Long-tailed Test-Time Adaptation for Vision-Language Models
Authors: Xucong Wang
Zhe Zhao
Zekun Wang
Xiaofeng Cao
Xu Wang
Di Wu
Pengkun Wang
Yang Wang
Venue: ICLR 2026 Poster (2026)
Keywords: Test-Time Adaptation; Vision-Language models; CLIP; Long-tailed Learning
Primary area: transfer learning, meta learning, and lifelong learning
TL;DR: Long-tailed Test-Time Adaptation for VLMs

Abstract: Test-Time Adaptation (TTA) aims to further adapt models to unlabeled test sets arriving in a sequential datastream, thereby progressively strengthening the model's generalization ability. While existing TTA methods for Vision-Language Models (VLMs) are primarily designed and evaluated on (nearly) balanced dataset configurations, real-world test sets may exhibit a long-tailed distribution where major classes dominate the decision boundaries of minor classes, presenting unique challenges. As the first attempt to solve this problem, this paper proposes Long-tailed Test-Time Adaptation (dubbed as L-TTA), which consists of three co-designed mechanisms: Synergistic Prototypes (SyPs), Rebalancing Shortcuts (RSs), and Balanced Entropy Minimization (BEM). SyPs introduce two fine-grained prototypes to enrich tail classes with extra inter-class knowledge; RSs employ learnable shortcuts to achieve learnable adaptation, regularized by class re-allocation loss to enforce distinct feature clustering; BEM restrains excessive entropy minimization of confident classes with extra penalty term, with theoretical propositions to justify its rebalancing capabilities. Extensive experiments over 15 datasets under various long-tailed settings highlight the superior performance of L-TTA in both accuracy and class balancing.

Published as a conference paper at ICLR 2026
LONG-TAILED TEST-TIME ADAPTATION FOR VISION- LANGUAGE MODELS
Xucong Wang', Zhe Zhao 1., Zekun Wang' Xiaofeng Cao Xu Wang! Di Wu',
Pengkun Wang 1.2 & Yang Wang 1.2.
University of Science and Technology of China (USTC)
2 Suzhou Institute for Advanced Research, USTC 3 City University of Hong Kong
4 School of Computer Science and Technology, Tongji University
(uuco, 224543, wangzekun, wdexy) @mail wustt.teee. edu. en, x aofengcaa@Boongi edu. en, {wx309, pengkun, angyan (ngyanauustc Bustc. custcc.ddu.c.
ABSTRACT
Test-Time Adaptation (TTA) aims to further adapt models to unlabeled test sets ar- riving sequential datastream, thereby progressively strengthening the model's s generalization ability. While existing TTA methods for Vision-Language Models (VLMs) are primarily designed and evaluated on (nearly) balanced dataset con- figurations, real-world test sets may exhibit a long-tailed distribution where major classes dominate the decision boundaries of minor classes, presenting unique chal- lenges. As the first attempt to solve this problem, this paper proposes Long-tailed Test-Time Adaptation (dubbed as L-TTA), which consists of three co-designed mechanisms: Synergistic Prototypes (SyPs), Rebalancing Shortcuts (RSs), and Balanced Entropy Minimization (BEM). SyPs introduce two fine-grained proto- types to enrich tail classes with extra inter-class knowledge; RSs employ learnable shortcuts to achieve learnable adaptation. regularized by class re-allocation loss to enforce distinct feature clustering, BEM restrains excessive entropy minimization of confident classes with extra penalty term, with theoretical propositions to justify its rebalancing capabilities. Extensive experiments over 15 datasets under various long-tailes thig-tailed settings highlight the superior performance of L-TTA in both accuracy and class balancing. Code: https: / //ggttttuub// com xwc
INTRODUCTION
Pretrained Vision-Language Models (VLMs) like CLIP (Radford et al., 2021) and ALIGN (Jia et al., 2021), show remarkable zero-shot and generalization ability, giving credit to their strong joint- modality modeling capabilities (Li et al., 2022d; Gandelsman et al., 2023; 2024) that learn from web-scaled image-text datasets. Aware of this, various studies propose Parameter-Efficiee Fine- Tuning (PEFT) (Jia al., 2022: Bahng et al., 2022; Zhao et al., 2023b: Han et al., 2024; Tian al., 2024: Sheng et al., 2025) over VLMs to efficiently transfer them to specific downstream tasks; as two leading approaches in PEFT, adapter tuning (Zhang al., 2022: Gao et al., 2024) and prompt learning (Zhou et al., 2022b; Wang et al., 2022: Lu et al., 2022: Khattak et al., 2023; Wang et al., 2023a; Yao al., 2024) freeze the model and only finetunes several learnable modules plugged in layers, enjoying higher efficiency and wider flexibility than the full finetuning.
However, the dramatic distribution gap (Lu al., 2022) between the labeled training sets and the unlabeled test sets always hinders the generalization of VLMs over the latter ones. To circum- vent this issue, recent studies introduce novel scheme named Test-Time Adaptation (TTA) (Wang et al., 2020; Niu et al., 2022; Zhang et al., 2024c; Shu et al.. 2022; Zhang et al., 2025c:a) for VLMM, which enables the model to adjust itself to test sets following an unsupervised "Entropy- Minimization (EM)" manner (Wang et al., 2020; Shu et al., 2022; Feng et al., 2023; Yoon et al., 2024; Zhang et al., 2024c;b; Gao et al., 2024: Zanella Ben Ayed, 2024) during inference. For ex- ample. TPT (Shu al., 2022) and Di (Feng et al., 2023) select the most confident augmented views and minimize the entropy of prediction probabilities over these views; Following methods
"Corresponding Author.

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Published as a conference paper at ICLR 2026
(a) New Task
(b.1) Fallure Model: Text-induced Tall Erosion
(b.2) Failure Mode2: Modality-bian Diia Amp.
Goldfish
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[c.1] Our Design1: Synergistic Protofypes
(c.2) Our Design2: Rebalancing Shortcuts
Text protobypes (8 (Biased)
Synergistic Prototypes
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Ixage Hocaay
Algement
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f |Complamentaryl
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Figure I: (a): The TTA task under long-tailed settings with VLMs. (b): Specific failure modes for LI-TTA with VLMs. (c): Two targeted designs: Synergistic Prototypes and Rebalancing Shortcuts.
(a)
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Figure 2: Results:
Existing SOTAs show severe degradation in tail-class representations (Left) and vulnerability to imbalance ratios (Right), while our L-TTA shows considerable robustness.
introduce extra knowledge regularization
prediction probabilities (i.e. C-TPT (Yoon et al., 2024), O-TPT (Sharifdeen et al., 2025)), among supportive views (i.e, SwapPrompt (Ma et al., 2023), PromptAlign (Abdul Samadh al., 2023)) historical features (i.e, DMN-ZS (Zhang al., 2024d), HisCTPT (Zhang et al., 2024c), TDA (Karmanov et al., 2024), DPE (Zhang et al., 2024a), BPRE (Qiao et al., 2025)). Another focus is to dynamically schedule different prompts perceive varied semantics, thus making them specialized and avoiding the еттоr accumulation over a single prompt (Zhang et al., 2024b; Xiao al., 2025; Wang et al., 2025).
Despite their superior performance on (nearly) balanced datasets, these methods suffer from signifi- cant degradation on Long-Taile -ailed (LTT) test sets, as evidenced in Figure 2. A primary and fundamental reason is that 77A is one-epoch process where training and testing are intertwined, causing later predictions to be influenced by all preceding steps (except for methods that perform TTA individ- ually for each sample, like TPT (Shu et al., 2022)). This characteristic also precludes the use of traditional strategies like up-sampling or compute-intensive regularization. We further identify two specific failure modes unique to the VLM-based LT-TTA setting: Text-induced Tail Ero- sion: The textual modality exacerbates long-tailed challenges because text embeddings themselves carry biases from pre- mr--traning. As demonstrated in Figure 1 (b. 1), certain classes consistently yield higher accuracy than others, regardless of their status as head or tail classes. We refer to these as rich classes. When rich classes coincide with head classes, tail erosion is further intensified. e Modality-bias Amplification: Applying unimodal LT-TTA methods (Niu et al., 2023; Zhao et al., 2023a) VLMs progressively amplifies the inherent mismatch between visual and textual represen- tations. Figure 1(b.2) shows that adapting the unimodal SAR method on VLM backbone results in significant performance drop and instability compared its use on pure visual backbone. This underscores the critical need for bi-modal adaptation that refine the multi-modal manifold of VLMs.
To cireumvent these problems, we propose Long-tailed Test-Time Adaptation (L-TTAA) First, L- TTA equips two prototypes to accumulate multi-modal semantics beyond text embeddings. mean- Mitigating
while apply a more granular update strategy to augment tail class representations. Asp. I, II); Then, to dynamically balance the accumulated knowledge of head/tail classes, L-TTA introduces learnable Rebalancing Shortcuts (RSs) that are directly applied to the prototypes, op- timized with class re-allocation loss to boost the discernable feature clustering. ( Mitigating Asp. II) Furthermore, we propose Balanced EM (BEM) to counteract the head-class bias inherent in EM, providing tailored optimization objective for LT-TTA. BEM weighs the class priors with
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the prediction confidence, thereby favoring finer adaptation of uncertain and tail classes; Also, introduce two propositions to guarantee its theoretical capabilities. Extensive experiments demon- strate that L-TTA outperforms existing methods in various benchmarks under long-tailed settings (see T-SNE and macro-F1 comparison reported in Figure 2), showing remarkable robustness to the noise settings along with high computational efficiency. Our contribution be summarized as:
We first study the Test-Time Adaptation (TTA) under long-tailed scenarios, and highlight the drawbacks of existing approaches under such circumstances.
We introduce L-TTA, the first TTA for long-tailed settings. L-TTA introduces synergistic Prototypes (SyPs), learnable Rebalancing Shorteuts (RSs), and Balanced Entropy Mini- mization (BEM) to deal with degraded tail distributions and decision boundaries.
Extensive experiments show that L-TTA surpasses existing methods in both accuracy and class balancing capabilities. Ablation studies show that L-TTA is efficient and robust.
2 RELATED WORKS
2.1 VLMS TEST-TIME ADAPTATION FOR VLMS
We have observed an exponential advancement of Vision-Language Models (VLMs) (Radford et al., 2021; Wang et al., 2023b; Hurst et al., 2024), which learn and modulate diversified information as human do. As one of the earliest VLMs (Radford al., 2021; Jia al., 2021; Li al., 2022a; Liu et al., 2023; Hurst et al., 2024). CLIP introduces image-text contrastive pretraining to learn the modality-shared space with the web-scaled unlabeled data, which endows with strong zero-shot generalization ability and wide compatibility (Wang et al., 2023b; Rao et al., 2025).
Building upon this, emerging studies propose Parameter- Efficceen FineTTuning (PEFT) mechanisms to efficiently adapt CLIP (Zhang et al., 2022; 2023; Liu et al., 2024). However, they mainly adopt the labeled sets for finetuning and struggle to generalize to domain-shifie
test-sets. To deal with this, Test-time Adaptation (TTA) refines VLMs on test-data with an unsupervised Entropy Mini- mization (EM) scheme; For example, TPT selects augmented views yielding highest confidence and applies EM to optimize the text prompts; Following advancements concentrate on 1) calibrating the uncertainty in predictions (Yoon et al., 2024; Sharifdeen et al., 2025); 2) applying extra regular- izations between views (Ma et al., 2023; Abdul Samadh al., 2023); 3) caching historical predic- tions (Zhang et al., 2024d; Karmanov et al., 2024; Qiao et al., 2025); 4) Exploring visual adaptations or reinforcement learning (Osowiechi et al., 2024; Hakim al., 2025; Zhao et al., 2023b).
Notably, an emerging line of TTA also focuses on non-i.i.d. test-data (Boudiaf et al., 2022; Gong al., 2022; Niu al., 2023; Wang et al., 2024; Zhao al., 2023a). For example, LAME (Boudiaf et al., 2022) adjusts model outputs via Laplacian regularized maximum-likelibood estimation to avoid catastrophic degradation. DA-TTA (Wang et al., 2024) aligns source and test distributions using a dedicated loss and incorporates domain shift detection for continual adaptation. SAR (Niu et al., 2023) a sharpness- aware and reliable EM that leverages group/layer normalization to stabi- lize TTA. DELTA (Zhao et al., 2023a) uses aatch-rnormmalizttion and online re- e--eiightigg to reduce class bias. Our L-TTA differs from these methods with its unique focus on Long-tailed TTA of VLMs, where the cross-modality misalignment and text-prior bias pose unique challenges.
2.2 LONG-TAILED LEARNING
Long-Tailes (LT) Learning Li et al. (2024b); Zhang et al. (2025b); Jin et al. (2023); Li et al. (2024a; 2022b) built upon the reality that real-world datasets always exhibit a pervasive long-tailed distri- bution, in which learning head classes would result in degraded decisions of tail classes. Classical LT methods include Augmentation (Chawla al., 2002; Kuo et al., 2020), resampling (Wallace al., 2011; Chawla et al., 2002), reweighting (Menon et al., 2020; Cui et al., 2019; Cao et al., 2019; Ren et al., 2020), scaling (Li et al., 2023), pre-defining unbiased targets (Li et al., 2022c) or contrastive learning (Zhu et al., 2022: Du et al., 2024). Recently, several studies focus LT problem over VLMs, for example, LTGC (Zhao et al., 2024) proposes a generative framework leveraging recursive reasoning and filtering operations to enrich tail classes; LPT (Dong et al., 2022) applies the Visual Prompt Tuning (VPT) to long-tailed tailed recognition by dynamically enriching semantic groups between tail classes. Candle (Shi et al., 2024) introduces virtual prototypes and refined logit-adjustment loss
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Synergistic Prototypes
Update
Final Predictions
DPc
1000000 100000
000000
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Iala Logits
ugmeentdd Images
Attention
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Allention
InIl Minus
Iall
A photo of (cless) bed photo (class) photo the (cless)
Patropy Minimiizatio
Text Emb.
Templates
Final Logits
Figure 3: The overview of L-TTA. For image, obtain its augmented views and primary log- its. In Synergistic Prototypes (SyPs), we update DPs and EPs with confident and uncertain visual embeddings evaluated by these primary logits, then synthetically combine them to enrich tail repre- sentations. Rebalancing Shortcuts, the SyPs are combined with learnable shortcuts and adapted with class re-allocation loss. The final logits are optimized with Balanced Entropy Minimization.
solve the long-tailed few-shot adaptation of VLMs. In contrast, we first concentrate on the LT problem in test-time adaptation of VLMs.
3
METHODOLOGY
3.1 PRELIMINARIES
We begin by presenting the general settings of TTA VLMs: given pretrained CLIP F c'mmpriiinn the visual encoder V {V1. V...... Vz and text encoder T = (Ti. T2. .Tz). we aim to further adjust the prompt (could be hand-crafted like "a photo of [CLASS]." or pre-optimized (Zhou al., 2022a;b)) m E D to fit the test data. Here D. is the text hidden dimension. Concretely, we're firstly given the class texts all samples, denoted as y. We denote C as classes, C, as a class, |C, as its class cardinality (size), C' as number of classes. For an image embeddings with V, and textual embeddings of all classes with T, where each layer E,1E, is:
Nest, generate its visual
[ct, dt} = Ev (1- ig = (a)); (e,, k,, [.] k co E.([ y))
(1)
Where &, ky are intermediate visual and textual features. Ct. are cls/eot-token; After obtaining CL and e, from the last layer, we map them into joint space with separate projections P. and P::
f =
Po(e)); t= P(eL);
(2)
Here f E IR, t E RCDD, D is the dimension of the joint space. We generate output logits by calculating the cosine similarity, i.e, cos(f. t). The final predictions are:
exp(ze/T)
Plye x) Ee exp(ze /T)* ÿ = aargma.,FPy.l
(3)
Here is the temperature to control the density of predictions, cos is the cosine similarity. In the following, we also denote as the softmax function, f(x) as the visual embeddings of x.
3.2 L-TTA: LONG-TAILED TEST-TIME ADAPTATION
We then introduce the framework of L-TTA (shown in Figure 3), consisting of Synergistic Proto- types, Rebalancing Shortcuts, and Balanced Entropy Minimization.
Synergistic Prototypes. Leveraging prototypes to maintain historical knowledge has emerged as a trend in recent TTA methods. However, this scheme faces the following tricky problem under long-tailed scenarios: Tail-class prototypes are more likely to be uninitialized at the beginning phase, while persistently storing inadequate semantics as the datastream progresses. To solve this problem, we introduce our first innovation named Synergistic Prototypes (SyPs), which comprises Determin- istic Prototypes (DPs) and Exclusionary Prototypes (EPs). DPs maintain the similar functionality as well-studied prototypes (Karmanov et al., 2024: Zhang et al., 2024a) to register maas--ettrmiiistie features: In contrast, EPs store the most improbable features of each class generated from all possi- ble samples, meaning that EP of each class can be always updated along the datastream, thereby alleviating the above problem. We elaborate our innovation in the following.
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Concretely, for image at the step s, randomly crop Q views E { their visual embeddings f()) and predictions P(yl@) of(()tt) Recall t is the pre-stored tex- tual embeddings. We gradually update our Deterministic Prototypes (DPs) {v=)a= of the maximal class c² in (P(y) @), i.e, ve,, with embeddings yielding lower prediction entropy than the threshold 0 via an Exponential Moving Average (EMA) manner:
i= from it, generating
(NV 1) + ic- Uc [[(NOP 1) + 0.- d. - (VVIET((E))) s.l. T= - ((HI(P(()))) < 0}
(4)
where (H(() calculates the entropy, ||. calculates the Euclidean norm. NDP is the update counter for class с in DPs until step and increases by I at each step. If T * Ø, we update 0 with the minimal entropy in T following the above EMA manner. Although not all views are eligible to represent classes and some of them are even ambiguous
subject to multiple classes, their prediction distributions could reveal fine semantic correlations between classes; By dynamically maintaining the visual embeddings (ā) for all the classes into extra prototypes, we could indirectly exclude the features least likely to occur in every class accordingly. This idea leads to our design of Exclusionary Prototypes (EPs) u {uo) Specifically, for logit (z.) of each view Tt, we update EPs of all classes based on P( (2,) as the following:
(NEP Dc) + the Uhe <- I[(NEE Φc) + uill" Un = f (i:), d. max P(ye (T.) - P(yolTt)
VCEC (5)
max, (P(ce) (2:))
Recall C' the number of classes, NER is the update counter for class in EPs until step s and increases by &. at each step. Notably, our EPs largely differ from TDA (Karmanov et al., 2024)'s "negative cache", which selects visual features whose prediction entropy is within specific thresholds and combine them into the negative cache of the predicted class. EP employs the prediction of every view guide EP updates of all classes, consequently capturing more refined inter-class associations and enriching tail class representations. Furthermore, this mechanism also grants EPs considerable robustness against OOD semantics, by endowing them with less &e in EMA updating.
Rebalancing Shorteuts. Besides statically storing historical knowledge, learnable adaptation enables the model to dynamically adjust the class balance and rectify its predictions. Considering that prompts in existing TTA methods bring extra gradient flow across the text encoder, we keep the prompts frozen (Zhang et al., 2024a), then introduce learnable Rebalancing Shortcuts (RSs) over our SyPs to achieve dynamic adaptation. Furthermore, considering the peculiarity of our long-tailed TTA settings, we design RSs as the impetus to boost the active feature clustering and interactions of different classes in our SyPs. Formally, RS is implemented by a cross-attention with shared hyper- class vectors; assume there are K hyper-class vectors q {q; |- queries, and merge the RSs with the following formula:
we treat DPs v, EPs u as
Vg= < Attn([ve, tel, 9;)q; + Uoi ue + Attn (u,, A(t,,
tel. 9,)q; + uoi
(6)
Where Attn calculates the attention score. We aim to ensure that these hyper-class vectors achieve ideal clustering atop the prototypes and promote knowledge transfer between head and tail classes. Gaining inspiration from the Load Balancing Loss (LBL) of mixture-of-expprts in LLMs (Qiu et al., expert, then incorporate Class Re-Allocation
2025), we propose to treat each hyper-class (CRA) loss to delegate each expert with evenly distributed class semantics (notably, before Eq. 6):
CERA (avg avg. avg.Attn(wo.. te]. qj) + avv. (co, avg, tc], q;)) (7)
(Atman,,AAtt((// te], (;:)) is the pseudo label generated by binarizing attenticon scores in each row; 1(x) equals when x is true, else 0. Here, the average of attention scores over all class prototypes for expert j. i.c, avg,Attn(. 9;) is treated as the expert activations; avg,(ce.())) stands for the counts of expert j being the top-1 entry in Attn(: 4;)- By minimizing
Where (w) = 1(j
the dot product of above two terms for u and v, we encourage all prototypes to distribute their attention weights uniformly among hyper-class vectors, resulting in discernable feature clustering and reducing dominance of head-class prototypes. The synergy between SyPs and RSs operates as follows: RSs rely on the comprehensive and stable prototypes from SyPs to compute reallocations, SyPs leverage RSs to go beyond static caching and enable proactive refinement.
The next part will summarize how summarizes how SyPs and RSs enhance predictions; additionally, we propose Balanced Entropy Minimization (BEM), variant of EM that will be jointly employed with CRA to optimize shorteuts.
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Final Predictions & Balanced Entropy Minimization. We first introduce the final prediction of L-TTA at each step for both training and inference, which comprchensively utilizes the modified synergistic prototypes for enhanced and robust predictions:
PITTa(yela)) = (f(x)t A(f(z)v)) - A(((z)u.)
(8)
Where A(x) = X1 hyper-parameterd We then introduce our Balanced Entropy Minimization (BEM) to optimize this prediction each step. Entropy Minimization (HM) (Wang et al., 2020; Shu et al., 2022) is pervasive in TTA methods; In contrast, in the context of long-tailed T'TA tasks, EM would specifically degrade the decision boundary of tail classes. We introduce Proposition to formalize this claim:
x)) is affinity function for scaling (Gao et al.. 2024). X1
Proposition! 1 For the long-tailed TTA tasks, denote z = {21, 22.., zc} } as the output logits, in which we assume [C,l |C2l >..> |Col. |Cyl is the class cardinality of i. We split C. into Cread and Cran1 with certain measurements. The following holds true: Ei-Cim V Hl < 0 < EirCrun V HL.
We defer the proof to Appx. A. This proposition indicates that predictions of head-class become more confident than those of tail-classes after EM optimization, as they are most prone to constitute the maximal term. Unfortunately, solutions this problem are non-trivial, owing the distinct characteristics of EM relative to classic Cross Entropy loss: EM is unsupervised and only amplifies the model's 's intrinsic bias; Moreover, its gradient with respect to the logits does not exhibit clear linear relationship with the logit distribution; i.e, if the distribution becomes sharper, the positive optimization obtained by the confident class may also increase. This implies that when combining class priors into the logits for rebalancing like what logit adjustment (Menon et al., 2020) or bal- anced softmax (Ren et al., 2020) did, we may further exacerbate the model's bias toward the head classes and damage the decision boundaries. Therefore, unique variant of EM specifically to long- tailed settings is of necessity. This paper then introduces Balanced Entropy Minimization (BEM), designed with intuitive rationale: since adjusting high-confidence classes may further amplify the bias, we can focus on calibrating uncertain classes, actively guiding their optimization with the class priors. In implementation, BEM incorporates an additional penalty term into the EM loss:
LEE (PP)) -o(')) log(a(z")), === B33 log
Σ, T
where B is for controlling the penalty degree, π E RC is the class prior and set to the cardinality of all classes (notably, the class prior is continually updated based on the current predicted pseudo- labels) []
default. Our key innovation here is add an extra penalty term (1 P) to the log-amplified prior, which drastically reduces the contribution of confident classes and favors classes that are both rare and uncertain. Consequently, our BEM maintains the learning of head samples, while achieving finer adaptation of tail samples in capturing more discernable semantics, thereby mitigating the deviation of the decision boundary. Further comparisons of BEM and clas- sic LT methods are shown in Appx. G. In summary, we introduce the following Proposition 2 to demonstrate the theoretical capability of our proposed BEM:
Proposition 2 For the long-tailed TTA tasks, denote which we assume |Cal > C21... > Cc |C,l is the class cardinality of i. We split C into Cread and Crail with certain measurements. The following holds true (Recall H!' is from Eq. 9):
{21, z2.., ZC} as the output logits, in
Ei-ean 2 EisCon Vz, HI|
Vz, H' -E 1
(10)
The proof is in Appx. A. This proposition reveals that applying BEM shortens the optimization gap between head and tail classes. Thus, our BEM is both intuitively and theoretically interpretable. The final objective of L-TTA consists LaEn and CORA, connected by a syper-parameter ry:
LITTA LEEx (PuTTA) + C/CA
(11)
EXPERIMENTS
Datasets. We adopt four benchmarks but manipulate them to follow the tailet settings: 1 OOD Benchmark (OODB), which assesses the robustness of models towards unseen data by con-
ducting TTA over four OOD variants of ImageNet (Deng et al., 2009): ImageNet-A (Hendrycks

