be discussed.\n\n1-7. What happens when the sample does not belong to the two cases in (7), (10), and (13), i.e., $\\mu_O <= Conf(x) <= \\mu_I$? The current decision rule does not account for such cases.\n\n\n2. In (13), the use of $-L_{CE}$ for OOD samples aims to suppress intra-class noise, but this may unintentionally distort OOD representations.\n\n3. The method depends heavily on the threshold parameters $\\mu_I$ and $\\mu_O$. Empirical analysis of sensitivity to these hyperparameters should be provided.\n\n4. L202: While the proposed method attempts to distance both ID and OOD samples from the intermediate domain representations, it is possible that ID and OOD samples may still be drawn closer to each other (e.g., if vectors $F_I - F_M$ and $F_O - F_M$ are aligned). This could undermine the objective of separation and should be theoretically or empirically addressed.\n\n* Overall evaluation\n\nOverall, this paper tackles a challenging and underexplored problem in open-set single-image TTA and provides an reasonable approach leveraging vision-language models. However, the current version suffers from unclear technical presentation and insufficient explanation of key design choices.", "was_revised": true, "final_justification": "Overall, the rebuttal has significantly improved my understanding of the paper and strengthened the value of the proposed approach. Consequently, I will revise my recommendation to borderline acceptance.", "rebuttal": "### **Thank you for your time and consideration. We appreciate your recognition of the novelty and practicality of our method.**\n\nWe believe that your concerns can be addressed by providing further clarification of the technical details, allowing us to reach a mutual understanding.  We would like to highlight the **original details** corresponding to the **bolded lines** in the rebuttal section, as they provide further clarification and location in paper. Your attention to these details would be greatly appreciated.\n\n---\n\n### **\\[1-1] Motivation Behind the Method in the Introduction**\n\nThe motivation behind our Open-IRT method is intuitively presented in **Figure 1** and its caption (**Line 47**) and **Hypothesis 1 & 2**: the separation of ID and OOD distances through an intermediate domain, with the focus on expanding the distances within the separated regions to achieve a better distinction. Due to space limitations in the Introduction, we were unable to elaborate on the complete motivation for the method or specific terms (such as \"polarity\"). Therefore, the full motivation for our method is elaborated in the **Methodology Section**, particularly around **Hypothesis 1 (Line 47)** and **Hypothesis 2 (Line 191)**.\n\nFor **Hypothesis 1**, we design an ID/OOD scoring system based on the differential representation capabilities of the visual-language model's text encoder for prompts. In **Hypothesis 2**, we construct an intermediate domain. To substantiate the validity of the intermediate domain, we provide both **Theoretical Justification (Line 559)** and experimental verification (**Line 323**) in the appendix, along with a clear visualization of the intermediate domain (**Figure 4**).\n\n---\n\n### **\\[1-2] L144-L148 Motivation of the Method in the Introduction Section**\n\nAs discussed in **[1-1]**, we provided only a brief overview of the motivation for the method in **Figure 1 (Line 47)**. A more detailed explanation is found in **Hypothesis 1 (Line 47)**, and **Hypothesis 2 (Line 191)**. The theoretical foundations for the method are further analyzed and validated in the supplementary materials (**Lines 487-583**).\n\n---\n\n### **\\[1-3] Explanation of Symbols in Figure 1**\nDue to the need for clarity and conciseness, we opted for abbreviations in **Figure 1**. This process corresponds to **Equation 3 (Line 164)**, where:\n\n$sim\\_{pos} = sim(f, p\\_c)$, $sim\\_{neg} = sim(f, p_c')$, $sim\\_{diff} = \\left| sim(f, p\\_c) - sim(f, p\\_c') \\right|$\n\nThese quantities are used to compute scores in Equation 3. In the context of the paper, **Prototypes** refers to the embedded prompt tensors.\n\n---\n\n### **\\[1-4] Definition of Sliding Window Strategy**\n\nWe adopt the standard definition of Sliding Window in sequence data processing from computer science \\[1], where, in the context of our paper, it functions equivalently to a FIFO queue.\n\n---\n\n### **\\[1-5] $f\\_s$ Computation Paradigm**\n\nThe method for calculating the variance of $f_s$ has been validated for its rationality in \\[2], where it is computed across spatial dimensions independently for each feature channel. The feasibility of this approach lies in preserving channel-specific style information. The variance for each channel emphasizes fine-grained structural characteristics in that feature dimension, such as color, texture intensity, etc., without mixing with other channels, thereby achieving refined style representation and alignment.