Adapting Vision-Language Models Without Labels: A Comprehensive Survey

Eleni Chatzi; Hao Dong; Jian Liang; Lijun Sheng; Olga Fink; Ran He

REVIEW 3 major objections 5 minor 4 cited by

Adapting Vision-Language Models Without Labels: A Comprehensive Survey

T0 review · 3 major / 5 minor · reviewed 2026-08-05 · deepseek-v4-flash

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Pith's one-line read This survey organizes the field of unsupervised vision-language model adaptation into four paradigms defined by how much unlabeled visual data is available: none, abundant, batch, or streaming.

desk verdict A useful survey with a serviceable data-availability taxonomy; the cross-listings need explaining and the 'first' claim needs softening. read the letter →

arxiv 2508.05547 v1 pith:SBWR6XBW submitted 2025-08-07 cs.LG cs.AIcs.CV

Hao Dong , Lijun Sheng , Jian Liang , Ran He , Eleni Chatzi , Olga Fink This is my paper

classification cs.LGcs.AIcs.CV

keywords vision-languagemodelsunsupervisedadaptationtest-timetaxonomydata-freetransferdomainCLIPsurvey

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper argues that all methods for adapting vision-language models without labels can be usefully organized by one practical factor: how much unlabeled visual data the adapter gets to see. It proposes a taxonomy with four paradigms: Data-Free Transfer (no visual data), Unsupervised Domain Transfer (abundant offline unlabeled data), Episodic Test-Time Adaptation (a single batch of test instances), and Online Test-Time Adaptation (streaming data). The survey claims to be the first to center a taxonomy on this axis, and then maps the literature onto it, grouping methods within each paradigm by strategy. A sympathetic reader would care because the choice of adaptation technique in practice is usually dictated by what data is actually available at deployment time, and this framing makes that constraint explicit.

What carries the argument

The taxonomy itself is the load-bearing object: four named paradigms distinguished by the amount and flow of unlabeled visual data (no data, abundant static data, a batch, a stream). For each paradigm, the survey supplies a strategy-level decomposition that organizes the field's methods into clusters, such as text augmentation vs. image utilization in the data-free case. These strategy clusters are the mechanism by which the survey turns a scattered literature into a structured comparison space.

What would settle it

Find a published unsupervised VLM adaptation method whose data-access pattern does not match any of the four paradigms, for example a method that alternates between offline unlabeled data and online streaming data in a single adaptation run, or one that changes its category depending on how the data is split into batches; demonstrating such a method would show the taxonomy is incomplete or not mutually exclusive.

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Extended reading notes

Core claim

The paper's central contribution is a taxonomy that classifies unsupervised VLM adaptation methods according to the availability and nature of unlabeled visual data during adaptation: Data-Free Transfer uses only class names and text, Unsupervised Domain Transfer has a large static pool of unlabeled target images, Episodic Test-Time Adaptation adapts per batch at inference, and Online Test-Time Adaptation handles a continuous stream. Within each paradigm, the paper identifies recurring methodological strategies: text augmentation, image utilization, and network modification for data-free transfer; self-training, entropy optimization, and external resource utilization for domain transfer; ent

Load-bearing premise

The taxonomy assumes that every unsupervised VLM adaptation method fits cleanly into exactly one of the four data-availability categories and that these four categories cover the whole field.

Editorial extensions

If this is right

Practitioners can select an adaptation method based on the data regime their deployment actually has, rather than on method labels alone.
Future work within a single paradigm can be compared fairly, since the taxonomy pins down the data-access assumptions.
The strategy groupings reveal where methods borrow ideas across paradigms, such as pseudo-labeling appearing in both domain transfer and online adaptation.
The taxonomy exposes gaps: for instance, few methods address open-world or privacy-constrained settings within any of the four paradigms.
Researchers can use the taxonomy to identify the least-explored data regime and target new method development there.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