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Published as a conference paper at ICLR 2026
Table 1: Results on Long-tailed OOD Benchmark. We conduct 5 runs for each experiment with imb varying from 10 to 50. The accuracy of head/ / tail classes are in Appx. C. The "OOD Average" is the average results of four OOD datasets excluding ImageNet. The best results are in Bold.
Methods imb 10
Auc.
ImagoNet-A
Mac.
Arc.
ImageNNet-R
Mac.
Alc
TenigeNet-5
Mac.
Azc.
Imag-Nes-V2
Mac.
Acc.
ImugeNer
Mac.
Acc.
DOD Average
Mac.
CLP (C)
46.34
45.1
72.27
70.41
+2.17
201.7
59.6.
51.02
61.75
S0.14
50.04
50.34
TPT (NexriPS
52.79
45.08
77:33
74.28
45.22
43.60 1.60
61.34
55,70
65.53
60.36
59.11
55.42
CTTT (CL5/24
50.70
46.36
72.92
44.14
42.62
64.92
60.13
MIA (4)PW/24)
57.15
51.98
77.04
74.38
48.59
46.50
63.61
62.69
67.79
62.57
61.60
59,01
(CNPR-34)
60.00
54. 30
47.55
45.77
60.10
63.89
63.75
ZERO (Neu11P5
60.11
52
(NeudE5
60.31
54.41
90.58
69.32
45.45
67.78
63,32
70.16
64.19
64,50
WATT (Nomt 24]
501.0
76.96
66.75
60.36
70.04
64.48
63.91
59.30
CLIPATT (RACCV25]
58.58
78.22
73.50
46-42
66
68.17
62.01
62.13
0-TPT (UNVRR225]
50.00
45
75
72.61
64.06
79
62.53
54.92
65.28
60.76
58.02
54.23
SCAP|CVPR (CVP9/25)
00.54
52.26
801.7
75.48
48.97
45.38
67.41
56.55
70064
64,80
64.37
57,42
L-TTA (flan)
61.78
55.97
W2366
78.56
50L25
25:99
68399
64.19
71.30
65.30
65.97
61.18
Methods| inb - 20
Auc.
ImageNat
Mac
Aue.
ImageNNet R
Mac:
Ade
ImaigeNNe
Micc
A.e.
ImmggaNer V22
Mas.
Auc
ImsageNet
Mac.
Auc.
OOD Average
Mus.
CLIP (ICML/21
6.
12.47
42:25
40.45
58.36
50.60
63.36
57.33
55.00
50.51
TPT [NeuiPS
53.30
47.11
71:36
72.82
46.01
43.79
60.78
65.22
60.70
59.47
54.63
C-TPT
50.65
46.01
23.05
61.11
60.25
(CN999224)
ZERO (NeudPS 24]
53.60
77.02
43.32
65.26
59.96
59.92
RLCF [XCL)C224
36.62
50.33
76.51
72.22
45.57
42.95
64.76
56.63
69.74
62.16
61.37
55.56
DPE (NcudDS 24]
60105
91.38
75.87
50.30
67.41
63-53
64.21
58.82
WATT (Nout199344]
60.08
52.55 58
80.38
75.14
45.15
60
60.02
69:39 29
61-83
61.56
98.09
CLIPASTT (WACCV22)
57.57
$2.89
79.94
72.25
45.36
42.75
65.20
55,19
65:01
61.74
62.09
56.55
OTTPI (CTVRR225]
50.22
$4.92
75.00
7087
42.84
42.93
61.83
53,47
63.88
59.90
$7.75
$3.05
(CVP9/23)
60.36
52. 211
79.52
75.44
48.45
43.12
65.84
55.97
66:85
02.97
63.54
56.68
L-TTA (Ouns)
61.23
$4.79
N2.31
76.48
50.44
46,24
07.29
64.55
70.35
64.10
64,92
60,52
Methods Inb 50
ImageNet-A
Mar.
Acc.
Image:Ner-R
Triegs Ner-S
Mac.
ImageNe-VV
Acc.
ImsggeNe
Mac.
O015 Average
Mac.
CLI ([CSL)2))
44.74
36.26
71:52
62.60
36.00
55.05
50.38
53.70
53.41
46.38
40
12.00
56.57
50.64
41.01
44.01
12
55.00
MTA (4)
57.15
75.04
66.29
61.44
60.60
TDA (CVPR
58.97
70004
44.87
444
64.76
58.50
6RU7
62.06
62.37
54.79
ZERO INcudPS 24]
52.24
45.04
75:27
55.08
63.55
52.05
58.00
46
76.54
68.08
45
38. 5R
51.75
66.33
56.57
99.85
$1.87
DPE (NeudP5'24) 24]
60.21
47.46
80.76
69.96
48.07
43.50
65.80
60.76
68.06
62.37
63.71
55,43
WATT (NouteS'20 24]
57.36
89:33
78.05
70.24
45.5 45
40. 49
85.37
55.63
68.69
57.72
51.77
54.05
CLIPASTT (RACC125)
55.36
48.99
78.31
69100
44.12
20.16
64.56
54.50
66.70
57.05
50.59
53.14
0-TPT (2VPR77))
51.97
39.89
75.34
64.84
41
40.67
51.70 70
53.61
61 18
55,69
57.54
40.75
SCAP (CVPR/25)
59.05
47.00
78.08
73.11
45 55.86
41.03
65.31
54.19
66.30
58.85
62.08
53.83
L.TTA (Dams)
60.07
54.79
XE.01
7533
49.73
46001
67.10
02.45
69.74
63.41
64.68
39.78
et al., 2021b), ImageNet-V2 (Recht et al., 2019), ImageNet-R (Hendrycks et al., 2021a) and Benchmark (CDB), which evaluates the model's performance on ImageNet and 10 fine-grained datasets: Pets (Parkhi al., 2012). SUN397 (Xiao et al., 2010), ET (Helber et al., 2019), Caltech101 (Fei-Fei et al., 2004), Cars (Krause et al., 2013), DTD (Cimpoi et al., 2014), UCF (Soomro et al., 2012), Flower102 (Nilsback & Zisserman, 2008), Food101 (Bossard et al., 2014), Aircraft (Maji et al., 2013). 2 Corruption Benchmark (CB), which adds gaussian noise to images (with the variations/ / ranging from 0. to 0.4) to mimic harsher scenarios. Experiments on other 16 corruption types (Hendrycks & Dietterich, 2019) with a severity of 5 can be found in Appendix J. To enable these datasets to show obvious taiggd distribution (some of them may have already been slightly imbalanced), we conduct random sampling to ma- nipulate the cardinality distribution into an exponentially decayed curve yielding specific imbalance ratio. Notably, if the calculated cardinality is less than the class cardinality itself, we simply keep that class unchanged. We treat the top-20% classes as head and others as tail.
ImageNet-S (Wang et al., 2019). @
Implementation Details. Following previous studies, we adopt a pretrained CLIP for evalua- tion where the image encoder can be either ResNet-50 VIT-B/16 (ViT-B/16 in default, the results on ResNet-50 are in Appx. D). Following ablation studies on ImageNet, we generate 15 augmented views for a image via random resized cropping. 1) 1, A 6, A2 = 6, K 0.3, 3 1. We select Adam W as the optimizer with a weight decay of 1 and eps of 1e-3. The imbalance ratio, defined as imb = max; /Cl/minn// [C,), is selected from {10, 21, 50). All experiments are conducted on a sin- gle A100 GPU, and methods for comparison are reproduced with their provided hyperparameters. Besides accuracy (Acc.), we also report the macro-FI (Mac.) to highlight each model's capabil- ity in balancing different classes. We compare with classic methods like TPT (Shu et al., 2022), C-TPT (Yoon et al.. 2024), O-TPT (Sharifdeen et al., 2025); Training free methods MTA (Zanella & Ben Ayed, 2024), TDA (Karmanov et al., 2024). ZERO (Farina et al., 2024), disual-adaptation methods: WATT (Osowiechi et al., 2024), CLIPArTT (Hakim et al., 2025) and other training-based methods like RLCF (Zhao et al., 2023b), DPE (Zhang al., 2024a), SCAP (Zhang al., 2025a). Details of baselines and prompts are in Appx. B/ / E.
7

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Published as a conference paper at ICLR 2026
Table 2: Averaged Results on Long-tailed Cross-Domain Benchmark with an imbalance ratio of {10, 20, 50}. Please refer to Appx. C for detailed results and head / tail accuracy, We conduct 5 runs for each experiment and average the results. The best results are in Bold.
Methods
Acc.
Caltech
Mac.
Acc.
Pets
Mac.
Ace.
Cars
Mac.
Acc.
Flowers
Mac.
Ace.
Food101
Mic.
Acc.
ImageNet
Mac.
CLIP (UMB/22)
91.10
60.55
85.51
83.29
61.83
b1015
65.h7
00.01
82.31
78.24
63.45
56.66
IPT (NcudPS 22]
93.82
90.99
83.51
83.10
633
61..41
06.94
04.97
83.49
79.85
65.35
59.21
CTPT (CC
93.41
90.69
85.41
85.00
63.25
61:57
67.66
62.08
82.41
78.38
64.98
$8.47
MTA (CNPR/24]
93.48
90.97
86.36
$4.33
63.78
62.41
65.32
60.24
83.61
80.17
66.87
61.87
TDA (CMPR:24]
94.33
68.15
59.60
M0.68
68.70
63.10
ZERO [Ncur1PS244
92.50
39.30
$3.30
59.96
60.55
78.49
64.90
57.38
66.45
60.88
(NeutPSS3 241
73.95
63.36
WATT [NoudES
93.33
86.02
34.91
06.32
64387
M0.9
77.31
69.37
62.01
CI IPAPPTT (WACV
01.15
88.07
84.30
95
90
9R
60.66
80.75
76.98
67.63
60.27
OTTPT (CVPR
93.41
90.61
85.25
85.01
62.68
61.16
67.83
62.18
82,39
78.35
64.18
58.78
SCAP (CVP9225)
91. .0
06
86.15
77.26
64-30
62.
681 10
62.43
82.40
811.2
67.92
02:21
L-TTA (Oin)
95.36
92.29
91.07
86.41
70.10
64.13
74.28
67.68
85.55
80.94
70.46
64.39
Methods
Airezaff
SUNN99
17113
FansSAY
UX
+
Acc.
Mac:
Mac
Max
Mac.
14.39
16.79
42:30
35.10
59:18
TPT
14.73
CTFT (CCR
17.42
18.76
29.53
62.42
58.37
MTA (CNP9924)
17.81
18.60
62.67
TDA (C)PK
23.04
18.97
65.81
58.68
06
34.53
53.48
71.13
61.25
66.60
60.20
ZERO (NerriP8/24
16.51
17.36
39.33
54.63
42.88
36.03
37.94
28.41
65.97
$7.63
61.34
56.64
BLCF [CCLR224]
16.08
17.17
29.13
$2.75
41.90
36.77
40.69
29.03
65.75
53.75
61.12
56.20
DPP (Noud15/244 (4)
24.32
21.38
68.26
61.18
47.55
39.82
55.21
45.85
69.53
61L38
67.25
61.34
WATT [NeudP55224]
17.74
18.27
60.19
$5.05
45.60
37 47.49
45.09
33.36
68.09
59.38
63.15
58.19
CLIPA/TT|WOC (45CV225)
16.22
16.71
58.84
$3.33
42.68
35:95
42.45
31.51
66.31
57.79
61.56
56.84
0-TPT (CNDW
17.27
18:42
63.12
56.22
43.97
37.25
38. 34
30.69
66.99
58.57
62.29
$8.03
SCAP (CVPR:25
22,85
19.01
63.48
42.71
36.37
38.89
62.11
63.90
59.25
I-TTA
25.40
43.50
40.71
56.33
68.77
Table 3: Averaged Results on Long-tailed Corruption Benchmark with E {0.1, 0.2, 0.4) and
imb = 10. Please refer to Appx. for results with other corruption types. We conduct 5 runs for each experiment and average the results. The best results are in Bold.
Methods
Acc.
Callech
Maa.
Ass.
Pets
Mia.
Acc.
Cirs
Mac
Acc.
Flowers
Max.
Asc.
Foodi01
Maa.
Auc.
IategeNes
Mac
CLIP (CMM)/23
77.49
70.94
$4,45
52.91
36.51
33.41
35.35
34.18
33.81
37.75
34.47
TPT (NcurlSS
61.87
39.41
36
44.28
38.03
42.35
39.33
(CCR)22)
50.00
38.07
MIA (CVPR'24)
79.90
73.53
39.51
38.99
27-16
TDA (4]
79.87
73.05
36.27
42.12
38.80
7FRO (Ncarl18)34)
77.24
38.77
35.08
RLCT [XCUR224]
76.95
72.17
36.38
$2.11
38.72
32.51
38.05
35.37
35.16
32,92
39.79
36.30
IPE [NcudP5'244 24]
$1.30
77.16
58.12
$4.91
41.78
38.74
44.90
40.01
35.96
34,42
43.52
40.2
WATT (Noo1P9/244
50:42
76.00 08
55.48
$2.08
43.60
36 04
41. 34
37.56
38.13
18.38
41 16
59.09
CLIPA/IT (ROCC/1
79.06
74.57
36.65
53.84
41.43
35.08
40,83
37.41
36.79
25.
43.45
38.52
O-TPT (CVPR 25]
50.13
73.82
58.53
56.77
57.38
36 79
42.62
36,20
35.72
34,
40.90
39.26
SCAP (CVP9225
79.72
72:75
39,69
55.01
40.1 12
37.44
41.55
35.993
37.82
36.20
42.07
38.52
L-TEA
$2.45
78.35
39.46
46.68
42.72
37.92
43.10
Methods
Acc.
Mae.
Apc.
Auce:
Miac:
Acc.
Anc.
Ace.
Averaae
Mae.
CLIP (UMUU2))
3.15
37.14
15.50
23:20
10:4h
7.01
30.70
34.67
16
IPT INcuiPP 231
9:24
10.64
41.56
38.93
24.91
22.81
7.95
1.35
45.16
39.77
39.63
37.01
5.93
9.48
40.50
37.77
24.54
22: 33
8.35
5.36
42.96
39.55
38.27
35.67
MTA (CNPR224)
10.22
10.21
39.70
33.13
22.00
20.63
14.62
6.38
42.96
37.26
37.55
35.64
TDA (CNPR:24)
12.22
10.48
41.30
37.64
24.75
18.67
13.05
9.05
45.29
37.66
39.88
35,50
ZERO [NcurIPS/24]
10.52
9.07
40.72
36.54
23.32
20.08
11.40
7.18
42.84
35.46
37.64
34.17
RLCF [CCLUSS]
9.34
8.16
41.68
37.63
22.72
18:58
10.58
8.04
44.23
36.14
37.45
33.63
DPC (NeuriP524)
13.35
10.81
43.51
39.91
24.19
23.06
13.58
9.96
46.64
40.26
40.46
37.33
WATT [NeudPS'241
12.40
22.05
36.15
IPAITT (WACV"28]
11.49
15.26
TPT (CVER/25)
7.89
(CV19225)
11.26
11:83
40.02
24.44
22:97
11.55
7.70
39,05
39:27
35.99
L-TTA (Ooi))
15.65
14.57
45.01
41.24
30.94
27.43
17.42
13.94
47.21
42.46
43.31
39.97
4.1 RESULTS& & DISCUSSIONS
Results on the Long-tailed OOD Benchmark. As demonstrated in Table 1, L-TTA mostly out- performs existing methods in both accuracy and macro-FI under three imbalance settings. Going a step further, we find that existing methods consistently show performance degradation when the imbalance effects worsen. For example, focusing on the accuracy/macro-Fi of OOD average when changing imb from 10 to 50, we observe a drop of 1 1.38% for TDA, 0.79% for DPE, 2.29% for SCAP. Interestingly, TPT and C-TPT show even more robustness since they do not involve temporally accumulated knowledge, under the three imbalance settings. In contrast, L-TTA surpasses previous SOTAs by a significant margin (1.47% in accuracy/macro-F for OOD average: 1.67% for ImageNet); also, L-TTA yields a minor variation of 1.29% in macro-Fl when long-tail effects degrade. These results demonstrate the substantial robustness of L--TTA under long-tailed settings.
indicated by their relatively minor variations
8