\n\n---\n\n### **\\[1-6] Differences between Equation 8 and Equation 9**\n\nThe differences between **Equation 8** and **Equation 9** arise from the distinction between **ID** and **OOD** categories in the standard **OOD detection** task \\[3]. The overall setup of the OOD detection task is to first perform **ID/OOD** binary classification, followed by internal classification within **ID** data \\[3], without considering internal classification for **OOD** data. Therefore, in **Equation 8**, for ID data, the model needs to learn fine-grained semantic consistency and achieve precision for each class, which is why we set $y^{+} = \\hat{y}\\_{p}$, where $\\hat{y}\\_{p}$ is the pseudo-label designed for ID data (**Line 239**). Similarly, in the experiments, our accuracy metrics are also based on class-level accuracies (**Line 262, 655**). For OOD samples, since their class labels differ from those of ID samples, including $y^{+} = \\hat{y}\\_{p}$ in the calculation would render this condition meaningless.\n\nWe assume that the distinction between fine-grained classification for ID data and binary classification for OOD data is well-understood in the context of OOD Detection. Therefore, we did not elaborate further in the main text.\n\n---\n\n### **\\[1-7] Exceptions for $\\text{Conf}(x) > \\mu_{I}$ or $\\text{Conf}(x) < \\mu_{O}$**\nAs shown directly in **Equation 13**, the loss term $L\\_{psd}$ is calculated only when $\\text{Conf}(x) > \\mu_{I}$ or $\\text{Conf}(x) < \\mu_{O}$; otherwise, there is no backpropagation of loss, and the sample is directly used for inference (**Line 251**). The same applies to **Equations 7 and 10**. This approach avoids introducing noisy information due to ambiguous confidence into the model's learning process. For clarity, we will explicitly state that the loss is zero when the conditions $\\text{Conf}(x) \\leq \\mu_I$ and $\\text{Conf}(x) \\geq \\mu_O$ are met.\n\n---\n\n### **\\[2] The Impact of $-L\\_{CE}$ on OOD Samples**\n\nIn fact, for a standard **OOD Detection** task setup \\[3], we only need to perform binary classification between **ID** and **OOD**, and focus on **class-level** classification for **ID** samples without needing to perform fine-grained classification for **OOD** samples. As shown in **Figure 6**, existing OOD Detection methods (TPT[4], ROSITA[5], Open-IRT) introduce some distortion in the feature distribution of OOD data compared to direct inference (ZS-Eval). This is a well-known phenomenon in this field and does not negatively impact the evaluation metrics.\n\nAdditionally, it can be observed that our **$-L\\_{CE}$** method pushes samples away from intra-class noise. Consequently, the resulting impact on the distribution of **OOD** samples is a normal occurrence and does not interfere with the **ID-OOD** binary classification, nor does it affect the fine-grained classification results within **ID** classes.\n\n---\n\n\n### **\\[3] Values of $\\mu_I$ and $\\mu_O$, and Hyperparameters Robustness Experiment**\n\nIn fact, $\\mu_I$ and $\\mu_O$ are not hyperparameters. Their definitions are provided in **Line 216**, where they are dynamically calculated mean values based on the confidence of the samples. By dynamically computing the confidence mean, we are able to adjust what is considered high confidence and low confidence according to different distributions, thus avoiding the limitations of fixed thresholds.\n\nFurthermore, the main performance improvement in our method is driven by the two theoretical mechanisms proposed in **Hypotheses 1 & 2**. As seen in the ablation study (**Line 317**), the choice of threshold is merely a technical means in the implementation process and is not the primary contribution. \n\nAdditionally, other hyperparameter experiments (B, K, $\\alpha$, $\\lambda$) can be found in Sections C.2 (Line 770) and C.3 (Line 779) of the supplementary materials. Our method demonstrates robustness with respect to these hyperparameters. For example, the accuracy fluctuation due to $\\alpha$ values ranging from 0.1 to 0.5 is less than 0.33%, and for $\\lambda$ values between 0.1 and 0.5, the accuracy fluctuation is less than 0.57%.\n\n---\n\n### **\\[4] Optimization Goal and Theory for the Distances between $\\mathcal{F}_I$, $\\mathcal{F}_M$, and $\\mathcal{F}_O$**\n\nBy examining the optimization goals in **Equation 8 & 9** (**Line 233 & 235**), it can be seen that they separately promote the separation between $\\mathcal{F}_I$ and $\\mathcal{F}_M$, as well as between $\\mathcal{F}_O$ and $\\mathcal{F}_M$. They also facilitate the separation of $\\mathcal{F}_I$ and $\\mathcal{F}_O$ through contrastive learning of positive and negative sample pairs. This fundamentally prevents the alignment between $\\mathcal{F}_I - \\mathcal{F}_M$ and $\\mathcal{F}_O - \\mathcal{F}_M$. Our method consistently improves FPR metrics, demonstrating better ID and OOD separation (**Figure 6**). As shown in Table 1 (CIFAR-10C to SVHN with CLIP), it boosts FPR95 by 12.88% over the second-best method.\n\n\nBased on **Line 208**, we visualize the result of the intermediate domain in **Figure 4**. It is clear from the figure that, according to our construction method, the intermediate domain is theoretically situated precisely between **ID** and **OOD**, which aligns with the situation set in the theoretical analysis of the supplementary materials (**Line 559**). This intuitively demonstrates the theoretical foundation of Hypothesis 2.