A testable extension would be to audit the taxonomy's mutual exclusivity: pick a published method and check whether its behavior changes if it is given a different amount of unlabeled data, which would suggest the paradigms are not cleanly separated by data availability alone.
The survey's focus on data availability suggests a practical decision rule: when deployment has no images, use text augmentation; when it has a large unlabeled pool, use self-training or entropy optimization; when only a test batch is available, use entropy minimization or distribution alignment.
The taxonomy implies that the same underlying algorithm could be repositioned across paradigms by changing only the data feeding procedure, which would be a concrete way to test whether data availability is truly the organizing dimension.
If the taxonomy is right, benchmark design should report which paradigm each method targets, and current leaderboards that mix paradigms may be comparing methods under incompatible assumptions.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Short version: this is a useful survey with a genuinely serviceable organizing idea; the stress-test's 'Data-Free' complaint misses the definition, but the cross-listings are a real wrinkle that needs addressing before the taxonomy is a partition.

The genuinely new thing is the axis itself: organizing unsupervised VLM adaptation by how much unlabeled visual data is available—none, abundant static, an episodic batch, an online stream. That's a practitioner-relevant distinction that the cited prior surveys don't center, and it gives the paper a clear spine. The four paradigm chapters are well structured, the method coverage is broad (text augmentation, self-training, entropy optimization, memory mechanisms, etc.), and the benchmark table in Section VIII is useful. I also want to credit the authors for defining 'Data-Free' explicitly as no visual data from the downstream task—so including methods that retrieve or generate external images is internally coherent, and the stress-test note on that point is wrong.

The real soft spot is mutual exclusivity. SwapPrompt is discussed in both Unsupervised Domain Transfer (Sec. V.A) and Online TTA (Sec. VII.A); Neural Priming appears in both Data-Free (Sec. IV.B) and Unsupervised Domain Transfer (Sec. V.C). The survey never acknowledges that a method might be applicable in more than one paradigm, so the taxonomy feels like a labeling scheme rather than a principled partition. This is fixable: a short note that some methods are adaptable to multiple settings and are listed where they are most representative would do.

The 'first taxonomy' claim is also a bit assertive. It's plausible, but the comparison with prior surveys [37]-[40] is brief; a table contrasting the axes would make the novelty easier to verify. Some Table V symbols are garbled, but that's cosmetic.

Who's it for: anyone getting into label-free VLM adaptation who wants a map of the literature and a benchmark overview. The taxonomy will likely be cited, and the survey deserves a serious referee, but I'd ask the authors to tighten the category boundaries and moderate the 'first' claim before publication.

Recommendation: accept for peer review with those revisions requested.

Referee Report

3 major / 5 minor

Summary. This survey proposes an organizing taxonomy for unsupervised adaptation of vision-language models (VLMs), claiming to be the first to categorize methods by the availability of unlabeled visual data. It defines four paradigms: Data-Free Transfer, Unsupervised Domain Transfer, Episodic Test-Time Adaptation, and Online Test-Time Adaptation. Sections IV–VII describe each paradigm and its sub-strategies (e.g., text augmentation, self-training, entropy minimization, pseudo-labeling, memory mechanisms), with representative methods summarized in Tables I–IV. Section VIII surveys benchmarks and applications, and Section IX lists open challenges. An associated GitHub repository of relevant literature is provided.

Significance. If the taxonomy is accepted, the survey offers a practically useful framework for organizing a rapidly growing literature, and the strategy-level breakdown within each paradigm is helpful for practitioners. The paper is comprehensive in coverage, includes a wide range of recent methods, and identifies meaningful open problems such as theoretical analysis, open-world adaptation, and failure-mode documentation. The main contribution, however, is the taxonomy itself, so its internal consistency and clear operationalization are load-bearing. The current manuscript contains ambiguities and cross-listings that weaken the claimed novelty until resolved.

major comments (3)