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Published as a conference paper at ICLR 2026
Table 4: Comparisons of complexity. Here HM is the harmonic mean of Accuracy and Macro-fl.
means the model fails to provide valid outputs. means the model did not finish within time budget.
Methods
TPT
C-TPT
MTA
TDA
ZERO
RLCF
WATT
DPE
CLIPArTT
O-TPT
SCAP
L-TTA
Time (h)
3.80
3.80
1.87
0.91
0.86
8.30
27.70
1.38
6.42
3.81
2.96
1.45h
Memory (G)
17.94
17.94
1.29
0.89
1.68
19.84
1,54xm
1.81
1.71
17.94
1.97
1.89
HM on LT-CDB
61.12
61.06
61.75
64.51
60.42
59.71
62.07
66.31
60.54
61.39
63.31
67. 67.20
HM on LT-CB
40.04
38.57
\
41.04
38.04
41,93
38,05
40.74
46.08
Results on the Long-tailed Cross-Domain Benchmark. As shown in Table 2. further comparisons on 10 fine-grained datasets and ImageNet prove the universality and robustness of L-TTA again. We observe that prototype-based methods like TDA and DPE generally perform more competitive than other methods. Turing to our L-TTA, it outperforms existing SOTAs for 10 of the 11 datasets and shows a significant enhancement in the averaged metrics. We also find the improvement in Macro-F1 (2.20%) significantly exceeds that of accuracy (1.02%). This demonstrates that L-TTA can handle diverse specific domains besides re-balancing and shows impressive adaptability.
Results on the Long-tailed Corruption Benchmark. We aim to mimic more realistic scenarios with this benchmark and report the comparison results in Table 3. We can observe that the per- formance gains on L-TTA is expanded, which outperforms existing SOTAs by 2.87% and 2.64% in averaged accuracy and macro-F1; morcover, by comparing Table 2 and Table 3, we further veal that under noisy conditions, the superiority of previous prototype-base based diminish substantially, nearly matching the baseline TPT; This may stem from their dependence on high-quality prototypes and incapacity to balance different classes. By contrast, our L-TTA utilizes synergistic prototype- sand active shortcut learning for rebalancing, exhibiting more stability under corrupted settings.
Efficiency Study.
We show the results ImageNett in Table 4 with imb=10. It is observed
Table 5: Results on other backbones. \: invalid outputs.
CLTL
CLIH-
14
LigG
METACI
LI BigG
Method
Acc.
Mac.
Acc.
Mac.
Ace.
Mac.
Acc.
Mac.
that training-free methods like
TPT
67.16
66.08
67.57
66.45
68.09
65.56
ZERO, MTA and TDA perform
TDA
69.75
66.95
72.12
67.56
72.40
70.46
75.76
71.78
suboptimal (especially with cor- rupted data), for their sensitivity to data quality. DPE yields the
DPE
71.03
67.13
73.00
69.07
75.50
71.23
76.74
73.32
SCAP
69.64
67.74
70.92
68.54
71.70
67.88
L-TTA
73.07
70.15
74.67
71.37
76.75
72.77
77.91
74.46
lowest complexity but performs suboptimal; For L-TTA, the main computational overhead is to up- date two prototypes and shortcuts: prototypes are updated in parallel, while optimizing shortcuts is free of gradient tracking throughout the backbone, thus L-TTA only incurs minor computation overhead. Statistically, L-TTA shows more effectiveness than the cumbersome SCAP, WATT and RLCF which requires the gradient propagation along the visual encoder. Meanwhile, it embraces improvement in both accuracy and macro-F1 (1.14% and 1.67%). Conclusively, L-TTA keeps the trade-off between performance and efficiency.
Results on Other Backbones. We further explore the adaptability of L-TTA on stronger backbones. We compare our L-TTA with competitive baselines on four additional backbones (ViT-L/14, ViT- H/14, LigLIP-L/16 (Zhai et al., 2023), and MetaCLIP-Bige and report the averaged results over 10 fine- ane-grained datasets in Table 5 (Per-dataset results are in Appx. L). The performance gains of our model remain salient (an average of 1.5% Acc. / 1.8% Mac.) across all backbones. This because L-TTA effectively utilizes sample-level information and actively promotes continuous image-text alignment for re-balancing. We argue that these designs are essential for any backbone when faced with long-tailed TTA.
(Xu et al.. 2023; Chuang et al., 2025)),
4.2 ABLATION STUDIES AND SENSITIVITY ANALYSIS
Affinity Coefficients X1 and >2. We report an ablation study of X1 and x2 on ImageNet with imb as 10 in Figure 4.a. Specifically, we find that lowering a and Ag, i.e. the contributions of SyPs, generally yields poor performance; this indicates that our Synergistic Prototypes and Rebalancing Shortcuts undeniably contribute to the performance. However, we also observe that excessive A and A values lead to a slight degradation. As a trade-off, we set x and X as A₃ = 6, X₂ = 6.
Different Components. L-TTA consists of three components: Synergistic Prototypes (SyPs), Re- balancing Shortcuts (RS), and Balanced Entropy Minimization (BEM). We ablate each of them or their combinations under backbone ResNet50 or VIT-B/16 and report the results in Table 6. We find dropping DPs or EPs leads to decrement of about -3.95% / -3.22% in macro-F1 and -3.77% /

---

Published as a conference paper at ICLR 2026
(6,6)
Accuroc
Anourney
Maors-F1
73.15
7345
73
74.22
75.15
TU
68.8
12
67.
063
6633
1/.)
0..00
07.50
$7.1
35
4-
(a)
66 24
(c)
3.5
0.1
Ponalty
0.5
1.6
3.0
3.0
Figure 4: The ablation studies of (a) X and X2 evaluated on ImageNet; (b) balance factor eval- uated on ImageNet; (c) hyper-class vector (expert) number RSs evaluated on ImageNet and Food101; (d) Penalty factor 3 in BEM evaluated on ImageNet and Food101.
-1.78% in macro-F1, indicating both DPs and EPs in the SyPs contribute to the performance. Also, RS is instrumental in the further improvement, without which we observe considerable degrada- tion. Furthermore, SyP+RS+BEM always achieves superior performance to other settings, which shows BEM is an eligible advancement of TTA under Long-tailed settings. In summary, all of the components function synergistically and contribute to the overall performance of L-TTA.
The balance factor 77. We ablate the balance factor 77 on ImageNet by varying its value in Figure 4.b. We find that combining our class re-allocation loss results in a performance enhancement of about 1.19% in accuracy/maero-FF compared with solely employing the patropy-minimizatinn loss (i.e, 7 0). However, excessively large 7) also leads to the performance degradation. We speculate that this potentially stems from an over-emphasis on cluster homogeneity, which introduces opti- mization challenges and consequently hinders adaptation with limited data-stream. Our experiment results demonstrate that setting 1 = yields the best performance.
Vector number K in RS. We alter K from 0.1 to I on ImageNet and Food101 and report the
Table 6: Ablation studies on components.
results in Figure 4.c. Generally, we find that excessively small choices lead to performance degradation, which may result from the insuf- ficient modeling of clusters in the dataset; In addition, we also observe a gradual degrada- tion when K' keeps increasing, which may re- sult from exacerbated training challenges. Our experiment results show that setting K = 0.2 yields the best performance.
Methods
RN50
VIT-R/16
Acc.
Mac.
Acc.
Mac.
DP
57.38
31.66
68.68
63.40
DP+RS
58.10
52.24
69.76
64.12
EP
56.94
51.07
67.54
62,20
EPARS
57.43
51.86
68.03
62.77
SyPOD++PP>+S
59.59
53.04
70.94
65.17
SYP+RS+BEM
59.82
53.67
71.30
65.83
Penalty factor 3. We vary 33 from 0.1 to and report the results in Figure 4.d. We find that both higher (22) and lower (<0.5) choices generally result in the performance degradation; With appro- priate parameter selection, our L-TTA outperforms both the approach leaning on class priors (5=8) and the method relying solely original logits (8=0.1) 1) by up to 0.64% in accuracy/macro- F1. This validates the effectiveness of the additional penalty term we introduced in BEM. We find that setting
yields the best performance.
Robustness to dynamic head/tail- class shifts. This experiment inves- tigates the model's robustness to dy- namically changing head-tail classes.
Table 7: Ablation studies on head/tail class shifts.
Dataset
ImageNet
Flowers
0 1/3
2/3 0
1/3
2/3
Acc.
71.30 71.34 71.47 71.52
74.60 74.98 74.95 75.08 68.62 68.62 68.85 68.98 69.01
We vary the sampling probability for tail-class samples, where a larger increases their likelihood of appearing earlier in the stream. Results in Table 7 demonstrate L-TTA
Mac.
65.72 65.76 65.86 65.93
maintains stable performance across c, indicating the strong resilience of our proposed model.
5 CONCLUSION
This paper aims to extend Test-Time Adaptation (TTA) to harsher and realistic long-tailed scenarios. We propose our novel L-TTA, which consists of synergistic prototypes for enriching tail classes, rebalancing shorteuts to register knowledge and adapt models, and balanced entropy minimization as the first variant of EM targeting on long-tailed TTA tasks. L-TTA achieves superior performance over 15 datasets under various long-tailed ratios, while showing impressive efficiency.
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ACKNOWLEDGEMENT
The authors gratefully acknowledge the support from the National Natural Science Foundation of China (NSFC) under Grant Nos. 62402472, and 12227901. This work was also supported by the Natural Science Foundation of Jiangsu Province (No. BK20240461), the Project of Stable Support for Youth Team in Basic Research Field, CAS (No. YSBR-005) and the Academic Leaders Cultiva- tion Program at USTC. The Al-driven experiments, simulations and model training were performed on the robotic AI- Al-Scientist platform of Chinese Academy of Sciences.
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Next, we separately discover the gradient term for the top-1 logit entry, $1, and non-top-1 logit entries. For the gradient of H. with respect 81, deduce the specifie gradient expressions as Published as a conference paper at ICLR 2026
Appendix Long-tailed Test-Time Adaptation for Vision-Language Models
The Supplementary Material of the paper is organized as:
Appendix A: We give the proofs of propositions.
Appendix B: We introduce datasets and methods.
Appendix C: We report full results on CLIP-VIT-B/16.
Appendix D: We report full results on CeeP-ResNet50.
Appendix E: We report the prompts used.
Appendix F: We report extra ablation Studies.
Appendix G: We report extra discussions of applying traditional LT methods.
Appendix H: LLM statement.
Appendix I: Reproducibility statement.
Appendix We report all experiments results on long-tailed corruption benchmarks.
Appendix K: We report all experiments results on balanced datasets.
Appendix L: We report all experiments results on larger backbones.
Appendix M: We report the tuning efficiency.
A PROOFS
A.1 PROOF OF PROPOSITION 1
Proposition 3 For the long-tailed TTA, denote z == (21, 22, 23.,, 23.,,cc) as the output logits, which we assume [Ca] > |C2l > (Cal.>> |Ccl. |C,l is the class cardinality of 24. We split C into Craza and Craii with certain measurements. Denote Hi(-) as EM loss. The following holds true (6%/5b equals Vaa in the proposition of the main paper):
8H
8H
Ec 82s
Ecaa
(12)
82;
Proof. We can separate the overall expectation into the top-1 expectation term and the non-top-1 expectation term. With the law total expectation, we deduce:
SH
SHI
Eeaw 824
Meops Pread (Moop1)
82,
HH
+ +
Mnon) (Mann))
52;
(13)
8HI
Ec... 824
Eca..
Meop1 (
Praiz (Meop1)
8zz
5H
+ +Ecaa
Mnoa ) (MMnn))
82;
Where P(Meopt/ned represents the statistical probability of being top-1 / non-topl of the en- tries. Under the laigetailed scenario, the following assumption holds true: Pheaa(Meopp)) > Prati (Meapt) )PPana(-MMon) < Pea (Mono))
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follows, similar to (Wu et al., 2025):
8H
6
Pt log pt)
82,
dzg
log P + 8p:)
(14)
82s
pj log pi Pi Σ
(-pip:
PiP: log pi PIP.)
1=1
We finally derive:
OTH Ec
Mtop1)
Ee(
Σ
PiP
log
P1
(15)
881
pt
1=1
The last inequality can be easily deduced with the assumption that 81 is the largest entry in the logits, since the last log term always greater than O.
Then consider non-top-1 logit entries. We ignore the specific correlations between different classes and treat them as identical elements. To facilitate the discussion, we redirect our focus to their summation Snons Shon
Σ Zz. We make the following deduction:
1
Σ
PiP: log
0
(16)
pt
t=1
1=1
This shows that the gradient summation with respect different entries is O. Then, derive that:
8HI
8H/08z
S1H
SH
0
(17)
88rost
88rest/
ddz
8zz
881
2=2
Then we have:
8TH Fec 82;
OTH
8TH grest
FCc
(Moan)
Eec
(18)
821
82g/ 88rest
Consequently:
8H
8H
EE Mtop1)
IEc
M
(19)
08
8z:
This means that the gradient of entropy with respect the top entry theoretically smaller than that with respect to non-top 1 entry. Since head classes play as the top- entry more frequently, we can easily deduce that proposition holds true.
A.2 PROOF OF PROPOSITION 2
23. 23.,,3.... σC} we assume |C11 > |C2l > (aal.. >> |Col- |c,] the class cardinality of 24. We split into Creed and Cusin with certain measurements. Denote H(-) EM loss. The following holds true:
Proposition 4 For the long-tailed TTA, denote z. {21, 23..,
the oulput logits, in which
8TH 82
8H
6TH
5TH!
E
EEc..
|BCrasd
EE
(20)
8zs
8z:
82
Proof. Similarly, we have:
öTH
81H
HIH
Ecaue
Ec
Eca.e
Meops Pread (Mrop1) + Ecaua
Mnon Proad (Mnon)
8zz
8z
82;
(21)
5TH
8TH
HTH
Ec...
Ec..r
Meops Peail (Meop1) + Eca.
M Peasl Mlaii(Maou) (moa)
8z4
8z
821
8HH
8HH
8HP
Ca
Ecz.a
Meopp Phead (Mrops) Ecame
[Mnon (hooa)Peeee
Phead (Mnon)
8:2
8zi
821
(22)
8TH
5TH
5H!
E.
Ec..
(Moos))Pasil (Mropa)
Ecco
[Mnon) Peaill (Mroa)
8zz
8z;
82,
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Published as a conference paper at ICLR 2026
Returning our BEM, it incorporates additional prior term into the logits. Since BEM primarily exerts stronger influence on uncertain categories, we could assert that EM and BEM yield the same behavior in confident (top-1) 1) classes. Focusing non-topl classes, head classes receive greater negative gradient gains than tail classes under equivalent conditions. That is:
8HH
8H1
OIH'
8THI
>
[Mnnn) - Ber. ( 82m
[Mnan) > Eer.al 32m
[Mnon] Eercal
M
(23)
8am
82m
Since the following two formula always hold true:
OH
8HH
OIH
8H
Eco
Meop1) +0 Er
Mnon) Errosd òzz
E BCaii
>> 0
(24)
8z
dzs
8.s
We can derive the following formula:
OH,
8H
8TH
OTH
Ecrns
~ Phoss (MMon)) (Eeraa
(Maca) - Bren
[Maoca) > 0
82m
82m
82m
82m
(25)
8H Bca.
8H'
ÖIH
OIH'
Bca.
Peasl (Mzon) (Eer
(Maaa)
Ec
[Maoaa) 0
82
82m
62m
82m
According to Equation 23, we can easily derive the following formula:
SH
STHI
5HH
OH'
Be
E
>
0
(26)
8mm
8mm
82m
82m
8TH
6H
OT
5TH
Bcw.
Eewa
(27)
821
82g
821
821
Here we proved Proposition 2.
B INTRODUCTION OF DATASETS AND METHODS
Datasets:
We list the detailed statistics and introductions of datasets in Table 46; Methods: We list the introductions of methods used in our experiments as follows:
TPT (Shu et al., 2022): TPT proposes to crop various augmented views from the original image, and select the most confident predictions to conduct entropy minimization.
C-TPT (Yoon et al., 2024): C-TPT calibrates the uncertainty of model predictions by en- forcing texture representations different prompts to closer to their centroids.
MTA (Zanella Ben Ayed, 2024): MTA is free of optimizing prompts. Instead, it searches the mode for different views and optimizes their inlierness. MTA can be adapted to API- based models.
TDA (Karmanov et al., 2024): TDA proposes a positive cache to store discriminative class knowledge and negative cache to store the noisy, spurious, or OOD semantics for different classes. TDA is training- free.
DPE (Zhang al., 2024a): DPE introduces dual prototype evolving to adapt each step. It's free of back prooppronnnntion over vision and text encoders.
O-TPT (Sharifdeen et al., 2025): O-TPT further enhances C-TPT by learning orthogonal text representations to obtain textual dispersion.
SCAP (Zhang et al., 2025a): SCAP introduces supportive cliques to learn various attribute prompts for each class, accompanied by a graph retention technique to accumulate learned knowledge from previous batches. In the inference stage, utilizes both the preserved attributes and the learned prompts for robust prediction.
WATT (Osowiechi et al., 2024): WATT leverages diverse text prompt templates to gen- erate pseudo labels for model updates, then employs weight averaging (either parallel or sequential) to consolidate learned information. The text embedding ensembled during evaluation to enhance generalization across domain shifts, achieving strong performance even with episodic images without additional transformations trainable modules.
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CLIPArTT (Hakim et al., 2025): CLIPArTT introduces lightweight TTA method that gen- erates instance-speciffe text prompts from top-K predictions and computes pseudo-labels via image-text similarity matrices. During training, it fine-tunes normalization layers using cross-entropy loss to enhance robustness across domain shifts.
ZERO (Farina al., 2024): ZERO an ultra-efficient training-free TTA. retains confi- dent views and perform marginalization to get the final prediction without backpropagation, outperforning SOTA methods while being 10x faster and more memory-friendly.
RLCF (Zhao al., 2023b): RLCF is universal TTA method that uses CLIP ward model to provide feedback. Concretely, it first samples task-specific candidates via beam search, then optimizes model parameters via REINFORCE algorithm with baseline- adjusted CLIPScore reward to enhance zero-shot generalization across classification, re- trieval, and captioning tasks.
C EXPERIMENT RESULTS ON CLIP-VIT-B/16
The full experiment results of Cross-domain Benchmark built upon CLIP-ViT-B/16 backbone under the imbalanced ratio of (10, 20), {50) shown Table 11,12 respectively. The head tail accuracy of the OOD benchmark is shown in Table 16. The head / tail accuracy of Cross-domain Benchmark shown in Table 18, 19. The full experiment results of Corruption Benchmark shown in Table 20, 21. As shown in the tables, L-TTA mostly outperforms existing methods, demonstrating its superiority.
D EXPERIMENT RESULTS ON CLIP-RESNETS0
The full experiment results of Cross-domain Benchmark built upon CLIP-ResNet50 backbone un- der the imbalanced ratio of {10, 20), {50} in shown in Table 13, 14 respectively. The full experi- ment results of OOD Benchmark built upon CLIP-ResNet50 backbone under the imbalanced ratio of { [10, 20, 50} are shown in Table 15. As shown in the tables, L-TTA mostly outperforms existing methods, demonstrating its superiority.
E PROMPTS
We list the prompts used in experiments in Table 17.
F MORE ABLATION STUDIES
Number Augmented Views. we al- ter the number of augmented views in Figure 8.b (For representational convenience, we plot with the total count comprising both augmented views and the original image. For example, 4 corresponds to three augmented views and one original image). We find that the optimum lies in about 8; Excessively large or small choices, both leads to performance degradation, espe- cially in macro-F1. This finding contradicts with the intuition that more augmented views lead to better performance from previous stud- ies (Wu et al., 2025), for which speculate that: although larger number of views can more dis- tinctly reflect various inter-class relationships, it also leads to an increase in OOD samples, thereby contaminating the DPs. Simultancously, greater cropping uncertainty reduces the distinction be- tween EPs, consequently diminishing their ability to indicate "the semantics least likely to occur in this category".. Our experiments show that 16 (15 augmented views) is the optimal choice.
72.5
72.5
70.0
70.0
67.5
67.5
65.0
65.0
62.5
Accuracy
62.5
Macro-F1
Macro-F1
Apturacy
60.0
60.0
10
15
2 4 8 16 32 64128
Temperature
Number of Aug, Views
Table The ablation studies of (a) temperature evaluated on ImageNet; (b) number of augmented views evaluated on ImageNet.
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Temperature. We alter the temperature from 1 to 20 in Figure 8.a. Interestingly, we find that excessively large and small temperature leads to degraded performance in macro-F1; for accuracy, did not observe a clear trend. This phenomenon appears quite anomalous, contradicting the conventional wisdom that temperature adjustment can balance the learning of head and tail classes. However, argue that: this occurs because temperature's functionality in the EM Loss lacks the clarity than when it in Cross-Entrop (CE) Loss. For CE loss, changing temperature (i.e., reweight- ing class contributions) explicitly balances the per-class contribution owing to the one-dimensional nature of logit gradient. In contrast, EM's gradient with respect to logits is high-dimensional, re- stricting the optimal temperature a narrow range where deviations in either direction theoretically impair model efficacy. Our experiments show that choosing 2 yields the optimal performance.
G CAN CLASSIC LT METHODS BE WELL APPLIED TO LONG-TAILED TTA?
While this paper designs various techniques for
Table 9: Comparison with classic LT methods.
TTA under long-tailed settings, we would like
RN50
VIT-B/16
to also discover the performance of some clas-
Methods
Acc.
Mac.
Acc.
Mac.
sic methods, like balanced softmax and logit adjustment to this task. We first briefly intro- duce these methods as the following:
MTAI
57.30
51.23
67.79
62.57
MTA+BS
57.31
51.28
67.87
62.33
MTA+LA
57.40
51.30
67.90
62.68
MTA+BEM
57.84
51.91
68.25
63.34
DPE
57.96
52.16
70.16
64.19
Logit Adjustment (LA) (Menon et al., 2020): LA is classical LT method that directly adds the class prior, i.e, its proportion of data volume across all classes, the logits. LA can be ap- plied in both the training stage, as well as the inference stage (i.e. post-hoc adaptation). For both stages, LA can be formulated as:
DPE+BS
57.92
52.18
70.19
64.23
DPE+LA
58.00
52.25
70.24
64.35
DPE+BEM
58.45
52.76
70.57
64,80
Ours-BEM+RR
59.50
52.76
70.75
65.12
Ours-BEM+LA
59.57
53.14
71.01
65.25
Ours
59.82
53.67
71.30
65,83
|C| Σ, C,1
P= Oz)) z=z---0g(1 (T), π
(28)
Where 7 is the temperature. Notably, the original paper only discuss LA's effects with supervised cross-entropy loss. indicates that LA enlarges the prediction gap between dominant classes and rare ones that the model would better distinguish them.
Balanced Softmax (BS) (Ren et al., 2020): BS proposes to directly combine the class cardinality into softmax to achieve the head-tail balancing. can be formulated as:
exp((.) P. Σ, ([C]/exp(zz)
The equation 29 is nearly similar with equation 28, where the only difference lies in the signal of the appended term and the temperature. In our experiments, We optimized the temperature to its optimal value.
So, does these techniques really perform well on our long-tailed TTA tasks? we report the ablation results in Table 9 with RN50 and VIT-B/16 as the backbone respectively. We find that the perfor- mance gains from incorporating either LA or BS were quite limited. For instance, with MTA, using LA showed almost no improvement, while BS provided only modest gains of approximately 0.10% in accuracy and 0.08% in Macro-F1, RN50. a enhancement within the standard variations. This validates our intuitive explanations in the main paper that merely adding class prior is not adequate for TTA tasks. In contrast, our BEM yields considerable performance improvement: For MTA, we observe improvement of about 0.64% and 0.68% WITH RN50, 0.46% 0.77% with VIT-B/16. This also reflect that our BEM is widely applicable and shows great compatibility.
Some uni-modal LT-TTA methods. We note that some uni-modal TTA methods have indeed begun addressing long-tail challenges. For instance, SAR (Niu et al., 2022)'s framework includes label shift among its three practical TTA scenarios. SAR then employs batch-agnostic norms, noisy samples filtering and sharpness-aware entropy minimization deal with it; DELTA (Zhao et al.,
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2023a) introduces Test-time Batch Renormalization (TBR) refine normalization statisties, and Dynamic Online re-weighTing (DOT) to mitigates class bias in long-tailed TTA. However, these methods unanimously fails the VLM-specifie failure modes introduced in our Figure 1.
H THE USE OF LARGE LANGUAGE MODELS (LLMs)
LLMs are only for polishing the writing of this paper.
1 REPRODUCIBILITY
The code is available.
J EXPERIMENTS ON LONG-TAILED CORRUPTION BENCHMARK
We conduct experiments other corruption types besides gaussian blur, which are first defindd by ImageNet-C: gaussian noise, shot noise, impulse noise, speckle noise, defocus blus, motion blur, blur, snow, frost, fog, brightness, contrast, jpeg.ppeeeee compressiin, pixelate, saturate, elastic transsorr The results are shown in Table 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38. 39. The averaged results shown in Table 23.
As evidenced by these results, L-TTA's performance advantage not only persists but becomes more pronounced under corruptions. Furthermore, have three key observations:(a): Training-fre methods generally struggle on this benchmark. For instance, MTA fails on 12 of 16 corruption types, and the advantages of TDA and ZERO substantially diminished. These methods rely the assumption that features form reliable, consistent clusters- an assumption violated by corrup- tions that fragment distributions and disrupt mross-modal alignment. (b): The gains of unimodal, rraiiig-required methods (e.g., SCAP) degrade, converging to the level of classic TPT. This stems from their lack of genuine cross-mes mooll co-adaptation, causing them amplify modality misalign- ments under corruption.
(c) L-TTA delivers extensive and consistent improvements. This is be- cause that: instead of relying on distributional assumptions, L-TTA leverages instance-specific cues (SyPs and BEM) and employs proactive modality- learning (RSs) to continuously counteract corruption-indee induced biases within and across modalities.
K EXPERIMENT RESULTS ON BALANCED DATASETS
The experiment results on balanced datasets are in Table 45, The results show that L-TTA can also outperforms existing methods on balanced datasets.
L EXPERIMENTS ON LARGER BACKBONES
The experiment results on larger backbones for TPT, TDA, DPE, SCAP and L-TTA are shown in Table 40, 41, 42, 43, 44. The performance improvement our model is consistent across different backbone architectures, with only minor fluctuations observed. This is because L--TTA effectively leverages sample-level information and actively promotes continuous image-text alignment, which generally beneficial regardless of the underlying backbone in long-tailed test-time adaptation.
M TUNING EFFICIENCY
Our method involves tuning several additional parameters for different datasets, but this process is more of an op- tional refinement choice rather than a necessary step that critically affects model behavior. We explain this from two perspectives: First, as shown in Figure varying 7, K, and 3 within a broad range ( (x0.5-2) results in only minor performance changes of about 0.2%. Furthermore,
Table 10:
Ablation studies on Per-
dataset Syype--prammete FineTuning.
LT-CDB
LT-OODB
Method
Acc.
Mac.
Acc.
Mac.
w/o PHFT L-TTA
68.52
63.12
64.88
59.98
68.77
63.44
65.19
60.49
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Published as a conference paper at ICLR 2026
Table 11: Results on Long-tailed Cross-domain Benchmark under the imbalance ratio of 10 (upper one) and 20 (lower one). We conduct 5 runs for each experiment under each imbalance setting and report the Accuracy (Acc.) and F1-Macro (Mac.). All the methods are built upon CLIP- VTT-B/16 backbone. The best results are in Bold.
Methods
Caltech
Pets
Cars
Flowers
Food10)
Acc.
Mac.
Acc.
Mac.
Acc.
Mac.
Acc.
Mac.
Acc.
Mac.
CLIP [CMM/211
93.17
90.47
84.68
85,23
62.58
62.21
65.93
62.69
82.77
81.76
TPT [NeurlPPS222
93.97
91.61
84.50
84,32
63.97
63,37
67.40
63.02
83,89
82.92
C-IPT [CCLR224]
MIA (CVPR221
93.18
91.54
85.08
86.03
64,52
64.09
63.96
62.73
83,99
83.11
TDA [CVPR
94.06
90.96
87.71
86.01
68.17
63.06
71,70
64.76
86.23
83.82
DPE [NourPS
94.91
92.56
91.18
89.33
68.45
64.74
74.36
68.70
84.87
82.74
O-TPT [CVPR'25]
93.51
91.08
85.96
86,40
63.40
63.08
68.37
64.53
82,72
81.47
[CVPR'25]
L-TTA (OBi))
95.12
92.46
91.62
90.22
70.33
65.54
74.91
68.99
85.53
83,33
SUN197
UCFI01
Methods
Acc.
Мас.
Apc.
Mac.
Acc.
Mac.
Acc.
Mac.
Acc.
Mac.
[CCML211]
TPT [NcurPSS22
16.75
17.71
64.13
60.48
44.30
38.03
37.69
31.76
66.75
62.86
C-IPT (4CLX'24
19.17
19.58
63.67
60.40
44.61
39.35
37.72
32.27
66,93
61.75
MTA [CVPR'24]
20.14
20.07
60.21
43.99
38.01
39.52
32.79
67.47
63.43
TDA [CVPR'24]
20.83
19.95
66.59
62,60
46.30
36.86
57.02
48.25
71.09
64,26
[NcorPS
O-TPT [CVPR'25]
18.78
18.68
64.26
60.70
45.08
38.87
38.37
32.75
66.09
61.14
SCAP (CVPR'22]
22.92
63.73
61.29
44.84
37.70
52.48
45.53
68.54
66.71
L-TTA (Ouss)
27.02
24.14
69.79
65.44
31.63
47.50
$7.07
48.67
70.77
64.86
Caltech
Pets
Cars
Flowers
Food101
Methods
Acc.
CLIP [CML'21]
92,98
90.37
83.80
83.25
62.10
60.78
66.18
62.37
82.34
79.01
TPT (NcurtPS
93.50
91.10
83.71
84.06
63.37
61.93
67.17
62.40
83.45
81.47
CTTP [CCK 241
93.19
90.88
85.50
86.09
63.37
61.81
67.62
63.36
82.30
79.89
MTA [CPRR221]
93.58
91.47
84.05
84.72
64.43
63.64
64.41
60.93
83,59
81.56
TDA [CVPR'24]
DPE [NcurIPS224 24]
94.67
91.92
90.04
87,20
68.46
61.72
74,17
66.67
83.90
79.70
O-TPT [CVPR'25]
93.15
85.35
90
62.80
61.33
67,70
63.37
82,32
79.87
SCAP (CVPR'25)
91.32
86.92
86.47
78.73
63.90
61.61
68.27
62.14
83.07
82.91
L-TTA (Ouns)
95.28
92.94
91,91
87.30
70.44
63.46
74.89
67.22
85.65
80.53
Methods
Aircraft
SUN397
DTD
EureSAT
UCF101
Acc.
Mae.
Acc.
Mac.
Acc.
MaС.
Acc.
Macc
Acc.
MBc.
[ICM]22]
43.37
TPT 222
15.55
17.17
64.04
57.91
43.76
37.91
37.12
30.63
65.82
$9.16
MTA (CVPR'24)
18.16
18.85
62.57
57.17
43.76
38.01
40,31
31.31
67.23
61.84
TDA (CPP)224)
25.38
20.26
65.40
58.79
44.72
33.66
55.34
53.73
70.39
61.49
DPB [NcorIPS22 24]
25.77
21.35
67.89
61.91
47.79
40.51
56.07
47.04
70.32
62.45
O-TPT [CVPR'25]
17.48
18.75
62.86
36.99
43.95
37.96
38.05
31.21
66.82
$9.35
[CVPR'25]
L-TTA (Oum)
26.67
21.85
68.48
62.40
47.02
37.43
57.72
47.41
71.37
62.82
we report an additional experiment in Table 10, which compares the results using identical hyper-
parameters (tuned on ImageNet in the above) and parameters tuned for each dataset. The results show that L-TTA remains consistently SOTA under this fixed configuration, with performance fluc- tuations within only 0.3%.
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Table 12: Results on Long-tailed Cross-domain Benchmark under the imbalance ratio of 50. We conduct 5 runs for each experiment under each imbalance setting and report the Accuracy (Acc.) and F1-Macro (Mac.). All the methods are built upon CLIP-ViT-B/IC backbone. The best results are marked in Bold.
Caltech
Pets
Cars
Flowers
Food101
Methods
Acc.
Acc.
CLIP [ICML
93.15
89.01
82.05
81.40
60.80
57.28
64.91
56.94
81.92
73.94
IPT [NourIPS 221
93.99
90.27
82.31
80.91
62.19
58.94
66.25
57.48
83.11
75.16
C-TPT (CCLR'S
93.57
90.00
84.55
82.65
59.58
66.92
58.35
82.13
73.66
MTA (CVPR'24
93.68
89.90
83,95
82.25
62.39
59.49
65.58
57.06
83.26
75.84
TDA [CVPR
94.79
90.14
83.72
80.34
68.50
55.13
70.82
57.76
85.64
76.21
DPE INcudPP'244
94.97
90.93
81.01
69.74
56.97
73.32
61.26
83.57
73.59
O-IPT (CVPR'25)
93.57
89.90
84.43
82.74
61.84
59.08
66.81
58.65
82.13
73.72
SCAP (CVPR'24)
91.36
86.97
85.71
72.84
63.89
61.62
60.13
83.05
82,90
L-TTA (Ours)
95.69
91.47
89.68
84.70
69.52
60.45
73,04
62.84
84.30
76.97
Methods
Aureraft
SUN397
DTD
EuroSAT
UCFI01
Acc.
Acc.
Mac.
Acc.
CLIP 2I
11.12
16.47
59.71
49.90
41.78
33.26
36.91
28.99
66.26
55,54
TPT [NcurIPS 22]
11.90
16.38
63.75
52.86
42.33
33.90
36.56
27.77
67.56
58.10
C-TPT HCLR'22
15:40
17.64
62.91
52.29
42.89
34.72
38.01
26.10
68.36
56.13
MTA (CVPR'24
15.12
16.88
61.91
52.40
42.61
34,80
41.52
28.74
68.76
58.91
TDA [CVPR'20
22.91
16.71
65.44
54.64
43.17
33.08
48.07
45.19
71.92
58.01
DPE [244
21.95
20.60
68.03
56.52
46.51
35.75
52.78
41.03
68.07
53.87
O-TPT (CVPR'2S 25]
15.54
17.82
62.25
50.96
42.89
34.93
38.60
28.11
68.06
55,22
(CVPR'24
35.71
35.41
L-TTA (Ours)
22.79
19.64
68,56
59.76
46.84
37.21
54.79
45,30
69.61
$9.58
Table 13: Results on Long-tailed Cross-domain Benchmark under the imbalance ratio of 10 (upper one) and 20 (lower one). We conduct 5 runs for each experiment under each imbalance setting and report the Accuracy (Acc.) and F1-Macro (Mac.). All the methods are built upon CLIP- ResNet50 backbone. The best results are marked in Bold. SCAP (Zhang et al., 2025a) is not included here, because there re no implementation other backbones rather than CLIP- VIT-B/61.
Caltech
Pets
Cars
Flowers
Food101
Methods
Acc.
Mac.
Aec.
Mac.
Acc.
Mac
Acc.
Mac.
Acc.
Mac.
IPT INeuriPS 22]
86.73
83.31
82.07
81.69
56.81
55.49
60.79
56.17
74.27
72.67
C-TPT [ICLR'24]
86.56
82.74
82.41
82.15
54.92
53.94
62.26
58.02
74.51
72.84
MIA [CVPR'24]
85.64
82.68
82.20
82.35
57.09
55.67
60.48
56.21
74.23
72.70
TDA [CVPR'24]
89.43
85.35
86.74
84.73
$7.22
52.34
67.94
60.28
77.75
74.54
DPE [NcurPPS224
89.71
85,82
86.32
84.31
60.10
55.28
68.28
62.47
75.96
73.45
O-TPT [CVPR'S 25]
86.77
83.03
82.20
81 81.88
54.54
53.60
62.44
$8.09
74.56
72.94
L-TTA (Ours)
90,89
86.83
87,35
85.08
60.98
55.91
69.50
62.66
78.35
75.34
Methods
Aircraft
SUN397
DTD
EuroSAT
UCF101
Acc.
Mac:
Acc.
Mac.
Acc.
Mac:
Acc.
Mac:
Acc.
Mac.
TPT [N2
12.39
14.22
60.45
56.46
39.15
33.33
17.58
15.90
61.37
54,09
C-TPT [ICLR 2224
13.09
14.74
60.71
56.80
39.15
32.74
19.86
15.69
61.59
54.74
MTA [CVPR'24]
12.86
14.35
59.71
56.07
39.62
33.93
20.24
16.54
61.92
55.16
TDA [CVPR 24]
15.42
13.39
61.66
56.52
45.24
37.55
50.00
45,14
66.49
57.36
DPE 24
17.83
15.76
62.89
59.25
49.29
44.02
49.34
38.19
62.89
56.06
O-TPT [CVPR'25)
13.17
60.44
56.46
39.15
32.79
30.78
28.05
61.75
55.04
L-TTA (Ours)
19.85
18.31
63.45
59.12
50.95
45.66
50.57
45.10
67.06
58.10
Methods
Caltech
Pets
Cars
Flowers
Food101
Acc.
Мас.
Acc.
Mac.
Acc.
Mac
Acc.
Mac.
Acc.
Mac.
TPT [NeurIPS 22]
86.22
82.83
81.07
79.61
$5,46
52.73
59.85
54.32
73.02
69.72
CTTPT [CC 2424
86.43
82.69
81.84
81.33
54.11
52.54
63.61
58.75
73.77
70.61
MTA [CVPR'22 24]
85.35
82.65
81.58
80.37
$5.73
$3.15
61.04
55.57
73.65
70.78
TDA JCVPR'24
88.34
84.17
85.27
80.63
56.49
49.20
68.33
58.93
77.30
72.34
DPE (N4u
90.39
86.25
87.31
84.58
59,50
52.39
66.99
60.41
75.43
70.23
O-TPT [5]
86.45
82.63
81.67
80.57
53.52
$1.63
61.91
56.89
73.74
70.56
1-TTA (Ours)
90.52
87.42
87.61
85.87
60.58
53.10
69.04
60.67
78.29
71.61
Methods
SUN397
EuroSAT
Acc.
Mас,
Aec.
Mac.
Acc.
Mac.
Acc.
Mac.
Acc.
Mae.
TPT [NeurTPS222 22]
11.11
13.23
611.34
53.74
38.92
31.22
16.92
15.39
61.49
$2.89
C-TPT [ICLR 24)
10.33
12.64
60.52
54.26
39.53
31.60
17.86
16.66
62.01
53.31
MTA [CVPR'24]
11.11
13.09
59.11
52.98
39,30
33.46
18.68
16.37
63.16
54.19
TDA [CVPR 24]
15.34
13,13
61.06
53.73
42.61
32.67
39.00
37.77
66.86
55.10
DPE 244
15.25
13.58
63.12
56.85
45.68
40.75
45.99 .9
35.90
62.67
52,50
[CVPR'2.
L-TTA (Ours)
19.40
15.00
63.94
$6.06
48.56
40.54
50.08
35.91
67.45
55.02
24