\n\n---\n\n### **\\[Additional question] How is the prompt learned?**\n\nThe definitions of prompts are given in **Line 152**. We did not design updatable prompts because the core contribution of our paper lies in constructing an intermediate domain from the data perspective, and this design does not conflict with the use of updatable prompts. A detailed discussion of the update module can be found in **Line 665** of the supplementary materials.\n\n---\n\n### **Reference**\n[1] Ganardi, Moses, Danny Hucke, and Markus Lohrey. \"Derandomization for Sliding Window Algorithms with Strict Correctness∗.\" Theory of Computing Systems 65.3 (2021): 1-18.\n\n[2] Huang, Xun, and Serge Belongie. \"Arbitrary style transfer in real-time with adaptive instance normalization.\" ICCV. 2017.\n\n[3] Yang, Jingkang, et al. \"Generalized out-of-distribution detection: A survey.\" IJCV 132.12 (2024): 5635-5662.\n\n[4] Shu, Manli, et al. \"Test-time prompt tuning for zero-shot generalization in vision-language models.\" NeurIPS 35 (2022): 14274-14289.\n\n[5] Sreenivas, Manogna, and Soma Biswas. \"Efficient Open Set Single Image Test Time Adaptation of Vision Language Models.\" arXiv preprint arXiv:2406.00481 (2024)."}, {"review_id": "CAUTlw24yE", "reviewer": "Reviewer_4p14", "summary": "The authors propose a novel open-set test-time adaptation method designed to handle single-image inputs. The approach first classifies each test sample as in-distribution (ID) or out-of-distribution (OOD) using the Polarity-aware Prompt-based OOD Filtering (PPF) module, which computes similarity scores with both positive and negative prompts. These scores are modeled with a Gaussian Mixture Model (GMM) to distinguish ID from OOD samples. Once classified, the model performs adaptation via an Intermediate Domain-based Test-time adaptation (IDT) module, which constructs a middle domain between ID and OOD representations. Adaptation is guided by a contrastive loss and a bidirectional pseudo-labeling strategy to refine the feature space and improve ID-OOD separation over time.", "questions": "1. The supplementary material discusses performance across different batch sizes, but does not report results for very small batches (e.g., B=1 or B=8). Could the authors include results in that regime, or comment on how the method degrades with decreasing sample support?\n\n2. The method relies on negative prompts (e.g., \"a photo of no [CLS]\") for OOD detection. However, prior work (e.g., CLIPN, NegPrompt) reports that such prompts can be unreliable with CLIP. Could the authors provide more insight into how their specific formulation avoids these pitfalls, or whether other prompt designs were tested?\n\n3. Could the authors elaborate on the design and selection of prompts (positive and negative)? For example, how sensitive is the method to the phrasing of prompts? Did they explore alternatives such as learned or templated prompt tuning?\n\n4. During early test-time steps when only a few samples are available, how stable is the GMM estimation? Could the authors elaborate on how bootstrapped resampling helps here, and whether other techniques were considered?", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths:\n- The paper is well written and clearly structured, making the proposed method easy to understand and potentially straightforward to reproduce.\n- The approach is extensively compared against a wide range of state-of-the-art baselines across multiple datasets, consistently showing strong performance.\n- The authors conduct several relevant ablation studies, including the impact of different score modeling methods (e.g., GMM vs. LDA) and the adaptation loss components.\n- The supplementary material is thorough and provides additional insights, which strengthens the overall experimental validation.\n\nWeaknesses:\n- Although the method targets the single-image test-time adaptation setting, it still relies on feature and score banks that accumulate information over multiple samples. Therefore, the title and claims of true single-image operation may be slightly overstated. In addition, the supplementary study on batch size does not explore values below B=64; evaluating lower values would be important to fully support the single-image claim, even if performance degrades.\n- The method employs negative prompts such as “a photo of no [CLS]” to distinguish OOD samples. However, recent works (e.g., Learning Transferable Negative Prompts for OOD Detection, 2024; CLIPN for Zero-Shot OOD Detection, 2023) suggest that CLIP often struggles with such formulations. A more detailed discussion on prompt engineering choices and their limitations would strengthen the paper and help clarify whether this design choice generalizes well.\n- While the authors introduce bootstrapped resampling to stabilize GMM estimation with few samples, there is limited quantitative or qualitative evidence that this improves performance. It would be useful to understand how sensitive the method is to GMM misestimation early in adaptation.", "was_revised": true, "final_justification": "Thank you for the detailed and thoughtful rebuttal. I appreciate the additional experiments and clarifications provided, which help to address some of the concerns I raised.