---

Published as a conference paper at ICLR 2026
Table 14: Results on Long-tailed Cross-domain Benchmark under the imbalance ratio of 50. We conduct 5 runs for each experiment under each imbalance setting and report the Accuracy (Acc.) and F1-Macro (Mac.). All the methods are built upon CLIP-ResNet50 backbone. The best results marked in Bold. SCAP (Zhang et al., 2025a) is not included here, because there re imple- mentation to other backbones rather than CLIP-VIT-BIE
Methods
Caltech
Pets
Cars
Flowers
Food
Acc.
Мас.
Acc.
Mac.
Acc.
Mac.
Acc.
Mac.
Acc.
Mac.
IPT INcurIPS 22]
85.63
79.80
80.32
75.45
55.09
50.23
60.78
52.72
72.97
63.26
C:IPT [ICLR'24 242
85.99
75.80
72.25
63.29
MTA [CVPR
84.15
79.27
79.57
75.34
54.54
49,46
61.32
51.48
73.23
63.82
TDA [CVPR
87.17
82.48
83.97
76.63
$7.29
45.11
67.73
53.15
76.85
64.63
DPE 244
89.90
82.91
85.09
78.65
58.42
45.03
66,32
54.41
74.68
63.48
0-TPT
[CVPR'25]
79.81
40
74.93
52.20
47.80
63.67
54.11
73.20
63.23
1-TTA (Ours)
91.30
84,43
85.21
79.30
59.44
49.35
69.84
55.84
77.28
63.95
Methods
Aireraft
SUN397
DTTD
EuroSAT
UCF101
Acc.
Acc.
Mac.
Mac.
TPT [Nc
8.40
13.59
61.66
48.88
38.99
29.12
13,00
13.47
63.47
49.20
C-TPT [CCL 24]
7.98
11.37
60.46
49.96
40.11
29.93
13.76
14,64
64.27
51.39
MTA [CVPR 24]
8.54
12.02
58.75
47.89
38.71
29.71
14.39
15.88
64.17
51.62
TDA [CVPR'24]
14.26
11.16
60.29
49.01
41.78
25.78
31,03
30.82
69.23
53.17
DPE 2NcuIPS'24]
17.76
13.66
61.77
50.94
42.06
29.13
49.55
32.97
66.53
49.12
O-TPT [CVPR' 25]
8.26
11.63
60..9
49.94
40.66
29.59
24.14
23.96
64.47
51.73
L-TTA (Ours)
18.48
15.88
62.70
51.73
42.86
51.34
31.86
54.26
Table 15: Results on Long-tailed OOD Benchmark. We conduct 5 runs for each experiment and report the accuracy (Ace.) and macro-F1 (Mac.). All the methods are built upon CLIP-RN50. The "OOD Average" is calculated by averaging four OOD datasets excluding ImageNet. The best results marked in Bold. SCAP (Zhang al., 2025a) is not included because of the above reason.
imb-10
Iet-ANet-A
ImageNer-A
ImageNer
OOD average
Acc
Mac.
Acc
Mac.
Ace
Mac.
Ace
Mac.
Acc
Mac.
Acc
Mac.
TPT [NeurlPS 22]
25.00
23.17
59.72
57.79
12.59
31.81
55.77
$4.28
56.57
50.50
43.26
41.76
MTA (CVPR'24
26.39
25.10
$8.05
56.30
36.04
35.78
55.67
54.50
$7.30
51.23
44.04
42.92
TDA (CVPR'24)
30.62
27.95
63.00
60.08
35.58
33.31
56.68
50.53
58.13
52.88
46.47
42.97
0-TPT [CVPR:25]
22.75
21.30
$7.55
55.84
33.55
30.69
55.04
52.47
56.81
50.77
42. 22
40.08
1-TTA (Dirs)
32.96
30.57
64.88
61.92
37.13
35.93
$9.07
$4.98
$9.82
53.67
48.51
45.85
Methods
ImageNet-A
ImageNer-R
ImageNot-8
Image:Ne--V2
ImageNet
OOD average
Asc
Mac.
Acc
Mac.
Acc
Mac.
Acc
Mac.
Acc
Mac.
Acc
Mac.
[NcurieS
31.96
30.55
40.82
C-TPT [IC R224
22.96
21.45
$7.94
54.15
31.36
30.11
55.79
52.06
54.37
50.27
42.01
39.44
MTA (CVPR'20 24]
27.53
25.38
58.79
57.83
35.35
34.04
54.22
$3.05
57.94
51.02
43.97
42.58
DPE [NeurPPS
31.31
28.75
63.27
58.96
35.07
33.84
57.83
50.81
59.02
53.51
46.87
43.09
0-TPT [CVPR 25]
22.40
20.70
57.47
53.81
31.33
32.98
56.06
$2.29
55.
51.14
41.82
39.95
lnbes50
Methods
ImsgeNettAA
JenggeNet-R
ImageNet-S
ImageNet-V2
ImaggeNt
OOD
Mac.
Mac.
Acc
Mac.
Mac.
Mac.
Acc
Moc
TPT [NeuriPs 221
24.28
19.74
60.08
49.66
11.19
27.53
55.005
53.00
55.71
50.82
42.65
$7.45
C-TPT (3CL
22.09
18.07
58.37
48.30
32.31
26.84
56.03
$1.75
$3.93
49.75
42.200
36.24
MTA (CVPR
27.63
25.38
58.79
57.83
35.35
34.04
54,22
53.05
55.25
50.59
44.00
42.58
TDA (OVER 241
30.20
23.71
63.19
50.54
34.10
30.10
56.85
49.00
$6.14
52.86
46.08
38.34
DPE [NeurIPS
30.76
24.92
64.81
$2.10
35.05
31.07
56.42
47.15
57.22
53.62
38.81
O-TPT [CVPR'25]
17.72
L-TTA (Dinn)
30.64
25.46
65.58
58.42
36.11
33.93
$7.73
54.80
59.31
53.10
47.52
43.40
25

---

Published as a conference paper at ICLR 2026
Table 16: Head / Tail accuracy Long-tailed OOD Benchmark. We conduct 5 runs for each experiment and report the accuracy (Acc.) and macro-FI (Mac.). All the methods are built upon CLIP-VIT-B/16. The "OOD Average" is calculated by averaging four OOD datasets excluding ImageNet. The best results are marked in Bold.
ImageNet-A
ImageNet-R
ImageNet-S S
ImageNet-Vi V2
ImageNet
Average
imb=10
Head
Tail
Head
Tail
Head
Tail
Head
Tail
Head
Tail
Head
Tail
TPT [NeurIPS 22]
53.27
50.71
78.91
72.27
48.04
36.52
63.11
55.36
66.61
63.50
61.99
55.67
C-TPT [ICLR'24 24]
52.17
47.78
77.59
73.70
48.34
38.25
62.12
56.41
66.10
64.87
61.26
56.20
MTA [CVPR 24]
61.07
53.73
80.38
75.22
50.34
43.17
64.74
59.20
68.08
66.19
64.92
59.50
TDA [CVPR'24)
64.47
57.31
85.58
78.31
51.00
41.28
68.45
64.49
70.45
64.92
67.99
61.26
DPE [NcurPS'24 24]
64.31
56.79
85.13
78.22
52.35
42.45
69.71
63.61
71,36
66.87
68.57
61.59
O-TPT [CVPR'25]
54.67
50.64
78.15
72.22
48.66
35.10
63.19
56.42
67.14
66.53
62.36
56.18
SCAP [CVPR'25]
62.46
55.72
83.72
72.45
51.86
38.54
68.95
63.43
65,30
65.84
66.46
59.20
I-TTA (Ours)
65.30
56.90
86.00
79.28
53.51
44.00
70.02
65.30
72.04
68.81
69.37
62.86
ImageNer-A
ImageNet-R
ImageNer-S
ImageNet-V2
ImageNet
Average
imb=20
Head
Tail
Head
Tail
Head
Head
Tail
Head
Head
Tail
TPT [NeuriPS 22]
52.74
47.92
77.58
74.01
48.17
38.49
61.13
55.47
66.39
62.80
61.20
55.74
C-TPT [ICLR'24
52.03
46.09
76.52
73.75
48.21
37.54
63.69
55.92
62.35
61.16
60.56
54.89
MTA [CVPR'24 24]
61.07
53.73
80.38
74.22
49.09
43.87
63.12
58.89
68.04
62.87
64.34
58.72
TDA [CVPR'24]
64.72
56.69
85.33
77.70
50.71
41.66
66.05
66.01
68.30
62.33
67.02
60.88
DPE [Neur 24]
63.49
56.82
85.00
77.66
52.24
42.91
68.12
64.25
70.72
65.61
67.91
61.45
O-TPT [CVPR'25]
54.28
50.42
78.16
73.93
48.49
36.95
63.54
55.07
66.09
62.55
62.11
$5.78
SCAP [CVPR/25]
62.33
54.31
83.60
76.51
51.66
36.50
68.55
62.17
65.28
65.10
66.28
58.92
L-TTA (Ours)
65.25
57.12
86.64
78.48
53,66
43.75
68.76
65.34
71.84
68.50
69.23
62.64
ImageNet-A
ImageNet-R
ImageNet-S
ImageNet-V2
ImageNet
Average
imb=50
Hcad
Tail
Head
Tail
Head
Tail
Head
Tail
Head
Tail
Head
Tail
TPT [NeurIPS 22]
52.47
37.25
77.49
72.77
46.89
37.06
60.49
52.59
65.90
59.07
60.65
51.75
C-TPT [ICLR 24]
53.81
35.00
76.35
72.69
46.12
36.76
60.59
50.90
63.72
59.37
60.12
50.94
MTA [CVPR'24 24]
61.07
53.73
80.37
75.22
50.39
41.17
64.74
59.15
65.96
60.41
64.51
$7.94
TDA [CVPR'24]
64.50
56.52
85.41
78.33
50.24
42.22
68.15
63.54
66.51
63.06
66.96
60.73
DPE [24
63.16
55.50
85.01
78.83
49.54
43.36
68.89
62.75
67.03
65.97
66.73
61.28
O-TPT [CVPR'25)
52.88
51.33
78.02
72.65
48.04
35.09
61.10
50.46
62.28
60.98
60.46
$4.10
SCAP [CVPR'25]
62.31
56.05
82.24
76.30
51.23
37.35
68.30
61.29
64.02
59.59
65.62
58.12
L-TTA (Ours)
64.57
57.93
86.81
78.95
51.75
42.57
68.15
63.44
67.84
67.17
67.82
62.01
Table 17: The list of prompts. Notably, following DPE (Zhang et al., 2024a), we also employ the CuPL (Pratt et al., 2023) methods to further expand prompt pools.
Datasets
Prompis
Coltech101
"a photo of a (CLASS)."
Pets
"a photo of a (CLASS). a type of food."
Cars
"a photo of a (CLASS)."
Flowers
"a photo of (CLASS). S... a type of flower."
Food101
"itap of a (CLASS) ). a type of food." "a photo a (CLASS)."
SUN397
DTD
"(CLASS) texture."
EuroSAT
"a centered satellic photo (CLASS)."
UCF101
"a photo person doing (CLASS]."
Aircraft
"a photo of (CLASS), ), type of aircraft."
ImageNet
"a bad photo of the { (CLASS)."
ImageNet-V
"a origami of the (CLASS)).
ImageNet-S
"a photo of the large (CLASS)."
ImageNettA
"a (CLASS) in video game."
"art of the (CLASS)
ImageNet-R
"a photo the small (CLASS))
26

---

Published as a conference paper at ICLR 2026
Table 18: Head / Tail Accuracy Long-tailed Cross-domain Benchmark under the imbalance ratio of 10 (upper one) and 20 (lower one). We conduct 5 runs for each experiment under each imbalance setting. All the methods built upon CLIP-ViT-BE backbone. The best results marked in Bold.
Caltech
Food
Methods
Hend
Tail
Headd
Tail
Head
Tail
Ilead
Tanl
Tlend
Tail
TPT LNcuriPS
95.74
77.30
C-TPT [CCLR22
94.91
90.61
86.66
85.27
65.59
60.34
70.84
65.36
85.53
76.88
MTA (CVPR
95.37
79.85
TDA (CNPR'24 24
97.07
88.44
91.71
89.02
71.25
66.52
73.40
67.32
88.20
81.68
[NerIPS
96.89
81.91
T.TPT (CVPR 25]
93.74
88,79
84.23
80.86
62.78
58.47
69.97
65.10
85.06
76.43
SCAP (CVPR'25]
87.39
62.31
L-TTA (Ouns)
96.36
92,96
94.28
88.36
71.15
69.71
78.34
71.04
88.49
84.78
Methods
EuroS
Teead
Tail
Head
Tail
Teadd
Tail
Head
Tanl
Teln
Tail
TPT [NcuriPS 22]
19:40
10.30
65.80
59.96
45.67
40.60
40.12
31.38
70.11
55.71
C-TPT [CCLR'24
23.17
9.29
64.03
63.00
44.56
44.32
38.97
36.77
71.30
56.85
MTA (CVPR
TDA [CVPR/24]
24.24
17.03
67.54
67.13
45,47
44.15
58.17
57.08
73.84
67.10
[44
26.87
57.62
O-TPT [CVPR'25]
21.21
10.04
61.32
59.07
46,56
41.07
40.35
32.01
71.09
55,80
SCAP [CVPR225]
24.53
40.20
47.77
TTA (Ours)
29.83
22,71
70.27
70.16
54,99
47,59
58.33
$6.69
72.39
68.26
Caltech
Food101
Methods
Hend
Tail
Head
Tail
Head
Tail
Head
Tial
Temd
Tail
TPT [NcurPSS
95.78
C-TPT [CCR
95.29
88.10
85 94
85.01
64.47
60.23
69.67
62.96
84.31
79.06
(CVPR
95.42
TDA (CVPR'24
95.17
93.10
90.29
80.33
67.96
67,83
74.70
67.21
87.19
83.87
INcurIPS22
96.63
66.09
81.99
T-TPT (CVPR
94.75
88.06
83.91
79.91
65.17
56.43
73.32
$9.12
85.60
72.48
SCAP (CVPR'25]
75.91
L-TTA (Ouns)
97.55
91.24
94.93
85.97
71.05
69.85
79.70
69.98
87.23
83.71
Methods
Head
Tail
Head
Tail
Hend
Tail
Head
Taal
Temd
Tail
TPT INcuriPS
C-TPT [[CLR224
21.00
9.25
63.90
60.19
43.95
41.86
39.26
33.83
71.49
58.15
[CVPR'24
40.12
60.67
TDA [CVPR'24]
26.36
21.63
66.14
65.60
45.16
41.76
57.16
48.93
72.35
65.10
244
27.89
T-TPT (CVPR'25 25]
19.92
11.38
63.27
61.07
46.95
36.63
40.92
31.62
70.01
$5.06
SCAP (CVPR'25]
24.26
TTA (Ours)
29.64
22.00
68.66
68.48
47.95
46.39
57.76
55.75
73.07
67.85
Table 19: Head Tail Accuracy on Long-tailed Cross-domain Benchmark under the imbalance ratio of 50. We conduct runs for each experiment under each imbalance setting. All the methods are built upon CLIP-VIT-B/16 backbone. The best results are marked in Bold.
Caltech
Pets
Cars
Flowers
Food101
Methods
Head
Head
Tail
Head
Head
Tail
Head
Tuil
IPT INeuriPS 22
94.55
90.16
84
77.95
66.05
53.65
68.94
59.86
83.78
75.02
C-TFT [[CLR 244
94.41
90.99
85.63
83.23
66.57
53.53
69.21
61.51
83.63
76.01
MTA [CVPR'24]
95.20
90.59
83.97
83.54
65.26
55.88
67.19
62.10
86.33
77.49
TDA [CVPR'20 24]
96.35
91.10
86.40
78.93
69.25
68.02
75.46
60.55
85.20
79.70
DPE
[N44
96.80
90.78
90.25
83.64
70.51
68.47
76.56
69.32
85.67
81.09
0-TPT [CVPR'25]
96.33
87.59
86.98
79.32
64.81
55.69
68.94
59.48
83.97
79.20
SCAP
[CVPR'25]
94.03
86.02
86.71
83.50
64.41
63.42
66.93
62.31
82.85
74.90
L-TTA (Ours)
97.80
91.74
91.14
86.44
71.50
69.54
77.91
70.31
87.51
82.36
Methods
Aircraft
SUN397
DTD
EuroSAT
UCFI01
Head
Head
Head
Head
Head
IPT [22
13.85
8.13
64.78
60.05
43.45
39.69
38.13
31.46
71.14
60.80
C-TPT [ICLR'2
18.10
8.82
62.84
62.65
43.88
39.36
40.29
31.35
71.78
63.13
MTA [CVPR'22 24]
17.63
9.73
61.86
61.38
44.09
40.29
43.15
38.43
71.95
64.14
TDA [CVPR'24]
25.98
15.03
66.18
63.89
45.35
39.72
50.49
41.18
72.39
64.60
DPE [NeurPP/224]
26.47
17.09
68.27
67.53
46.76
43.18
$4.00
50.44
70.19
65.66
O-TPT [CVPR'22 25
15.38
10.58
63.13
61.18
43.73
37.41
40.25
32.81
71.30
61,30
SCAP [CVPR'25]
25.12
42.91
35.50
$1.87
L-TTA (Ours)
26.62
20.77
68.43
68.10
48.88
43.64
57.25
52.04
72.74
67.94
27

---

Published as a conference paper at ICLR 2026
Table 20: Detailed Accuracy on Long-tailed Noise Benchmark under the severity of 0.1 (upper one) and 0.2 (lower one). We conduct 5 for each experiment under each imbalance setting. All the methods are built upon CLIP- backbone. The best results are marked in Bold.
Methods
Average
Callech
Pets
Cars
Flowers
Food101
Acc.
Маc.
Acc.
Mac.
Mac
Acc.
Mac.
Aec.
Mac.
Acc.
Mac.
TPT [NeurlPS'2 22]
54,31
52.16
92.38
88.79
78.94
78.47
57.02
57.01
61.46
56.41
69.71
67.91
C-TPT [CLR
53.61
77.97
55.38
61.52
57.03
66.03
64.15
MTA [CVPR 24]
$4.38
51.66
91.75
87.96
78.24
77.90
57.99
57.72
59.87
54.95
67.97
66.62
TDA (CVPR224)
56.27
79.61
53.83
57.79
66.53
DPE Neur1PS:244
57.33
53.56
92.86
89.90
83.27
80.15
61.63
57.14
66.52
60.66
67.59
61.66
O-TPT [CVPR
53.53
57.08
66.00
SCAP (CVPR'25)
55.32
51.45
91.01
87.63
82.79
76.96
56,52
55. 15.10
59.30
54,10
70.35
63.88
I.TTA (Ouns)
59.69
55.92
92.91
90.51
85.44
82.03
62.76
58.40
68.13
61.14
71.50
67.98
Methods
Aircraft
SUN397
DTD
BuroSAT
UCF101
Image
Acc.
Mac.
Acc.
Mnc.
Acc.
Mac.
A.c.
Mac.
Aoc.
Mac.
Head
Tail
TPT [NcuriPS 22j
12.54
35.37
59.39
53,78
58,
56.36
C-TPT [ICLR224
14.18
15.50
58.09
54.44
37.28
33.90
12.24
9.05
58.29
53.23
$7.25
54,25
[CVPR'24 24]
15.69
15.49
37.75
8.95
58.57
52.85
TDA [CVPR'24]
19.78
17.00
60.48
56.51
38.06
31.11
11.92
13.92
63.68
55,50
58.46
56.19
DPE INcurPPS 24]
19.50
16.14
62.56
57.61
39.02
36.02
15.06
14.79
63.47
57.04
59.20
58.07
O-TFT (CVPR 25)
14.57
16.29
57.66
54.17
37.12
33.77
12.73
9.90
58.18
53,30
56.35
57.50
SCAP (CVPR/25)
19.81
18.07
57.21
55.74
36.88
35.12
14.78
7,30
62.67
57.49
57.20
54.54
L-TTA (Ouns)
22,48
20.51
62.99
39.02
45.86
40.15
18.10
16.68
65.77
59.15
60.64
59.55
Methods
Average
Caltech
Pets
Cars
Flowers
Food101
Mac.
Acc.
Mac.
Avc.
Mac.
IPT [NcurlPSS 22]
42.83
40.13
86.94
81.21
67.96
65.79
44.11
43.34
50.15
44.26
39.13
39.33
C-TPT [ICL 244
41.48
38.56
85.51
79.58
66.57
63.94
42,90
42.52
47.88
42.64
35.71
35.20
MTA (4V
38.09
38.72
84.67
78.83
66.29
64.48
45.38
44.70
4.74
40.08
34.87
36.27
TDA JCVPR'24
42.92
38.21
85.89
79.90
67.24
63.57
47.45
42.01
46.83
40.52
37.12
35.98
DPE NeurtPS
43.82
40.77
86.99
82.57
65.64
63.34
47.33
44.76
49.61
44.73
36.30
36.48
0-TPT (CVPR 25]
41.18
38.55
85.64
79.83
67.26
64.83
42,02
41.48
47.33
42.09
35.56
35. 12
SCAP [CVPR'25]
42.87
39.48
84,99
77.83
67.70
62.42
46.11
42.30
49.03
41.88
37.05
38.00
L-TTA (Ours)
46.66
43.36
88,07
84.25
69.60
66.80
49,29
44.51
51.14
46.73
39.38
38.64
Mothods
Aircratt
SUN397
DTD
BuroSAl
UCFIO1
ImageNet
Acc.
Mac.
Acc.
Mac.
Mac.
Acc.
Mac.
Acc.
Mac.
Acc.
Mac.
TPT [NcurlPS 22]
10.52
12.03
47.94
44.47
23.71
22.45
7.97
4.95
49.94
43.85
42.80
39.75
C-TPT (CCLR124)
8.65
8.88
46.25
42.94
24.64
22.70
9.36
4.93
47.52
41.54
41
39.30
MTA [CVPR'241
10.44
10.88
45.80
42.23
22.15
20.25
13.28
5.93
47.19
40.43
44.17
41.88
TDA [CVPR'24]
12.53
50.34
41.04
43.01
39.28
DPE [4curIPS224]
14.57
12.07
48.61
43.73
25.74
25.43
14.80
10.58
48.01
42.42
44.38
42.31
O-TPT [CVPR'25) 25)
8.72
45.23
4.67
47.14
42.00
39.35
SCAP 25]
9.60
11.84
45.55
43.07
25.79
23.63
15.68
11.09
47.61
41.75
42.48
40.52
L-TTA (Ours)
16.40
15.60
50.37
45.55
31.51
30.12
17.49
14.18
52.72
46.38
47.26
44.15
Table 21: Detailed Accuracy on Long-tailed Noise Benchmark under the severity ratio of 0.4.
We conduct 5 runs for each experiment under each imbalance setting. All the methods are built upon CIIP-VIT-B/16 backbone. The best results are marked in Bold.
Methods
Average
Caltech
Pets
Cars
Flowers
Fond10)
IPT INeurliSS 221
21.34
18.75
56.30
57.13
31.67
31.35
17.09
15.74
21.22
17.11
6.19
6.85
C-TPT (4C
19.74
17.37
63.95
53.42
29.39
27.34
16.01
14.57
19.38
15.92
5.56
5.47
(CVPR'24)
16.55
14.56
16.88
4.29
TDA (CVER'20 24)
20.44
16.02
62.27
54.15
26.30
20.17
17.03
14.07
16.59
13.25
7.06
6.29
DPE [NcurIPS
20.18
17.66
64.05
59.02
25.45
21.23
16.37
14.32
18.58
16.43
4.05
5.11
0-TPI [CVPR'25]
18.83
16.81
63.12
$3.78
30.00
14.08
18.89
15.43
5.41
SCAP [CVDR225]
19.63
17.05
63.17
$2.79
28. 58
25. 66
17.72
14.91
16.31
11.81
6.05
6.71
L-T1A (Ours)
23,60
20.65
66.36
60.28
30,88
26.76
18.39
15.48
20.76
20.28
7.76
7.15
Methods
Acc.
Mac.
Acc.
Mac.
Acc.
Mae:
Acc.
Mac:
Acc.
UCFIOL
Mae:
Acc.
ImageNee
Mac:
TPT [NNeuliss
1.67
4.65
19.13
17.22
12.01
10.60
3.51
1.75
26.15
21.67
26.01
21.33
C-TPT [ICLR'24
3.97
11.70
20.87
20.67
MTA [CVPR'20 24)
4.52
4.25
14.87
14.31
9.14
7.41
16.65
4.25
23.13
18.51
70
22.25
TDA [CVER'24]
4.36
2.67
16.56
14.53
12.79
8.58
15.18
5.16
21.86
16.15
24.88
20.94
DPE [NcorlPSS20
19.37
10.88
O-TPT [CVPR/25]
3.89
3.84
15.77
14.34
10.92
9.96
3.32
3.89
15.77
15.19
24.34
20.92
SCAP (CVPR-25)
4.38
5.57
17.30
16.70
10.66
10.17
5.08
4.71
20.13
17.99
26.52
20.50
28