\n\nThat said, I remain somewhat concerned about the method’s reliance on a sufficiently large memory bank for GMM estimation. While the reported results suggest that performance stabilizes for B≥16, this requirement appears to contradict the claim that the method is applicable in a truly single-image test-time scenario. In practice, needing several images beforehand seems to weaken the core promise of single-image adaptability. I would encourage the authors to clarify this nuance more explicitly in the paper to avoid potential overstatement.", "rebuttal": "### **Thank you for the valuable time you spent reviewing our manuscript. We appreciate your recognition of the readability of our writing and the thoroughness of our experiments.** \n\nThank you for your insightful comments. In response, we have conducted additional verification experiments. We believe your concerns can be addressed through these experiments and the additional clarifications provided. **Key points** have been highlighted in **bold** to help you easily locate the relevant parts in the paper.\n\n\n---\n\n### **\\[Weakness 1] Performance with Small B Values (B<64)**\n\nWe appreciate your careful reading, including the supplementary materials. Your point is valid, and we have addressed it by including experiments with different values of **B = 4, 8, 16, 32, 64, 128, 256, 512, 1024** based on CLIP, specifically for the CIFAR-10C to MNIST task. The results are presented in the table below:\n\n---\n| B        | 4      | 8      | 16     | 32     | 64     | 128    | 256    | 512    | 1024  |\n| :------- | :----- | :----- | :----- | :----- | :----- | :----- | :----- | :----- | :----- |\n| Accuracy  | 81\\.35 | 81\\.65 | 83\\.83 | 84\\.26 | 84\\.40 | 84\\.53 | 84\\.55 | 84\\.67 | 84\\.48 |\n\nThe results confirm that extremely small values of B lead to performance degradation due to insufficient data for reliable GMM estimation. However, when B ≥ 16, the accuracy stabilizes, indicating that our method is robust under a wide range of practical settings.\n\n\n---\n### **\\[Weakness 2] Effectiveness of using negative prompts**\nYour question is critical, and we have carefully considered this aspect in both the method design and theoretical analysis. As detailed in **Section A.2** of the supplementary materials, we address the theoretical validity of the negative prompt. Specifically, in **Line 525**, we acknowledge that VLMs face known challenges in implementing human-like negation understanding [3]. However, the effectiveness of the negative polarity prompt in our framework does not entirely depend on the perfect negative logical reasoning ability of the VLM. Instead, it leverages the differential representation capabilities of the text encoder: the vocabulary difference between the positive and negative prompts, $w_c^+$ and $w_c^-$, naturally leads to different embedding vectors $p_c$ and $p_c'$ in the shared embedding space. Our framework utilizes this induced representational difference as a discriminative signal for OOD detection. This idea is reflected in the design of the polarity gap item in **Equation 3** (**Line 158**). It is also intuitively visualized in **Figure 3**.\n\n\n---\n\n### **\\[Weakness 3] Effectiveness of GMM in the Early Adaptation Phase**\n\nThank you for raising this insightful point. From a technical standpoint, during the early adaptation stage of our method, we deliberately delay the fitting of the GMM and the computation of the corresponding loss until a sufficient number of samples (i.e., bank size $B$) has been accumulated. In other words, to avoid the uncertainty introduced by small sample sizes propagating to the model through loss gradients, we perform direct inference for those samples instead, without engaging in backpropagation. We acknowledge that predictions for these samples may be relatively weak.\n\nTherefore, there are two main motivations behind incorporating the bootstrapped resampling method: \n\n1. OOD detection inherently depends on modeling data distributions, which requires a sufficient number of samples to distinguish ID from OOD instances effectively.\n\n2. GMM fitting relies on the statistical properties of the data; with limited samples, parameter estimates may deviate from the true distribution, degrading performance.\n\nThe following table presents the experimental results to support this design:\n\n| **B**    | **4**   | **8**   | **16**   | **32**  | **64**  | **128**  | **256** | **512** | **1024** |\n|----------|---------|---------|---------|---------|---------|---------|---------|---------|---------|\n| **w/ Resample**     | 81.35  | 81.65  | 83.83  | 84.26  | 84.40  | 84.53  | 84.55  | 84.67  | 84.48  |\n| **w/o Resample**    | 81.24  | 81.54  | 83.78  | 84.24  | 84.38  | 84.52  | 84.54  | 84.67  | 84.48  |\n| **Δ Accuracy (↑)**       | +0.11  | +0.11  | +0.05  | +0.02  | +0.02  | +0.01  | +0.01  | +0.00  | +0.00  |\n\nBased on our experiments under the conditions of $B = 4$ and $B = 8$, we effectively simulate scenarios where the memory bank contains only a small number of samples during the early adaptation stage. The results demonstrate that the bootstrapped resampling strategy successfully mitigates performance degradation caused by insufficient data during GMM fitting.\n\nMoreover, when the bank size $B$ is sufficiently large, the impact of bootstrapped resampling becomes marginal. This is expected, as no loss backpropagation occurs during the early inference stage, and thus, performance is less sensitive to sample size once adequate data is accumulated.\n\nOverall, our experiments suggest that as long as a reasonable $B$ value is chosen—avoiding extremely small settings—the proposed method remains robust and performs consistently across different configurations.\n\n\n---\n\n### **\\[Question 1] Performance under Very Small B**\n\nAs discussed in **\\[Weakness 1]**, model performance degrades when $B$ is very small, primarily because GMM fitting requires a minimum number of samples to produce reliable estimates. However, we observe that when $B > 16$, the model performance stabilizes and reaches saturation. \n\n---\n\n### **\\[Question 2] Theoretical Validity of Negative Prompts**\n\nAs discussed in **\\[Weakness 2]**, we elaborate on the theoretical rationale of using negative prompts in **Section A.2** of the supplementary material (**Line 525**). Our method does not rely on the VLM’s precise understanding of semantic negation. Instead, it leverages the **representational difference** between positive and negative prompts. \n\n---\n\n### **\\[Question 3] Choice of Prompt Format**\n\nYes, we explored the prompt format based on the discussion in CLIPN \\[1]. In practice, vision-language models (VLMs) are known to be sensitive to the phrasing of input prompts, and carefully crafting prompt formats can indeed improve performance. However, we intentionally adopted a simple and consistent format — “*a photo of no \\<class\\>*” — for two key reasons.\n\n1. We aimed to ensure fairness across all baseline comparisons. All baselines use the prompt “*a photo of a \\<class\\>*” for inference. To maintain consistency and minimize bias, we chose a minimal modification of that format for negative prompts. \n\n2. All methods, including ours and baselines, utilize the same CLIP-based text encoder. Since a well-optimized prompt would benefit all methods equally, we argue that the main performance differences stem from algorithmic and theoretical innovations, rather than from prompt engineering.\n\nOur method focuses on data-level improvements by constructing an intermediate domain. This perspective is theoretically and practically complementary to works based on learnable or tunable prompts \\[4–6]. As such, our contributions emphasize data-driven design and theoretical insights. We also briefly discuss the learnable module option in the supplementary material (**Line 665**).\n\nWe now provide results on CIFAR-100C to MNIST using negative prompts introduced in CLIPN \\[1], as follows:\n\n---\n\n| Prompt Phrase                     | Accuracy (%) |\n|----------------------------------|--------------|\n| a photo of no `<class>`          | 62.01        |\n| a photo without `<class>`        | 61.97        |\n| a photo not appearing `<class>`  | 62.12        |\n| a photo not contain `<class>`    | 62.19        |\n\nFrom the experimental results, we observe that different prompt formats do slightly impact model performance. This is due to the sensitivity of **Equation 3** to the input text prompts, where different textual inputs lead to different computed scores. Notably, some prompts even yield better performance than our original formulation.\n\nAlthough different negative prompts lead to marginal performance differences (±0.1%), we intentionally avoided prompt optimization to ensure fair comparisons. This further highlights that our gains stem from algorithmic contributions rather than prompt engineering.\n\n---\n\n### **\\[Question 4] Effectiveness of GMM in the Early Stage**\n\nAs discussed in **\\[Weakness 3]**, we employ bootstrapped resampling to address the challenge of limited samples during the early stages of training. This design is motivated by two key considerations: (1) OOD detection fundamentally relies on characterizing the distributional differences between ID and OOD data, which becomes more effective as the sample size increases; and (2) GMM fitting depends on sufficient data to reliably estimate the underlying statistical properties of the distribution.\n\nAs shown in the experiments under **\\[Weakness 3]**, once a sufficient number of samples is accumulated, the effect of this strategy becomes negligible in terms of performance improvement. From a theoretical perspective, alternative sampling strategies could also serve to increase the diversity of the feature bank and alleviate the sparse-sample issue in early training stages. \n\n---\n\n### **Reference**\n[1] Wang, Hualiang, et al. \"Clipn for zero-shot ood detection: Teaching clip to say no.\" ICCV. 2023.\n\n[2] Yang, Jingkang, et al. \"Generalized out-of-distribution detection: A survey.\" IJCV 132.12 (2024): 5635-5662.\n\n[3] Li, Tianqi, et al. \"Learning transferable negative prompts for out-of-distribution detection.\" CVPR. 2024.\n\n[4] Zhou, Kaiyang, et al. \"Learning to prompt for vision-language models.\" IJCV 130.9 (2022): 2337-2348.