---

Published as a conference paper at ICLR 2026
Table 22:
Ablation study the Per-dataset Hyper- Fine Funinu (PHFT).
Method
Caliech
Pets
Cirs
Flowers
FoodTO]
Acc.
Acc.
Acc.
Mac.
Acc.
Acc.
Mao.
w/o PHIT
94,98
92.43
91.50
89.85
69.91
65.02
74.79
68.78
85.27
83.16
L-TTA
95.12
92.46
91.62
90.22
70.33
65.54
74.91
68.99
85.53
83,33
Method
Aircratt
SUN397
DTD
EuroSAT
UCPI0I
Acc.
Mac.
Acc.
Mac.
Acc.
Mac.
Acc.
Mac.
Acc.
Mac.
46.63
L-TTA
27.02
24.14
69.99
64.68
51.63
47,50
57.07
48.27
70.77
63.86
Method
ImageNet
ImageNet-A
ImageNer-R
ImageNet-S
ImageNet-V2
Acc.
Mac.
Acc.
Mac.
Acc.
Mac.
Acc.
Mac.
Acc.
Mac.
w/o PHIFT
7212
63.27
61.56
55.26
82.48
78.09
49.98
43.45
68.60
63.87
I-TTA
71.30
65.83
61,78
55.97
82.86
78.56
50.25
45.99
68.99
64.19
Table 23: Averaged Accuracy on Long-tailed Corruption Benchmark of 16 corruption types: gaussian noise, noise, impulse noise, speckle spiskle_nosse, noise, defocus blur, glass blur, mofion zoom blur, snow, frost, fog, brightness, contrast, ipeg compression, pixelate, saturate, elas- tic transform. We conduct 5 for each experiment under each imbalance setting. All the meth- ods are built upon CLIP- VTT--VIT-/116 backbone. The best results are marked in Bold.
Method
Acc.
Mac.
Acc.
Mac.
Acc.
Mac
Acc.
Mac
Acc.
Mac.
[1CM221]
67.18
57.24
35,53
35,18
38.37
48.43
47.12
TPT (Nc
73.59
68.26
60.40
59.31
37.10
36.47
43.38
39.89
50.03
48.67
C-TPT [[CLR:24]
72.04
67.71
59.48
38.94
35,80
35.25
43,26
39.83
49.13
47.69
TDA [CMPR/24]
73.85
68.36
60.97
58.57
40.29
36.40
46.65
40.73
$1.54
49.31
7ERO [Nau
65 25
55.22
36.34
34.81
42.46
37,70
48.35
46.15
DPE [S2u
73.95
70.00
61.21
59.25
40.56
38.49
46.50
42.24
51.52
49.78
0-TPT (CVPR' 25]
72.02
67.23
58,70
58.32
35.44
34.89
42.98
39.55
48.09
46.46
(CVPR/25]
L-TTA (Clur)
75.08
72.51
64.56
62.99
44.76
41.11
$2.27
47.66
34,23
$2.56
Method
Aureratt
SUN197
DTD
EuroSAT
UCFIOT
Acc.
Mac:
Acc.
Mac
Acc.
Mac.
Acc.
Mac.
Ace.
Mac.
CLJP (ICML:31]
9:33
10.17
39.37
37.08
25.64
21.68
25.47
18.72
41.74
38.00
[NeurlPS'22]
23.39
39.58
C-TPT [CCRR22)
10.54
10.02
38.83
36 10.10
25.90
21.95
23.09
18.01
41,39
37.35
MTA [CVPR 24]
TDA JCVPR'22 244
12.70
10.35
39.42
21,26
25.11
ZERO NNertPS 24]
10.48
9.59
39.44
36.74
25.60
21.32
24.40
19.43
42.98
38.45
DPE [Nc
14.88
12.95
44.17
41.10
29.51
26.16
31,39
26.41
42.00
37.65
0-TPT (CVPR
10.62
10.00
38.26
35.33
25.29
20.91
22.73
17.44
41.06
36.72
SCAP [CVPRR22]
11.19
11.00
41.81
39.19
28.07
23.59
29.86
24.27
45.25
40.54
L-TTA
Table 24: Detailed Accuracy on Long-tailed Corruption Benchmark of shot noise. We conduct 5 runs for each experiment under each imbalance setting. All the methods are built upon CLIP- VIT-B/16 backbone. The best results are marked in Bold.
Flowers
Method
Acc.
Mac.
Agc.
Mac.
Acc.
Mac.
Acc.
Mac.
Acc.
Mac:
(ICML/11
86.94
76.30
55.65
53.79
TPT [NcurPSS22 22]
92.72
89.65
82.21
81.92
60.35
60.45
63.98
$9.06
73.52
72.09
C-IPT HCLK'24
88.28
84.70
83.03
81.90
61.96
60.51
61.10
56.45
76.14
74,88
(CVPR
TDA (CMPR/24]
91.79
87.29
81.47
78.
61.99
56.52
64.19
57.47
70.69
67.92
ZERO [Nc4
90.37
86.76
77.97
76.60
55.34
55,21
57.55
52.75
68.56
65,53
[NourIPS224
81.27
59.18
60.58
68.30
0-TPT (CVPR'22
90.17
96.55 16.54
78,74
78.54
55.45
$5.37
58.17
$4.63
65.24
63.65
SCAP
(CVPR'25]
86.96
82.42
$6.54
$8.53
62,73
1.TTA (Quns)
92.48
89.04
84.10
82.47
62.87
$7.28
70.98
66.21
70.83
68.46
Method
Aureratt
SUNT97
DTD
LuroSAT
UCTTOT
Misc:
Max.
Acc.
Mix::
Acc.
Mic.
CLIP [ICML/1]
12.86
14.64
56.72
$3.85
34.32
30.72
16.00
13.12
56.92
51.61
[NcurtPS'22
15.60
58.01
17.26
57.87
C-TPT [CC 2224
8.03
7.29
54,92
$1.78
38.10
34.37
16.94
14.98
57.58
51.88
MTA [CVPR224
ZERO NeadPPS22 24]
13.56
15.02
56.56
53.03
34,79
29.68
14,02
10.27
56.92
51.05
[NoutPS:24]
19.53
60.57
43.84
40.89
23.33
20.53
14.37
0-TPT (CVPR'2
15.28
14.97
56.06
52.07
35.57
29.02
16.58
10.51
55.27
49.53
SCAP [CVPR'25]
12.15
11.18
60.12
56.04
38.43
31.99
19.51
12.77
62.81
$7.03
L-TTA
29

---

Published as a conference paper at ICLR 2026
Table 25: Detailed Accuracy Long-tailed Corruption Benchmark of impulse noise, We duct 5 runs for each experiment under each imbalance setting, All the methods are built upon CLIP-VIT-B/16 backbone. The best results are marked in Bold.
Method
Calfech
Pet
Cars
Flowers
FoodTO
Mac.
Mac.
Mac.
Mac.
CLIP (ICML11
78.57
71.12
64.38
641.22
31.70
31.16
40.73
36.18
36.22
$6.13
[NcurIPS22
71.82
32.67
31.98
66
44,76
40.87
C-ITT HCLR 24]
83.15
83.77
74.57
72.71
31.85
31.74
47.76
44.98
42.85
40.54
MTA [CVPR
8244
80.28
76,46
70.34
30,70
30.01
41,10
37.09
35.47
30,05
ZERO [NeurPPS224]
79.24
71.14
62.13
62.01
33.04
32.28
41.53
36.24
36.56
36.78
DPE [NcudPS/24]
81.47
79.20
56.63
55,13
33.62
30.70
42.36
41.07
37,88
38.02
0-TPT (CVPR
79.42
72.26
63.10
61.88
32.07
30.84
41.10
36.52
36.10
34,81
SCAP (CVPRR22]
78.47
71.23
56.94
$7.48
30,70
27.84
44,79
38.49
36.28
35.23
I-TTA
86.3
80.76
59.55
35,31
32.13
52.72
48, 93.94
38,86
37.64
Method
Aureraft
SUN397
DTD
EuroSAl
UCF101
Mac.
Mac.
Mac.
Mac.
Mac.
CLIP MUPIIC
7.72
8.9
35,77
34.22
17:47
15.91
3.96
30.67
TPT [NeuriP5'22]
9.28
11.12
42.19
39.79
29.01
25.69
16.18
15.47
43.52
37.04
C-IPT (CCLR224
37.29
MTA JCVPR
7.57
5.96
50.42
46.65
25.98
20.23
18.94
12:47
51.85
42.59
TDA (CMPR/24)
9.27
8.05
11.38
37.45
19.81
13.77
12.66
8.23
43.41
34,93
NewdPS 24]
DPE |NeurPPS22 24]
14.49
14.94
10.47
38.92
17.78
18.91
11.02
5.18
11.70
12.46
О:TPT (CVPR'2
8.18
7.22
35.04
32.06
18.41
14.4]
6.31
4.30
37.58
32,04
[CVPR'25]
10.85
19.16
7.90
42.09
L-TTA (Chrs)
10.90
10.02
50.99
48.78
29.46
26.97
24.88
19.26
48.30
34,36
Table 26: Detailed Accuracy Long-tailed Corruption Benchmark of speckle noise. We duct 5 runs for each experiment under each imbalance setting. All the methods are built upon CLIP-VIT-B/1 backbone. The best results are marked in Bold.
Method
Caltech
Pers
Cars
Flowers
Food
CLIP (ICM//11
89.16
84.61
73.94
71.96
51.38
51.72
48.81
44.67
53.38
51.15
TPT [Neur
89.63
85.33
74,62
72.82
52.80
52.59
47.73
43.67
53.04
50,80
C:TPT ICLK 24
88.38
82.57
72.42
70.28
$3.10
$2.43
44.09
39.84
51.21
50.92
MTA [CVPR
85.53
73.29
58.54
$3.30
49.75
$5.04
ZFRO [Nc4
88.78
84.16
74.21
71.62
$1.03
51.24
49.60
45.34
55.58
$2.73
DPE [NeurtPS244]
89.29
87.05
77.86
77.08
59.69
55.22
58.53
$3.85
57.32
55.87
0-TPT (CVPR'25 25]
73:41
51.79
46.82
$0.30
SCAP (CVPR(25]
87.98
80.85
76.48
75.59
$5.30
$4.21
$4.64
$0.28
59.64
57.70
L-TTA
92.85
88.63
80.97
79.31
61.52
55.77
61.16
$4.77
57.79
56.27
Method
Airemit
SUNWY
DTD
FurnAA
L/C.TO
Acc.
Mic.
Acc.
Maa.
Acc.
Mac.
Acc.
Mac.
Acc.
Mac.
CLIP [CMM:21]
11.15
TPT (3
13.41
14.46
53.59
50.28
28.05
24.60
18.36
13.24
52.69
46.31
C:TPT [CLR
14.64
49.93
24.24
15.74
48.69
MIA [CVPR
TDA [CVPR'24]
14.56
12.55
$7.06
52.55
31.20
24.56
22.04
13.66
61.30
$2.22
NeadPPS 24]
49.76
DPE INeurtPS220 24]
21.26
19.74
61.27
57.90
34,05
33,37
26.86
18.68
60.91
53.58
O-TPT CVPR
11.85
11.52
52.50
48.57
29.80
25.46
18.78
11.25
53.02
46.96
SCAP [CVPR(25]
13.12
12.75
57.14
50.15
30.62
29.85
24.19
12.64
60.95
$2.05
L-TTA (Cuus)
17.50
16.68
61.54
57.17
39.44
37.74
27.42
20.47
60.82
54.97
30

---

Published as a conference paper at ICLR 2026
Table 27: Detailed Accuracy on Long-tailed Corruption Benchmark of defocus blur. We duct 5 runs for each experiment under each imbalance setting, All the methods are built upon CLIP-VIT-B/16 backbone. The best results are marked in Bold.
Method
Calfech
Pet
Cars
Flowers
Food
Mac.
Mac.
Mac.
Mac.
Mac.
CLIP (ICML21
44.96
37.04
19.46
I7.69
3.12
2.33
12.78
9.48
9.58
8.04
[NcurIPS222
37.79
17.13
3.82
2.95
12.78
9.54
10.35
8.14
C-ITT HCLR 24]
41.77
34.51
18.35
18.26
2.43
13.09
9.84
11.21
8.92
MTA [CVPR
ZERO [NeurIPS224]
41.82
32.53
14.77
11.13
2.59
2.51
13.70
10.72
12.03
9.57
DPE [NcudPS/24]
48.89
43.20
17.35
16.97
3.24
2.28
8.36
8.84
11.25
9.40
0-TPT (CVPR
42,70
35.26
18.42
18.32
3.27
2.44
12.97
9.71
11.14
8.72
SCAP (CVPRR22)
40.90
38.33
19.58
19.91
3.99
3.80
10.79
10.98
12.94
12.12
I-TTA
44.18
25.12
25.01
14,65
6.48
24.85
22.09
18.04
17.50
Method
Aureraft
SUN397
DTD
EuroSAl
UCF101
Acc.
Mac.
Mac.
Mac.
Acc.
Mac.
Mac.
CLIP MIPPIC
1.79
0395
8.
5.88
22.95
10,26
11.39
10.37
TPT [NeuriP5
2.49
1.85
8.71
7.31
7.07
6.14
23,83
11.42
13.02
11.58
C-IPT (CCLR224)
5.74
MTA JCVPR
TDA (CMPR/24)
1.44
0.70
11.02
8.27
7.96
5.13
22.24
12.79
14.98
10,20
NewdPS 24]
12.07
DPE NNeurPPS22 24]
3.58
3.47
12.61
11.80
12,95
11.79
29.23
19.24
8.42
7,58
О:TPT (CVPR'25
2.26
0.69
7.79
7.04
4.84
3.85
11.11
6.68
10.00
8.68
[CVPR'25]
2.02
10.15
9.42
12.34
12.36
L-TTA (Chus)
8.26
6.77
12.34
11.52
18,30
14.61
32.00
20.74
23.56
18.16
Table 28: Detailed Accuracy Long-tailed Corruption Benchmark of glass blur. We conduct
5 runs for each experiment under each imbalance setting. All the methods are built upon CLIP- VIT-B/16 backbone. The best results are marked Bold.
Method
Caltech
Pers
Cars
Flowers
Food 101
Mac.
CLIP 1ICM/21
77.77
70.02
59.97
57.35
22.61
21.05
37.06
32.76
37.96
37.38
TPT [NeurPP5222]
79.16
72.51
59,80
57.05
23,08
21.31
37.12
32,82
38,84
38.48
C:TPT ICLK 24
79.07
72.14
61.50
59.05
22.69
21.42
39.27
34.77
37.76
36.35
MTA [CVPR
[CVPR'24]
72.20
23.53
36.96
40.56
ZFRO [No4
78.32
70.68
62.82
60.00
24.65
22.71
37.49
32,66
41.67
40.86
DPE [NeurIPS244]
82.08
74.47
62.18
59.13
25.21
22.11
41.01
39.35
40.33
40.27
0-TPT (CVPR'25 25]
21.36
36.31
SCAP (CVPR(25]
80.22
75.69
62.15
81.13
22.45
21.78
40.25
35.62
39.08
38.08
L-TTA
82,76
78.39
67.61
64.78
31,24
30.26
43.39
38.
43.18
42,22
Method
Airemit
SUNW!
DTD
FuroSA
L/CFT0
Acc.
Mic.
Acc.
Mac.
Acc.
Mac.
Acc.
Mac.
Acc.
Mac.
CLIP [CMM:21]
691
TPT 23
6.87
7.33
36.41
34.02
18.28
14.65
28.54
22.38
37,86
34,32
C:TPT [CLR
29.78
13.66
13.26
13.94
37.20
32.49
MIA [CVPR
TDA [CVPR'24]
5.84
4.92
38.52
34.19
18:41
11.17
30.89
32.22
43.78
36.52
NeadPPS
36.20
DPE (NeurtPS224 24]
10.67
10.20
40.51
39.13
21.06
21.82
35.60
26.93
47.49
42,51
O-TPT CVPR
5.14
4.84
32.70
29.58
17.47
13.56
14.24
14.40
37.14
32,23
SCAP (C)PR(25]
6.20
7.19
38.94
34.62
20.85
15.11
28. 28
42.64
37.50
L-TTA (Cuus)
14.22
12.95
42.11
40.49
23.89
21.42
41.61
32.12
42.01
39.29
31

---

Published as a conference paper at ICLR 2026
Table 29: Detailed Accuracy Long-tailed Corruption Benchmark of motion blur. We conduct 5 runs for each experiment under each imbalance setting. All the methods are built upon CLIP-VIT- B/16 backbone. The best results are marked in Bold.
Method
Calfech
Pet
Cans
Flowers
Food
Mac.
Mac.
Mac.
Mac.
Mac.
CLIP (ICML/1)
44.96
37.04
19.46
17.69
3.12
2.33
12.78
9,48
9.58
8.04
[NcurIPS
37.16
17.91
3.98
2.36
12:58
9.17
10.13
8.52
C-IFT HCLR 24]
41.77
34.51
18.35
18.26
2.43
13.09
9.84
11.21
8.92
MTA [CVPR'24
17.04
ZERO [NeurIPS224]
41.77
30.56
13.90
11.27
4.61
3.60
13,70
10.72
12.03
9.56
DPE [NcudPS/24]
45.28
40.16
17.77
15.11
1.17
20.45
7.98
9.18
10.67
8.80
0-TPT (CVPR
42,70
35.26
18.42
18.32
3.27
2.44
12.97
9.71
11.14
8.72
SCAP (CVPRR25]
44.83
37.17
16.44
15.89
5.02
3.74
14.85
12.22
9.85
8,31
I.TTA
48.64
42.46
25.95
22.78
11,27
10.13
16.66
15.62
18.52
17.36
Method
Aureraft
SUN397
DTD
EuroSAl
UCF101
Acc.
Mac.
Mac.
Mac.
Acc.
Mac.
Mac.
CLIP MUPPIC
1.79
0.95
8:15
5.88
22.95
10,26
11.39
10.37
TPT [NeuriP5'22]
2.19
1.15
11.25
8.94
8.38
7:77
24.25
11.38
11,78
10.53
C-IPT (CCLR224)
5.74
8.69
MTA JCVPR
TDA (CVPR/24)
2.49
1.03
11.11
8.35
8.11
4.37
25.10
13.97
13.34
9.28
NewdPS 24]
DPE NNeurPPS226 24]
3.27
2.78
8.92
9.46
15.44
15:44
28.50
18.60
13.71
11.84
О:TPT (CVPR'25
2.26
0.69
7.79
7.04
4.84
3.85
11.11
6.68
10.00
8.68
[CVPR'25]
1.11
9.83
7,70
12.64
L-TTA (Chus)
5.90
4.60
15.25
13.00
23.82
20.60
30.63
24,80
16.75
13.18
Table 30: Detailed Accuracy Long-tailed Corruption Benchmark of zoom blur. We conduct
5 runs for each experiment under each imbalance setting. All the methods are built upon CLIP- VIT-B/16 backbone. The best results are marked Bold.
Method
Caltech
Pers
Cars
Flowers
Food 101
Mac.
CLIP 1ICM/21
92.97
89.91
84.50
84.36
58.85
59.60
62.57
58.29
78.11
77.24
TPT [NeurPP5222]
93.66
90.49
85.11
84.98
59,21
59.89
63.50
59.21
78.96
78.20
C:TPT ICLK 24)
92.76
89.92
84.02
84.94
$7.49
$8.19
64.04
59.54
77.15
76.09
MTA [CVPR
[CVPR'24]
90.48
86.35
59.01
61.76
81.53
ZFRO [Ncu
86.36
79.71
81.54
79.57
59.20
54.50
63.38
56.50
78.23
76.59
DPE [NeurtPS244]
93.54
90.12
87.37
86.90
66.19
61.74
70.55
63.98
81.05
78.86
0-TPT (CVPR'25 25]
92.80
85.0
58.21
75.79
SCAP [CVPR(25]
94.03
91.36
86.26
86.60
60.91
61.19
69.14
66.77
$1.09
79.29
L-TTA
94.17
90.65
90.35
89.38
68.76
62.55
71.16
68.30
82.58
79,80
Method
Airemit
SUN
DTD
FurnAA
L/CFT0I
Acc.
Micc
Acc.
Mac.
Acc.
Mac.
Acc.
Mac.
Acc.
Mac.
CLIP [IMM:21]
32.79
TPT 23
16.83
18.12
61.86
$9.10
39,53
34.43
40.35
35.39
65.48
61.77
C:TPT [CLL
15.59
54.22
33.09
32.25
31.90
58.09
MIA [CVPR
TDA [CVPR'24]
21.03
18.36
64,42
60.50
40.87
33.26
42.15
43.20
69.51
62,53
NeadPPS
DPE INeurtPS224 24]
22.43
20.37
65.28
61.33
48.36
43.54
45.50
42.52
72.33
63.
O-TPT CVVR
15:98
15.92
58.06
54.17
38.38
33,26
39.91
34.68
63.46
58.12
SCAP [CVPR(25]
19.48
19.83
62.23
$7.68
42.10
34,50
43.60
36.28
65.46
60.64
L-TTA (Cuus)
27.05
25.40
66.44
63.05
50.69
46.08
47.68
44.74
71.87
68.67
32