\n\n[5] Zhou, Kaiyang, et al. \"Conditional prompt learning for vision-language models.\" CVPR . 2022.\n\n[6] Kan, Baoshuo, et al. \"Knowledge-aware prompt tuning for generalizable vision-language models.\" ICCV. 2023."}, {"review_id": "GKq42f4DLm", "reviewer": "Reviewer_g3am", "summary": "This paper introduces Open-IRT, a single-image open-set Test-Time Adaptation framework for VLMs. The method proposes to use semantical text understanding to build a statistical barrier between ID and OOD samples. The method comprises different procedures, including preserving a memory of scores and a memory of features, the introduction of an intermediate domain based on confidence thresholds, and the employment of InfoNCE to contrast each image with respect to this intermediate domain. The method is evaluated on a series of popular TTA benchmarks, and compared to previous adaptation methods applied on CLIP and MaPLe, showing competitive results in harmonic accuracy and OOD detection metrics.", "questions": "1. Harmonic accuracy is used to cover both ID accuracy and detection accuracy, but what is the traditional ID accuracy for the experiments). Open-set TTA is a combination of two problems: a) OOD detection, and b) ID classification. This harmonic accuracy does not allow us to see at which of both does Open-IRT excel at, or if it is actually competitive in both. \n2. How can we be sure that the good detection performance (problem a)) is not due to the fact that the ID images are corrupted (or synthetic in the case of VisDA-C), and the OOD images are not? Using corrupted versions of the OOD datasets would be beneficial. In the opposite sense, what happens natural images are used as ID datasets? Think of Imagenet, CIFAR-10, CIFAR-100, etc. \n3. Is the final ID/OOD score the one computed from Eq. 3? This part is not very clear. As I mentioned before, although the writing is clear zooming in, the narrative can get confused when we zoom out Section 3. \n4. What's the performance of other closed-set methods? TENT for instance, can be seamlessly applied on VLMs by just doing entropy minimization on the softmax cosine similarity logits. Moreover, other recent methods have been proposed: WATT(Neurips '24, model ensembling), OSTTA (ICCV '23, confidence-based detection), which are not included.", "limitations": "Yes", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strenghts:\n\n-The ideas are novel and tackle important issues in the field of (open-set) TTA: single image adaptation, modelling of the ID-OOD distributions without using batches of images, and explicitly enforcing separability between ID and OOD samples in the feature space (which is not often done, with exceptions like UniEnt).\n-Experiments include different popular benchmarks and a significant number of baselines on two different VLMs, showing competitive results.\n-Using InfoNCE to create and contrast an intermediate domain is sound and clever.\n\nWeaknesses:\n\n-The paper is well written and generally clear, but information is very condensed and feels crowded by the end of Section 3; a better narrative could be possible. \n-Most of the most recent open-set TTA methods are evaluated against Open-IRT, however, the performance of other closed-set strategies is overlooked, specially in VLMs. For instance, methods such as WATT, TENT or OSTTA (pioneer in open-set) could be present for completeness.\n-Although several benchmarks are included, using the standard Imagenet (or any other representative of natural images) would further demonstrate to what extent this method works because of its open-set detection capabilities, an not only due to the fact that some images are corrupted and the OOD datasets are not. Similarly, having corrupted versions of MNIS, SVHN, etc., would aid to the completness of the study.", "was_revised": true, "final_justification": "The paper provides a new method to deal with OOD TTA, a field that is just recently gaining traction in the context of VLMs. The method is sound, and the results demonstrate to be competitive. A final version of this paper would require adding additional results as the ones mentioned in my review, as well as improving the storytelling of the method for clarity, as it is charged with many concepts that need to be processed by the reader. I suggest a borderline accept and leave the final decision to the judgment of the Area Chair.", "rebuttal": "### **We sincerely thank you for your time and effort in reviewing our work. We are grateful for your recognition of the novelty of our idea and the robustness of our methodology.** \n\nThank you for your feedback on the experimental section. We believe that your concerns can be addressed through a review of the original details, as well as the additional experiments and analysis provided. To facilitate your reading and help you locate **key points** in the original text, we have highlighted the important parts in **bold**. \n\n---\n\n### **\\[Weakness 1] Condensed Information in Paper**\nWe sincerely appreciate your recognition of the detailed aspects of our work, as well as your constructive suggestions regarding the overall structure. In our writing, we aimed to convey as much relevant information as possible to facilitate understanding, which may have led to an overly condensed presentation. In future revisions, we will streamline certain parts of the text and reallocate some of the detailed content to the supplementary material for improved clarity and readability.\n\n---\n\n### **\\[Weakness 2] Additional Baselines (Tent \\[2], WATT \\[3], OSTTA \\[1])**\n\nThank you for your suggestion. Our work specifically targets **single-image-level test-time adaptation (TTA)** in **open-set recognition**. Many classical TTA methods, including Tent, WATT, and OSTTA, were not originally designed for this specific setting. To ensure a fair comparison, we adapted these methods accordingly (Line 660) to align with our evaluation protocol.\n\nDue to space limitations, we have to adopt representative baselines in the main paper. In response to your comments, we have conducted additional experiments incorporating Tent, WATT, and OSTTA. Specifically:\n\n1. For the classical closed-set TTA method **Tent**, we integrated it with our original Zero-shot Evaluation (ZS-Eval) setup.\n2. For **WATT** and **OSTTA**, we applied a fair and consistent evaluation strategy as described in Line 660.\n\n| Method (ViT‑B32) |         | MNIST             |        |        | SVHN              |        |        | Tiny‑ImageNet       |        |        | \n|:----------------:|:-------:|:-----------------:|:------:|:------:|:-------------------:|:------:|:------:|:-------------------:|:------:|:------:|\n|                  |         | AUC  ↑            | FPR ↓  | HM ↑   | AUC  ↑            | FPR ↓  | HM ↑   | AUC ↑               | FPR ↓  | HM ↑   |\n| CIFAR‑10         | ZS‑Eval | 96.74             | 18.87  | 73.28  | 92.12             | 43.98  | 71.37  | 91.59               | 24.66  | 72.28  |\n|                  | Tent [2] | 96.88            | 17.95  | 73.84  | 92.24             | 42.70  | 71.91  | 91.72               | 23.90  | 72.64  |\n|                  | TPT [4]    | 96.51             | 19.49  | 73.88  | 92.24             | 44.36  | 71.89  | 91.56               | 24.78  | 73.66  |\n|                  | WATT [3] | 96.59            | 18.98  | 74.30  | 92.18             | 43.79  | 72.20  | 91.58               | 24.33  | 73.79  |\n|                  | OSTTA [1] | 97.21           | 14.53  | 76.34  | 92.63             | 41.87  | 73.00  | 91.91               | 23.27  | 75.40  |\n|                  | ROSITA [5]  | 99.18             | 3.82   | 81.72  | 93.39             | 33.47  | 74.59  | 98.88               | 4.12   | 80.93  |\n|                  | Open‑IRT | **99.57**         | **1.66** | **82.35** | **93.95**     | **32.19** | **75.78** | **99.11**           | **3.56** | **81.57** |\n| CIFAR‑100        | ZS‑Eval | 89.27             | 60.99  | 46.18  | 78.25             | 79.92  | 44.64  | 72.66               | 61.23  | 45.77  |\n|                  | Tent [2] | 89.42            | 59.81  | 46.53  | 78.44             | 78.67  | 44.87  | 72.85               | 60.32  | 45.84  |\n|                  | TPT [4]     | 89.39             | 60.27  | 46.12  | 78.25             | 80.14  | 44.74  | 72.63               | 61.17  | 46.29  |\n|                  | WATT [3] | 89.28            | 60.22  | 46.50  | 78.36             | 79.40  | 45.02  | 72.69               | 60.87  | 46.49  |\n|                  | OSTTA [1] | 92.77           | 24.41  | 54.31  | 88.69             | 51.03  | 47.23  | 91.90               | 29.65  | 54.47  |\n|                  | ROSITA [5]  | 94.39             | 23.97  | 57.18  | 90.28             | 45.25  | 51.76  | 91.27               | 30.19  | 56.12  |\n|                  | Open‑IRT | **99.38**         | **3.71** | **58.75** | **94.09**     | **27.21** | **52.28** | **93.29**           | **22.30** | **56.97** |\n\nWe observe that OSTTA shows the most notable improvement due to its alignment with open-set conditions. Tent and WATT primarily focus on test-time adaptation without explicitly modeling the distinction between ID and OOD data. Although OSTTA is more suitable for open-set scenarios, it still falls short in adaptability compared to more recent methods like [5] and our approach with 7.18 average FPR improvements.\n\nWe acknowledge the important role these baselines have played in advancing the field, and we believe our work builds upon and extends their foundational contributions in the context of open-set, single-image TTA.\n\n---\n\n### **\\[Weakness 3] Does Corruption Dataset Influence Experimental Results?**\n\nThank you for your valuable suggestion. We carefully addressed this concern in our experimental design. In **Table 3 (Line 308)**, we present experiments with **uncorrupted** datasets, using **VisDA** as the ID dataset and **MNIST** and **SVHN** as OOD datasets—none of which are corrupted. Additionally, we evaluated settings where both ID and OOD data are corrupted, as shown in **Table 1 (Line 275)** and **Table 7 (Line 750)**, for **CIFAR-10C** and **CIFAR-100C**. For example, in the MapLe-based CIFAR-10C to CIFAR-100C experiment, our method outperforms the second-best method by 1.72 in FPR and improves accuracy by 0.41.