---

Published as a conference paper at ICLR 2026
Table 31: Detailed Accuracy on Long-tailed Corruption Benchmark of snow. We conduct runs for each experiment under each imbalance setting. All the methods are built upon CLIP-VIT-B/16 backbone. The best results marked in Bold.
Method
Caltech
Pets
Cars
Flowers
Food101
Acc.
Mac.
Acc.
Mac-
Acc.
Mac.
Acc.
Mac.
Acc.
Mac.
CLIP TICE 21
88.99
85.45
77.00
77.05
50.75
50.27
35.29
50.69
72.44
71.13
TPT [NeurIP5223
89.22
$5.10
77.19
77.30
50.61
49.91
55.53
50.89
71.87
70.11
78.81
47.67
51.19
69.79
MTA [CVPR
TDA (CMPR/24)
91,95
88.46
83.34
80.42
57.54
51.94
60.36
52.44
76.58
73.83
NNwu
51.90
49.01
DPE INeurlPS'26 24]
91.58
88. 10
85.55
82.03
58.18
51.99
61.54
53.48
76.40
74.95
0-TPT [CVPR'25]
89.66
86.
79.10
48.02
51.27
69.57
SCAP (C)PR/25]
90.97
84.56
82.96
82. $2.19
49.40
49.76
60.15
56.73
74.38
73.14
L-TTA (Curs)
93.79
88.74
88.19
85.46
62.29
62.97
66.62
$8.61
78.44
75.86
Method
Acc.
Maa.
Acc.
Mac:
Acc.
Macc
Acc.
Mac.
Acc.
Mac.
CLJP [ICMU:21]
11:77
12:91
54,84
33.23
29.
35.63
31.27
58.42
TPT (22
11.79
12.54
53.99
51.51
33.30
29.57
35.24
31.28
57.12
53.17
C-IPT [44)
12:70
13.15
51.84
48.
31,51
26.39
32,20
28.96
57,75
$2,70
TDA [CMPR/24]
15.97
13.60
60.16
55, 80
34.48
28.81
42.82
38.00
67.34
59.52
ZERO [NeuiPS224
11.78
10.22
52.50
08.84
33.10
26.00
38.65
39.13
62.90
36.35
INeurlPS220
16.12
14.08
62,20
56.86
28.42
45.51
41.01
$9.19
O-TPT (CVPR 251
12.78
13.24
51.71
48.03
31.36
26.21
32.42
29.11
57.53
52.61
(CVPR'25)
L-TTA (Clus)
19.58
18.13
55.12
$2.36
36.08
31.38
48.95
44,26
67.09
62.53
Table 32: Detailed Accuracy on Long-tailed Corruption Benchmark of frost. We conduct runs
for each experiment under each imbalance setting. All the methods are built upon CLIP-VIT-B/16 backbene. best respli aae marked Beld.
Method
Cars
Flowers
FoodTDi
Acc.
Mac:
Acc.
Muc.
Ace.
Mac.
Ave.
Mae:
Acc.
Mac.
CLIP [JCM 21)
14.40
10.74
7.99
7.17
1.32
0.76
4.34
2.61
2.63
0.90
TPT [NeurIPS 22]
15.46
12.32
7,88
7,80
0.79
0.22
4.07
2.41
1.68
0.28
[CLR'24
MTA [CVPR:24]
TDA (CVPR
18.58
12.40
6.94
7.00
1.32
1.04
4.27
2.19
2.15
1.04
ZERO [NeurlPS'44
14.81
11.43
2.10
7.57
1.15
1.08
3,18
1.44
2,80
1.59
DPE [NeurIPS224]
14,82
11.43
7.57
7.08
1.35
0.84
3.18
1.45
2.66
1.01
[CVPR'25
SCAP (CVPR
16.54
11.24
8.95
8.81
2.55
1.67
5.21
3.19
2.21
1.79
1-TTA
25.67
20.23
12.22
11.68
8.78
8.29
16.12
15.13
5.89
5.50
Method
Aireraft
SUND997
DTD
EuroS
LCPTOT
Acc.
Mac:
Acc.
Mac.
Acc.
Mac.
Acc.
Mae.
Acc.
Mac.
CLIP [JCL 211
1.88
0.83
2.60
1.85
11.54
6.70
3.25
4.23
TPT [NeurIPS:22]
1.79
0.90
3.34
2.38
9.29
4,87
10.21
4,89
4.69
4
C-TPT [CCLR221
2.35
MTA [CMPRR24]
TDA [CVPR'24
2,41
1.34
3.05
1.99
14.98
7.69
6.67
4.10
6.51
4,00
ZERO [NeorIPS224
2.16
3,05
1.43
7:25
3,08
4.41
DPE [Nc
3.34
2.69
3,03
2.44
8.73
7.01
5.95
4,80
7.00
5.82
0-TPT [CVPR'25]
SCAP (CVPR
2.20
2.15
2.84
2.12
10.51
6.39
5.20
4.96
7.75
4.70
L-T
8.96
3,63
2.74
20.23
18.71
19.50
15.49
4.04
33

---

Published as a conference paper at ICLR 2026
Table 33: Detailed Accuracy Long-tailed Corruption Benchmark fog. We conduct 5 runs for each experiment under each imbalance setting. All the methods are built upon CLIP-VIT-B/16 backbone. The best results marked in Bold.
Method
Caliech
Pets
Cars
Flowers
Foodd ID1
Mac.
Acc.
Mac
Mac.
CLIP [ICME/21]
20.39
13.55
5.35
3.87
0.55
0.15
5.14
3.65
2.26
1.24
TPT [NcurIPS/22]
19.59
13.09
6.49
5.43
0.88
0.16
4,96
3.48
3.60
2.15
C-IPT [ICLR'2
18.29
12.88
4.66
3.16
0.55
0.11
2.87
3.31
1.86
MTA [CVPR 24]
24]
19:84
ZERO [N4r
23.65
16.61
6.32
5.18
0.58
0.24
4.71
2.77
1.84
1.04
DPF [NeurPPS244]
19.28
18.10
4.16
3.50
0.64
0.14
2.36
2.30
2.87
2.30
0:TPT (CVPR
18.92
13.30
4.66
3.31
0.57
0.18
4.83
3.01
3.37
SCAP (CVPR'25]
18.43
16.73
4.99
4.17
1,56
1.37
4.34
1.03
3,00
1,08
(Chrs))
Method
Aireraff
SUN397
DDTD)
EaroSAT
UCFTO1
CLIP [C
0.31
0.13
1.99
1.22
1.87
1.67
9.93
3.96
1.15
0.88
TPT [New
L.05
1.63
3.48
2.53
2.84
2.63
3.68
2.40
1.72
TTTTT [ICLK22]
0.70
1.74
1.29
1.40
1.18
13:47
4.03
1.59
1.27
MTA [CVPR'24]
[CVPR 24]
0.78
2.63
1,36
4,84
2.61
6.85
2.33
ZERO NeurPPS 24]
0.49
0.39
3,00
1.32
4.44
2.15
9.94
5.06
1.12
0.63
[NcorlPS'24
1.56
4.28
0-TPT (CVPR 25
1.09
0.22
1.77
1.27
1.40
0.99
12.30
4.22
1.54
1.13
SCAP CVPR 25]
1.58
0.60
1.88
1.68
4.34
2.84
14.08
8.09
1.55
1.40
(Ouns)
8.87
7.22
18.66
15.32
13.78
3.11
Table 34: Detailed Accuracy on Long-tailed Corruption Benchmark brightness. We conduct
5 runs for each experiment under each imbalance setting. All the methods are built upon CLIP- VIT-B/16 backbone. The best results are marked Bold
Method
Caltech
Pers
Cars
Flowers
Food 101
Acc.
Mac.
Acc.
Mac-
Acc.
Mac.
Acc.
Mac.
Acc.
Mac.
CLIP (CCM
91.04
88.
82.90
82.60
$7.91
$8.49
60.73
57.73
79.32
78.15
[NcurPS522]
C-IPT [CCR
90.87
87.74
82.90
83.31
58.33
58.78
62.57
59.46
78.80
77.51
MTA [CVPR
86.13
84.94
78.33
80.08
65.19
56.37
61.77
$8.89
76.33
73.52
TDA JCVPR 24]
89.87
85,70
83,50
60.49
59.70
79.72
7FRO
[N4
90.49
87.16
82,27
81.98
58.65
86
61.77
$7.54
74,28
70,82
[NcurtPS244]
0-TPT (CVP
90.41
87.31
83.04
83.52
58.26
$8.73
62.08
14
78.76
77.41
SCAP (CVPR'25]
91.25
88.63
85.69
85,16
59.67
57.76
63.69
60.46
79.82
78.07
L-TTA (Curs)
60.81
Method
Aireratt
SUNI97
DTD
EuroSAl
UCF101
Mac.
Mac:
Mac.
CI (3MM)31)
13.82
17.00
39.27
36.36
40.87
33.71
$7.92
28.61
59.78
55.00
TPT [NeurlPS 23]
18.26
19.48
61.53
59.07
43.54
36.10
42.04
33.15
62.51
58.27
C-TPT [CL 244
35.91
MTA [CVPR224
15.42
19.42
61.18
58.13
35.77
29.19
38.58
31.67
$8.92
$4.65
[CVPR'24)
16.72
60.45
31,80
30.90
$3.99
ZERO NewrPSS 24]
17.16
11.59
58.29
52.10
40.90
33.86
38.91
30.95
60.03
52.80
DPE [NeatPS:22 24)
22.61
18.15
65.06
58.43
37.09
31.25
40.92
33.08
64.46
57.45
SCAP [CVPR'25]
18.12
18.86
62.39
$9.36
46.00
43.24
61.82
55.79
65.77
61.52
L-TTA (Ours)
24.07
22.99
67.15
63.26
47.74
40.50
67.34
62.04
67.33
62,04
34

---

Published as a conference paper at ICLR 2026
Table 35: Detailed Accuracy Long-tailed Corruption Benchmark of contrast. We conduct runs for each experiment under each imbalance setting. All the methods are built upon CLIP-VIT- B/16 backbone. The best results are marked in Bold.
Method
Caltech
Pets
Cars
Flowers
Food 101
Mac.
Mac.
Mac.
Mac.
Mac.
CLIP (ICML
92.14
89.92
84.16
$4.56
59,14
59.52
0.1.88
00.15
79.37
78.52
[NcurIPS'2
95.33
92.39
86,66
62.92
62.83
61.91
82.00
81.40
C-IFT HCLR 24]
92.72
90.13
83.25
83.94
59.36
59.62
65.20
61.91
78.56
77.79
MTA [CVPR
87.71
86.33
80.15
78.29
60.04
58.24
63.88
59.91
72.86
70.31
ZERO [NeurIPS224]
90.34
87.69
83.80
80.92
60.84
$7.57
62.76
56.36
76.20
74.08
DPE [NcudPS/24]
94.47
92.30
86.71
85,37
67.97
62.19
72.15
64,80
83.60
$2.47
(CVPR'22 25]
SCAP (C)PR:25]
92.39
90.20
85,38
83.94
62.94
62.53
65.05
60.07
82.16
78.35
1.TTA
95.90
93.29
88,75
87.65
64,25
75.50
66.37
85,
82.87
Method
Aireratt
SUN399
DTD
EuroSAT
UCTTO0
Acc.
Mac.
Mac.
Mac.
Acc.
Mac.
Mac.
CLIP [ICML 21]
18.55
18.24
57.83
43.21
36.75
41,78
31.87
56.71
IPT [NcurIPS 221
19.50
19.53
64.55
61.23
44.39
38.43
43.71
34.05
64.18
59.95
[[CLR'24)
18.09
56.66
32.64
MTA JCVPK
20.71
17.50
62.03
60.86
36.12
30.38
38.42
33.91
58.78
52.04
TDA (CMPR/24)
22.04
17.80
66.79
63.05
44.15
34,76
46.45
45.78
67.44
59.77
NewdPS 24]
28.56
DPE [NeurPPS 24]
25.45
21.10
67.04
61.18
47.74
41.94
48.22
46.43
68.55
60.25
О:TPT (CVPR'25
18.55
18.19
60.53
56,34
41.97
34.04
39,90
32,88
61.98
36.78
[CVPR'25]
20.29
59.37
37.36
32.04
$9.71
L-TTA (Chrs)
28.76
24.30
67.78
65.84
48.25
42.43
63.84
60.96
70.21
63.95
Table 36: Detailed Accuracy Long-tailed Corruption Benchmark of jpeg compression. We conduct runs for each experiment under each imbalance setting. All the methods are built upon CTT-//IT-B/16 backbone. The best results are marked in Bold.
Method
Caltech
Pers
Cars
Flowers
Food 101
Mac.
Mac.
Mac.
Mac.
Mac.
(ICM//2
90.21
86.50
72.69
74.16
55.65
53.72
39.69
54.92
68.33
67.05
TPT [N2
91.07
87.13
74.70
75.94
59.97
57.85
62.99
58.66
72.91
71.54
C-TPT [[CL 24)
91.04
87.23
75.12
76.88
53,25
51.74
57.24
66.26
MTA [CVPR
89.20
96.46
68.46
62.90
$2.99
50.40
$6.37
$4.41
66.51
64.79
88.39
56.91
7FRO [N44
80.74
86.37
82.68
76.13
$7.87
$3.47
60.39
$5.70
66.50
$8.42
DPE [NeurtPS244]
92.99
88.04
84.56
80.77
63.35
58.46
68.01
59.72
74,28
70.31
0-TPT (CVPR'25
91.04
75.47
52.12
56.98
SCAP (CVPR(25]
91.95
88.23
78.72
80.74
$4.04
$4.67
64.06
61.25
70.99
70.49
L-TTA
92.70
89.85
84.38
81.96
66.21
60.40
71.28
64,83
77.88
76.48
Method
Airemit
SUNW!
DTD
FumnAA
L/C.T01
Acc.
Mic.
Acc.
Maa:
Acc.
Mac.
Acc.
Mac.
Acc.
Mac.
[ICM:21]
51.57
$6.57
TPT [NeurIPS 22]
18.39
17.55
56.86
54.00
41.83
35.73
29.45
27.87
62.83
57.89
C:TPT [CLR
15.05
49.46
31.61
58.74
53.63
MIA [CVPR
12.30
11.81
51.12
50.23
39.22
26.95
23.62
20.40
53.26
50.91
TDA [CVPR'24]
18.93
14.23
60.94
55.91
42.90
36.01
32,09
32.00
67.92
60.17
NeadPSS 24]
DPE (NeurtPS220 24]
20.68
14.98
61.25
56.04
44.82
38.40
34.87
34.14
68.26
61.98
O-TPT CVVR
16.13
14.84
53.28
49.30
36.66
30.86
26.24
18.20
58.79
53.66
[CNPR'25]
15.01
L-TTA (Cuus)
24.86
20.38
62.53
58.97
47.55
42,80
39.57
36.04
70.28
64.50
35

---

Published as a conference paper at ICLR 2026
Table 37: Detailed Accuracy Long-tailed Corruption Benchmark of pixelate. We conduct runs for each experiment under each imbalance setting. All the methods are built upon CLIP-VIT- B/16 backbone. The best results are marked in Bold.
Method
Calfech
Pet
Cans
Flowers
Food
CLIP (ICM/
56,05
46.02
34,89
30.86
3.78
2.73
22.75
20.66
16.88
14,83
[NcurIPS
55.70
31.41
3.48
2.07
22.89
20.96
16.04
C-TPT [CC 242
56,30
47.61
36.62
31.40
3.91
2.65
24.71
23.44
16.82
14,88
MTA [CVPR
46.05
32.08
3.77
22.16
ZERO [NeurIPS224]
52.10
40.14
29.23
26.16
4.20
4.20
19.74
16.13
17.96
15,50
DPE [NcudPS/24]
58.29
46.25
38.74
34.87
6.00
5.34
28.15
25.62
22.31
20.05
0-TPT (CVPR
47.55
36.62
31.54
2.68
23.46
16.82
14.82
SCAP (CVPRR25]
58,00
48.56
40.05
35.86
3.82
3.46
26.68
24.23
21.47
20.53
I-TTA
60.71
52.24
40.84
38.90
12,98
10.61
28.60
26.20
25.30
22.08
Method
Aireraft
SUN397
DTD
EuroSAl
UCFT01
Acc.
Mac.
Avc.
Mac.
Mac.
Acc.
Mac.
Mac.
CLIP (IPPCC
2.35
16.67
14,40
7.80
4.26
12:41
8.36
21.70
17:47
TPT [NeuriP5'22]
3.15
3.22
15,60
13.48
7.85
4.35
12.03
7.57
23,16
18.81
C-IPT (CCLR224
2:32
13:47
MTA JCVPR
TDA (CVPR/24)
3,97
3.66
18.65
15.79
7.64
3.91
22.46
18.36
26.42
19.95
NewdPS 24]
DPE INeurPP'22 24]
4.96
4.54
20.75
18.14
9.00
5.50
21.86
17.49
28.89
21.82
О:TPT (CVPR'25
3,20
2.34
15.59
13.42
8.74
5.39
14.43
10.09
20.88
15.94
[CVPR'25]
4.82
15.46
7.18
22.32
15.78
L-TTA (Curs)
11.85
9.61
26.84
21.41
16.62
11.36
33.47
26.29
32.00
25.65
Table 38: Detailed Accuracy Long-tailed Corruption Benchmark of saturate. We conduct runs for each experiment under each imbalance setting. All the methods are built upon CLIP-VIT. B/16 backbone. The best results are marked in Bold.
Method
Caltech
Pers
Cars
Flowers
Food
CLIP CICM/12
92.88
90.40
85.27
85.97
61.34
61.35
63.79
60.70
80.97
$0.17
TPT (N2
93.52
90.85
87.09
87.70
62.54
62.65
64.42
60.92
81.15
80.40
C-TPT [[CL 24)
90.73
86.17
61.56
61.37
61.03
80.00
79.08
MTA [CVPR
[CVPR/24]
91.15
82.54
7FRO [Nou
90.85
88.77
83.20
80.57
61.70
56.49
63.59
$4.20
80.00
79.74
DPE [NeurtPS244]
94.75
91.43
90.81
88.
68.90
63.81
72.06
63.37
86.38
85,37
0-TPT (CVPR'25
90.67
60.74
78,
SCAP (CVPR(25]
94.88
91.53
86.85
84.65
66.00
63.33
68.11
64.13
80.91
79.
L-TTA
95.29
92.45
91.21
89.93
70.09
65.04
73.39
66.79
88.27
87.13
Method
Airemit
SUN 397
DTD
FurnSA
L/CPT01
Acc.
Micc
Acc.
Maa.
Acc.
Mac.
Acc.
Mac.
Acc.
Mac.
CLIP [IMM:21]
TPT [N2
20.17
19.12
62.99
60.33
42.23
35.32
43.88
35.40
64.20
60.11
C:TPT [CLR
19.50
56.77
35.84
33.09
58.03
MIA [CVPR
TDA [CVPR'24]
23.36
18.39
65,70
62.02
43.21
34,84
38.30
37.56
72.00
66.05
NeadPSS
62.83
DPE INeurtPS220 24]
23.73
18.94
67.68
63.54
45.38
36.50
41.50
37.75
71.62
65, 63.96
O-TPT CVVR
19.95
19.26
60.81
56.84
42.75
36.42
40.58
33,39
64.67
58.32
[CNPR'25]
19.51
L-TTA (Curs)
30.42
28.62
68.52
67.36
48.90
37.97
46.44
43.49
74.52
68.36
36

---

Published as a conference paper at ICLR 2026
Table 39: Detailed Accuracy on Long-tailed Corruption Benchmark of elastic transform. We conduct runs for each experiment under each imbalance setting. All the methods are built upon CLIP-VIT-B/16 backbone. The best results are marked in Bold.
Method
Calrech
Pets
Cans
Flowers
Food101
Acc.
Mac
Mac
Acc.
Mac
Acc.
Mac
Acc.
Mac.
CLIP (ICME 21
92.88
90.40
85.27
85.97
61.34
61.35
63.79
60.70
80.97
80.17
TFT [NcuttPS 221
93.52
90.85
87.09
$7.70
62.54
62.65
64.,22
60.92
81.15
80,40
[CC
90.73
61.37
79.08
MTA JCVPR'20
(CMPR'24)
94.15
91.15
88,02
86.68
67.61
62.87
70.19
62.79
84,90
82.54
ZERO NNwutPS 24]
90.85
88.77
80.57
61.70
56,49
$4,20
79.74
DPE [NoafPS:244
94.75
91 43
90.81
38, 16
68.90
63.81
72.06
63.37
86.38
85.37
(CVPR'25
90.67
86.27
60.74
SCAP (C)
94.88
91 .3
86.85
84 65
66.00
63.33
68.11
64.13
80.91
79.20
L-TTA (Ouns)
95.29
92.45
91.21
89.93
70.09
65.04
73.39
66.79
88.27
87.13
Method
Aireraft
EuroSAT
Acc.
Mac
Acc.
Mac
Acc.
Mac
Acc.
Mac
Acc.
Mac.
35.23
42.22
60.82
TPT [NcurIPS/22]
20.17
19.12
62.99
60.33
42,23
35.32
43.88
35.40
64.20
60.11
CTTPT (CLK*22)
20.58
19.50
60.88
56.77
42.43
35.84
40.07
33,09
64,51
58,03
TDA (CMPR/24]
23.36
18.39
65,70
62.02
43.21
34.84
38.30
37.56
72,00
66.05
ZERO [Nau1PS524]
18.97
59.46
36.75
27.51
62.83
DPE (NeurPPS244
23.73
18.94
67.68
63.54
45.38
36.50
41.50
37.75
71.62
65.96
0-TPT [CVPR:25]
19.95
19.26
60.81
56.84
42.75
36.42
40.58
33,39
64.67
$8,32
(CVPR(25]
66.20
L-TTA (Ouns)
30.42
28.62
68.52
67.36
48.90
37.97
46.44
43.49
74.52
68.36
Table 40: Detailed Accuracy on 10 datasets of TPT. The best results are marked in Bold.
Method
Caliech
Pets
Cars
Flowers
Food10]
VIT/L-14
93.02
91.56
88.19
87.89 87.24
73.99
74.32
76.45
73.93
87.69
87.32
VIT-II
88.47
74.08
74.37
76.72
72.58
88.87
87.48
SIGLIP
93.51
92.07
90.28
88.66
76.62
70.05
76.55
72.80
87.79
87.19
META-Big
Method
Aircraft
SUN397
DTD
EuroSAT
UCFI01
VIT/L-14
21.75
23.10
66.64
63.41
$1.17
44,88
39.22
45.09
73.46
69.34
VIT-H
22.13
25.18
64.51
63.64
53.23
45.49
40.60
44.94
74.00
71.73
SIGLIP
22.94
20.29
64.97
62.18
53.37
45.28
40.78
46.91
74.09
70.15
META-Big
Table 41: Detailed Accuracy on datasets of TDA. The best results are marked in Bold.
Method
Callech
Pets
Cars
Flowers
Food101
VIT/L-14
92.89
91.96
89.64
89.33
76.85
72.61
76.86
73.01
87.92
86.18
VIT-H
94.23
92.72
91.17
89.49
67.5
78.56
74.64
88.66
83,92
SIGLIP
92.72
91.78
90.69
89.85
88.70
86.71
82.68
80.51
87.31
85.91
META-Big
92.84
92.14
90.76
90.11
86.65
84,13
81.14
76.56
86.32
84.76
Method
Aireraft
SUN397
DTD
EuroSAT
UCFIOT
VIT/I.-14
27.53
23.94
67.41
63.17
49.42
44.43
52.93
54.69
76.09
70.22
VIT-II
33.61
27.52
66.43
62.40
$4.77
50.54
52.14
53.25
78.84
73.26
SIGLIP
42.12
39.51
67.34
64.10
59.59
55.17
35.56
37.22
77.31
73.84
MRTA-Big -Big
44.53
40.08
70.59
55.88
60.05
55.88
63.67
61.14
81.02
77.13
Table 42: Detailed Accuracy on 10 datasets of DPE. The best results are marked in Bold.
Method
Caltech
Pets
Cars
Flowers
Food10]
VIT/L-14
92.51
91.33
89,80
89.05
77.16
73.29
77.29
74.17
85.81
84.75
VIT-H
94.72
93.18
91.82
89.27
83.64
69.00
79.03
85.28
87.06
83.05
SIGLIP
92.83
92.27
91.21
90.42
87.35
85.01
83.05
81.13
88.30
85.77
META-Big
92.88
92.10
90.72
90.30
87.40
85.42
83.46
81.07
88.70
87.58
Method
Aircraft
SUN397
DTD
EuroSAT
UCF101
VIT/L-14
22.14
20.34
72.28
67.92
60.90
$1.77
56.41
47.84
75.95
70.88
VIT-H
34.05
29.44
70.29
70.38
58.94
$1.62
53.36
47.17
77.06
72.31
SIGLIP
42.23
28.48
72.36
68.35
61.62
55.92
56.69
50.78
79.34
74.15
META-Big
44.04
42.08
73.63
59.51
61.93
55.54
63.77
62.02
80.86
77.57
37

---

Published as a conference paper at ICLR 2026
Table 43: Detailed Accuracy on 10 datasets of SCAP. The best results are marked in Bold.
Method
Caltech
Pets
Cars
Flowers
Food10]
VIT/L-14
92.91
90.87
90.85
89.63
80.62
78.17
78.60
75.05
89.15
87.63
VIT-H
93.02
91.86
92.10
90.22
81.88
80.32
79.21
77.15
88. 13
86.72
SIGLIP
92.74
91.21
92.58
90.10
81.95
80.47
81.70
78.52
90.46
88.35
META-Big
Method
Aircraft
SUN397
DTD
EuroS AT
UCFI01
VIT/L-14
16.76
24.31
74.92
68.62
53.91
46,59
46.63
49.66
72.05
66.83
VIT-H
26.98
76.56
70.09
53.25
45.07
45.24
78
72.84
SIGLIP
63.85
45.50
48.44
40.52
70.78
META-Big
Table 44: Detailed Accuracy on 10 datasets of L-TTA.
The best results are marked in Bold.
Method
Caltech
Pets
Cars
Flowers
Food10]
VIT/L-14
93,43
92.67
90.09
89.46
78.29
77.64
81.85
78.93
88.57
87.67
VIT-H
94.62
93.46
91.30
88.20
83.74
82.09
80.77
77.66
88.94
88.41
SIGLIP
94.51
93.62
93.18
90.93
85.26
86.00
82.70
78.81
89.39
87.62
META-Big
94.04
93.20
93.00
90.55
88.30
87.72
85.46
81.90
90.40
89.42
Method
Aircraft
SUN397
DTD
EuroSAT
UCF101
VIT/L-14
28,51
27.66
73,82
61.60
54.26
57.93
51.40
76.61
71.94
VIT-H
35.69
32.33
73.18
71.54
61.66
54.42
57.48
52.54
79.36
73.02
SIGLIP
46,93
42.43
74.69
61.61
62.53
60.42
58.34
52.21
79.92
74.07
META-BI -Big
44.49
42.80
73.74
56.43
62.49
59.46
65.81
64.40
81.37
78.73
Table 45: Results on Balanced Datasets.. The best results are in Bold.
Caltech
Pets
Cars
Flowers
Food101
Acc.
Mac.
Acc.
Mac.
Acc.
Mac.
Acc.
Mac.
Acc.
Mac.
CLIP
94.28
92.26
88. 14
86.80
65.30
63.5
67.28
62.39
83.8
83.72
TPT
94.9
92.13
88.57
87.04
66.49
64.86
69.32
64.65
84.81
84.74
C-TPT
94.27
91.52
87.76
86.63
65.77
63.84
68.09
65.07
82.6
82.5
MTA
94.3
91.94
87.26
86.54
65.29
64.68
66.27
62.8
85.38
85.1
TDA
95
92.08
89.42
89.03
68.98
67.25
72.38
65.91
86.18
86.07
ZERO
94.02
91.33
87.24
85.91
65.75
63.68
66.58
60.53
85.26
85.12
RLCF
95.08
92.48
88.98
86.23
66.46
60.41
85.1
84.92
DPE
95.05
92.1
91.76
87.45
67.14
65.49
74.67
68.85
86.42
86.4
WATT
94.91
92.19
87.33
86.01
66.04
64.5
71.28
66.93
64.32
85.86
CLIPAITT
92.56
89.84
87.27
85.95
65.12
63.43
66.42
61.61
82
81.78
О-TPT
94.18
91.48
87.95
86.83
64.63
63.64
69.8
82.41
82.32
SCAP
88.73
86.35
65.79
86.05
83.29
Hohenstaufen
95.46
93.62
92.29
90.3
70.49
67.43
75,91
88.34
87.33
Aireraft
SUN397
DTDD
FuroSAT
UCFTO1
Method
Acc.
Мас.
Acc.
Mac.
Acc.
Mac.
Acc.
Mac.
Acc.
Mac.
CLIP
23.88
20.73
62.58
61.62
44,56
40.69
41.33
36.03
67.97
64,9
TPT
25.45
64.97
63.98
44.72
41.2
35.1
68.37
65.48
C-TPT
22.89
64.58
62.69
44.85
41.81
37.68
35.12
66.76
62.59
MTA
24.24
22.71
63.06
61.37
44.48
41.25
44.52
40.06
68.01
64.05
TDA
25.32
22
67.61
66.14
45.86
41.31
60.2
60.11
70.68
65.18
ZERO
24.72
21.65
63.02
61.7
44.86
40.87
38.28
32.06
65.23
61.65
RLCF
23.22
69.58
65.34
45.02
40.79
60.26
DPE
29.22
26.89
69.01
68.88
48.97
44.04
56.73
50.25
70.55
64.71
WATT
24.13
22.21
66.14
64.82
46.56
42.64
44.9
38.43
62.92
59.56
CLIPATTT
23.13
20.21
65.74
62.58
46.4
42.18
42.29
35.03
61.59
58.27
0-TPT
23.22
19.24
64.53
60.55
46.74
43.42
38.53
35.76
66.68
62.57
SCAP
23.46
65.71
63.31
45.61
$2.57
71.88
66.62
Hohenstaufen
27.22
28.06
72.75
70.82
50.87
45.5
58.81
48.27
71.85
68.28
38

---

Published as a conference paper at ICLR 2026
Table 46: Introductions of datasets.
Datasets
Training / Validation / Testing
Types
Classes
Short introduction
10
4128
2465
Ohjects
object categories background)
Pets
2944 / 736 3669
Fg.. pets
37
37 cat / dog species
Cars
6509 1635 / 8044
Fg. cars
195
Car from different angles
Flowers
4093 2463
Fg. flowers
102
flower categories
Food101
50500 20200 30300
Fg. food
101
101 food dishes
SUN397
15880 / 19850
Scenes
397
397 scene types
DTD
2820 1128 / 1692
Texture
47
47 visual textures
EuroSAT
13500 / 5400 8100
Satellie Img.
10
Satellite images across 10 land use classes
UCFIDL
7639 /783
Actions
101
human actions
Aiscraft
3334 3333 / /333
Fg. aircraft
100
100 aircraft models
ImageNet
1,28M / 50000
Objects
1000
Images across 1,000 classes
ImageNet-A
/-/7500
200
Sub ImageNNt with natural adversarial
IeageNet-R
-/- 30000
multi-domain
200
Sub- ImageNet with artistic renditions
ImageNet-S
-/-558899 50889
Sketches
1000
Sub ImageNet with shape cues only
ImageNat-V
- 10000
Collecation
1000
Sub ImageNet with viewpoint variations
39


Reviews:
[{"review_id": "C1kvVCyshf", "reviewer": "Reviewer_hHnt", "summary": "This paper proposes L-TTA (Long-Tailed Test-Time Adaptation), a method for adapting vision-language models (VLMs) to long-tailed, unlabeled test distributions. The framework integrates three components—Synergistic Prototypes (SyPs), Rebalancing Shortcuts (RSs), and Balanced Entropy Minimization (BEM)—to mitigate head-class dominance during TTA. Experiments on 15 datasets show consistent gains over recent TTA baselines such as DPE and SCAP under class-imbalanced setups.", "questions": "1. How is the long-tailed TTA setting formally different from non-i.i.d. TTA? Could the authors provide a side-by-side comparison of assumptions and experimental protocols?\n2. How does BEM differ empirically from previous class-weighted EM variants such as in DELTA [e]?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The paper is nicely organized, with convincing visuals and comprehensive benchmarks.\n2. Evaluation across multiple datasets and metrics demonstrates the robustness of L-TTA.\n3. The ablation study on components supports the effectiveness of SyPs, RSs, and BEM.", "weaknesses": "1. The “long-tailed TTA” setting largely overlaps with non-i.i.d. TTA explored in prior works such as LAME [a], NOTE [b], SAR [c], DA-TTA[d], and DELTA [e]. The challenges analyzed (imbalance over time, EM bias, boundary collapse) are identical to those already studied in non-i.i.d. test-stream TTA. Therefore, I doubt the claim that this is the “first study of TTA under long-tailed scenarios.” The authors may need to demonstrate the conceptual or procedural difference more clearly.\n2. Because of the overlap mentioned above, the proposed method also appears similar to existing ones. BEM modifies EM by adding a penalty that down-weights already-confident classes to reduce head-class over-optimization. This seems very close to class reweighting in non-i.i.d. TTA (e.g., DELTA), which injects class-wise weights to counter imbalance during EM. As written, Propositions 1–2 provide intuition but not a decisive distinction from prior class-aware EM approaches, which makes this component appear incremental rather than novel.\n3. Section 2 lacks explicit discussion of non-i.i.d. TTA. Adding a subsection contrasting L-TTA with these prior studies is essential for proper positioning.\n4. Table 4 omits training-free TTA methods for VLMs (e.g., TDA, ZERO [f]), which are critical for comparisons in speed/accuracy trade-offs.\n\n[a] Parameter-free online test-time adaptation. CVPR 2022.\\\n[b] Note: Robust continual test-time adaptation against temporal correlation. NeurIPS 2022.\\\n[c] Towards stable test-time adaptation in dynamic wild world. ICLR 2023.\\\n[d] Distribution alignment for fully test-time adaptation with dynamic online data streams. ECCV2024.\\\n[e] Delta: Degradation-free fully test-time adaptation. ICLR2023. \\\n[f] Frustratingly Easy Test-Time Adaptation of Vision-Language Models. NeurIPS2024.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "dtUnuIS8cd", "reviewer": "Reviewer_42WD", "summary": "This paper presents the Long-Tailed Test-Time Adaptation (L-TTA) framework, which addresses the performance degradation of vision-language models in test-time adaptation scenarios where the test distribution is long-tailed. Standard TTA methods typically amplify bias toward dominant classes, thereby harming underrepresented ones.\nTo solve this, they propose a complex, three-part system:   \n\n- Synergistic Prototypes (SyPs): A dual-prototype memory system to separately accumulate confident (Deterministic) and improbable (Exclusionary) features, designed to enrich tail class representations.   \n- Rebalancing Shortcuts (RSs): Learnable cross-attention adapters to dynamically balance the prototypes, optimized with a novel Class Re-Allocation loss.   \n- Balanced Entropy Minimization (BEM): A new loss function that gates the influence of class priors using prediction confidence, specifically to protect tail classes from the over-optimization of head classes.   \n\nExtensive experiments demonstrate that L-TTA achieves strong generalization and outperforms existing methods under a wide range of long-tailed test settings.", "questions": "1. Does the model still outperform baselines when using a consistent set of hyperparameters across datasets? Demonstrating this would help confirm that the performance gains are not dependent on per-dataset tuning. A positive result here would increase my evaluation score.\n2. How does the model perform on balanced datasets? It would be helpful to see whether the proposed design adversely affects performance when the class distribution is uniform.\n3. Can the authors report performance on additional corruption types (e.g., blur, snow, JPEG compression)? I recognize the rebuttal timeline is limited, but even small-scale experiments would strengthen the evidence of robustness across diverse corruption scenarios.\n\nFinally, I remain open to adjustments or clarifications addressing the weaknesses raised in weaknesses section.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1. First work to formally study long-tailed test-time adaptation for VLMs, addressing a realistic yet overlooked deployment scenario.\n2. Introduces a coherent three-part novel system (SyPs, RSs, BEM) with each component targeting a specific weakness of standard TTA under imbalance.\n3. Outperforms 7 baselines across 15 datasets in accuracy and macro-F1, under different imbalance ratios. Gains are consistent on OOD, cross-domain, and noise benchmarks.\n4. Achieves competitive adaptation speed without full-model optimization, making the approach practical for real-world use.\n5. Provides thorough ablations and sensitivity studies showing the contribution of each component.", "weaknesses": "1. The method requires tuning several per-dataset hyperparameters, which contradicts with the core objective of test-time adaptation, where models are expected to generalize without dataset-specific tuning.\n2. The Balanced Entropy Minimization (BEM) loss depends on access to class priors ($\\pi$), which are not typically available in reality, making this assumption impractical.\n3. This paper primarily emphasizes entropy-minimization, prompt-tuning, and prototype-based approaches, but it omits discussion of important emerging TTA families, such as vision-encoder adaptation methods (e.g., CLIP-ArTT[1], WATT[2]), parameter-free approaches (e.g., ZERO[3]), and reinforcement-based TTA frameworks (e.g., RLCF[4]).\n4. The notation and exposition can be difficult to follow, and parts of the method are challenging to comprehend. Clarity and narrative coherence could be improved to enhance readability.\n\n**Refs: \n\n[1] Hakim, Gustavo A. Vargas, et al. \"Clipartt: Adaptation of clip to new domains at test time.\" WACV'25.\n\n[2] Osowiechi, David, et al. \"WATT: Weight average test time adaptation of CLIP.\" NeurIPS'24.\n\n[3] Farina, Matteo, et al. \"Frustratingly easy test-time adaptation of vision-language models.\" NeurIPS'24.\n\n[4] Zhao, Shuai, et al. \"Test-Time Adaptation with CLIP Reward for Zero-Shot Generalization in Vision-Language Models.\" ICLR'24", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "oS1NHcMGE3", "reviewer": "Reviewer_QBXH", "summary": "The paper introduces Long-Tailed Test-Time Adaptation (L-TTA), a new paradigm that explicitly addresses class imbalance during test-time adaptation of vision-language models. L-TTA focuses on real-world settings where data follow long-tailed distributions, and it consists of three key components: Synergistic Prototypes, Rebalancing Shortcuts, and Balanced Entropy Minimization. Extensive experiments on 15 long-tailed datasets show consistent gains in both accuracy and macro-F1, particularly for tail class.", "questions": "Q1. In realistic streaming scenarios, the head/tail imbalance may continuously shift over time. For instance, some tail classes may gradually become head classes as data distribution evolves. How would L-TTA behave under such dynamic imbalance transitions? Would its components (e.g., BEM or RSs) still maintain stability and prevent bias accumulation when the head and tail relationship itself changes over time?\n\nQ2. The proposed Synergistic Prototypes (SyPs) employ two prototypes per class to enhance representation learning for tail classes.\nHave the authors considered extending this idea to an adaptive or variable number of prototypes per class, which could further capture intra-class diversity and prevent overfitting to limited modes within each class?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "S1. **Realistic problem formulation.** \nThe paper is the first to define long-tailed test-time adaptation explicitly, bridging TTA and class-imbalance research. \n\nS2. **Theoretical support.**\nThe authors provide a proposition showing that BEM reduces the optimization gap between head and tail classes, giving analytical depth to their design.\n\nS3. **Comprehensive experiments.** \nEvaluations across 15 datasets and diverse settings (OOD, domain shift, noise) demonstrate robustness and clear macro-F1 improvements, validating that the method indeed benefits rare classes.", "weaknesses": "W1. **Limited methodological novelty.**\nWhile the paper defines a new and realistic long-tailed test-time adaptation setting, most of its core components --dual prototypes, rebalancing modules, and entropy regularization -- are adapted from existing long-tailed learning techniques. Therefore, the methodological contribution appears somewhat incremental, as it primarily repurposes well-known ideas rather than introducing fundamentally new mechanisms. Nevertheless, the adaptation of these ideas to a label-free, online TTA scenario is well-executed and empirically validated.\n\nW2. **Computational overhead not fully analyzed.** \nIn a test-time adaptation (TTA) environment, it is crucial to evaluate computational overhead, as most prior works explicitly report their runtime and justify that their methods can operate under realistic TTA constraints. To the best of my knowledge, the proposed method introduces additional parameters compared to existing baselines due to the inclusion of the RS module and dual prototypes. Therefore, a detailed analysis of runtime and memory consumption is required.\n\nW3. **Hyperparameter sensitivity.** \nThe method relies on several new hyperparameters (i.e., $\\beta$ in BEM, $\\tau$ for temperature, $\\eta$ for CRA weighting). The paper gives fixed settings but limited analysis on sensitivity or tuning difficulty. \n\nW4. **Intuition of synergy of SyPs and RS.** \nThe authors show the synergy of SyPs and RS with empirical results, but I cannot catch the intuition why those two modules can have positive synergy. Deeper explanation could make reviewers understand easily.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "tCcy3ZYvOm", "reviewer": "Reviewer_dibS", "summary": "The paper targets test-time adaptation (TTA) under long-tailed (LT) test streams for vision–language models (VLMs). It proposes a three-part framework: Synergistic Prototypes (DP+EP) for representation retention, Rebalancing Shortcuts (RSs) with a clustering re-allocation loss for structural balance, and Balanced Entropy Minimization (BEM) to reduce optimization bias toward head classes.", "questions": "- What concrete VLM-specific factors make LT-TTA harder than unimodal LT-TTA (e.g., cross-modal prototype drift, text prior effects)? Can you show cases where your gains require the VLM setup? (See Weakness 1)\n- Any results with larger/stronger VLMs? (See Weakness 2)", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- Addresses a practically important setting (LT streams in online TTA).\n\n- Clear, modular design with complementary roles (representation / structure / optimization).", "weaknesses": "- (Major) Problem novelty/positioning. The failure modes (head-bias accumulation, tail erosion under EM) are not VLM-specific; they are general LT-TTA issues. While packaging under “VLM LT-TTA” is useful, the problem statement and the proposed mechanisms (prototypes, rebalancing, entropy shaping) appear modality-agnostic, not specific to VLM. The paper should (i) sharpen what is uniquely VLM (e.g., cross-modal drift, text prior effects) and show where L-TTA leverages that, or (ii) reframe as a general LT-TTA method and broaden evidence beyond VLMs.\n\n- Model capacity: Results rely on low-capacity VLMs; validating on more powerful, recent VLMs (both open- and closed-source) would better establish its contribution and clarify whether gains persist or diminish with stronger backbones.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)