\n\nAdditionally, in response to **[Weakness 2]**, we conducted experiments with clean **CIFAR-10** and **CIFAR-100** datasets for ID, as well as **MNIST**, **SVHN**, and **Tiny-ImageNet** for OOD. Compared to the full ImageNet, we used Tiny-ImageNet for more efficient validation. These results further show that performance trends between our method and baselines remain consistent, regardless of corruption. \n\nIn these settings, our method consistently performs well, demonstrating that its effectiveness **does not rely on data corruption**. Furthermore, if corruption introduces domain shifts, all methods would be equally affected, ensuring fairness in the evaluation.\n\nFrom a theoretical perspective, as outlined in **Hypothesis 2**, our method models the distance between ID, OOD, and the intermediate domain, driven by semantic-level distributional differences, which corruption does not fundamentally alter. For example, even if a cat image is corrupted, its core semantic representation as a \"cat\" remains intact.\n\n---\n\n### **\\[Question 1] On the Choice of Harmonic Accuracy and Related Metrics**\n\nThank you for your insightful comment. We agree that **harmonic accuracy alone does not explicitly separate ID classification from ID-OOD discrimination performance**, as it reflects only their overall balance.\n\nTo address this, we include two additional metrics—**FPR\\@95** (**Line 641**) and **AUROC** (**Line 627**)—to directly evaluate ID-OOD discrimination. Lower FPR\\@95 and higher AUROC values indicate better separation between ID and OOD samples.\n\nTogether, FPR\\@95 and AUROC capture OOD detection performance, while harmonic accuracy helps reflect ID classification indirectly. This combination provides a more comprehensive assessment of model performance under open-world settings.\n\n---\n\n### **\\[Question 2] Potential Impact of Corruption Dataset on Experimental Results**\n\nAs discussed in **\\[Weakness 3]** and supported by the experiments in **\\[Weakness 2]**, we conclude that our method does not directly benefit from dataset corruption. The observed performance improvements stem from the effective modeling of the distance between ID and OOD distributions, as described in **Hypothesis 1 and Hypothesis 2**.\n\n---\n\n### **\\[Question 3] Final Score Calculation via Equation 3**\n\nThank you for your comment. Indeed, the final ID/OOD score is computed using **Equation 3**. To clarify, **Section 3** is structured as follows: **Section 3.1** focuses on the calculation of the ID/OOD score based on **Hypothesis 1**, while **Section 3.2** introduces the concept of the intermediate domain and the test-time adaptation method, which is developed around **Hypothesis 2**.\n\n---\n\n### **\\[Question 4] Additional Experiments (Tent \\[2], WATT \\[3], OSTTA \\[1])**\n\nThank you for your suggestion regarding the experiments. As per your recommendation, we have included the requested experiments with **Tent \\[2]**, **WATT \\[3]**, and **OSTTA \\[1]** in **\\[Weakness 2]**. All experiments were conducted using **uncorrupted datasets**.\n\n---\n\n### **Reference**\n\n[1] Lee, Jungsoo, et al. \"Towards open-set test-time adaptation utilizing the wisdom of crowds in entropy minimization.\" ICCV . 2023.\n\n[2] Wang, Dequan, et al. \"Tent: Fully test-time adaptation by entropy minimization.\" arXiv preprint arXiv:2006.10726 (2020).\n\n[3] Osowiechi, David, et al. \"WATT: Weight average test time adaptation of CLIP.\" Advances in neural information processing systems 37 (2024): 48015-48044.\n\n[4] Shu, Manli, et al. \"Test-time prompt tuning for zero-shot generalization in vision-language models.\" NeurIPS 35 (2022): 14274-14289.\n\n[5] Sreenivas, Manogna, and Soma Biswas. \"Efficient Open Set Single Image Test Time Adaptation of Vision Language Models.\" arXiv preprint arXiv:2406.00481 (2024)."}]

Author rebuttal:

Meta-review: Authors introduce in this work a challenging and practical scenario for Test-Time adaptation of pre-trained vision language models, which deals with an open-set single-image problem. The paper initially received mixed scores, with reviewers highlighting the problem as under explored, and acknowledging the novelty and soundness of the proposed approach, whose practical benefits (efficiency and versatility) are supported by comprehensive experiment results. Reviewers also raised important concerns, including unclear technical presentation, insufficient explanation of key design choices, lack of comparison to relevant test-time adaptation methods, or the inclusion of more complex datasets, among others. After carefully reading the discussions, authors have successfully addressed the majority of these concerns, and reviewers are satisfied with the authors responses. 

Considering all these points, I recommend the acceptance of this work. Having said this, I strongly encourage the authors to consider one remaining concern from one reviewer, i.e., revisiting the claim of single-image operation. The proposed approach relies on feature and score banks that accumulate information over multiple samples, and the single-image operation claim might sound a bit overstated. Similarly, despite these claims, the supplementary ablation study on the batch size does not explore values B<64, which does not support the single-image claim.
Decision: Accept (poster)