[Sec. IV.B and Table I vs. Abstract/Sec. III] The paradigm is defined in Sec. III and Sec. IV as 'data-free' meaning no downstream visual data, but the abstract states 'Data-Free Transfer (no data)' and Sec. IV.B explicitly includes methods that retrieve external images (ReCo, SuS-X, Neural Priming) or synthesize images with generative models (Shipard et al., AttrSyn, GenCL). This is not merely a naming issue: the taxonomy's central axis is 'availability of unlabeled visual data,' so the reader needs to know whether external/generated visual data count as 'data' for the taxonomy. Please rename the paradigm (e.g., 'name-only transfer' or 'no downstream data transfer'), revise the abstract, and explicitly state how external and generated visual data are treated within the axis.
[Secs. V.A, VII.A and Tables I–II] The taxonomy is presented as a partition of methods into four paradigms, but the same method appears in multiple paradigms without explanation. SwapPrompt [19] is discussed under Unsupervised Domain Transfer / Self-Training (Sec. V.A, Table II) and also under Online Test-Time Adaptation / Pseudo-Labeling (Sec. VII.A). Neural Priming [91] appears under Data-Free Transfer / Image Utilization (Sec. IV.B, Table I) and under Unsupervised Domain Transfer / External Resource Utilization (Sec. V.C, Table II). If the taxonomy classifies evaluation scenarios rather than methods, that should be stated explicitly and a method may legitimately appear in multiple scenarios. If the taxonomy classifies methods, these cross-listings violate mutual exclusivity. Please add a rule for assignments or a table mapping each representative method to its primary paradigm.
[Sec. III, 'Taxonomy' paragraph] The four paradigms are asserted as jointly covering the field, but no explicit completeness argument is given. Boundary cases are not addressed: for example, methods that adapt on a single test image with augmentations (e.g., TPT, Sec. VI.A) do not obviously operate on a 'batch of test instances' as the episodic definition states, and methods that use only LLM-generated text without any visual data could be seen as a separate axis. Since the paper's main claim is that this is the first taxonomy centered on unlabeled visual data availability, the authors should state the membership criteria for each paradigm and explain how boundary cases are resolved. A summary table of representative methods with their paradigm assignments would make the taxonomy's exhaustiveness and mutual exclusivity verifiable.

minor comments (5)

[Table V] The popularity encoding is corrupted: several cells contain raw LaTeX tokens such as '/star-half-alt' (e.g., rows for ImageNet-A, Office-Home, KITTI-STEP). This makes the table difficult to read and undermines the benchmark overview. Also define what the star ratings mean and fix the typo 'Unsuperivsed' in the table caption.
[Eq. (5)] The index i is reused for both the class index and the augmented-view index, which is confusing. Please use distinct indices, e.g., c for classes and n for augmented views, and align with the notation in Eq. (1).
[References] References [15] and [67] appear to be the same work (Menon & Vondrick, 'Visual classification via description from large language models') with different venue strings. This duplicate should be merged or clarified.
[Sec. V.A] Typo: 'high-qulity' should be 'high-quality'.
[Sec. VII.A] SwapPrompt is discussed in the pseudo-labeling subsection but does not appear in Table IV's representative works. Either add it to the table or note that it is discussed as a related variant.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the survey's taxonomy is a literature-organizing classification, not a derived prediction; self-citations are not load-bearing.

full rationale

This is a survey paper. Its central claim is the novelty of a taxonomy based on unlabeled visual data availability. That taxonomy is presented as a categorization (Sec. III 'Taxonomy'; Fig. 1) and is not derived from fitted parameters, equations, or a self-citation chain. Methods are grouped by their data assumptions (no data, abundant unlabeled data, batch, streaming), and the assignments are made against external published methods and benchmarks (Tables I-V). The paper's self-citations (e.g., [20], [27], [38], [80], [287]-[289]) appear as ordinary references to prior work by the authors or as comparisons to previous surveys; none is used to justify the taxonomy's structure or to forbid alternatives. The skeptical concern about mutual exclusivity (e.g., SwapPrompt listed under both Unsupervised Domain Transfer and Online TTA; Neural Priming under both Data-Free and Unsupervised Domain Transfer; 'Data-Free' including external/generated images) is a potential correctness/consistency issue in the survey's organization, not a circular derivation: the taxonomy does not reduce to a fit or to its own definitions in a way that makes the survey's claims vacuous. Therefore, under the hard rules, no circularity step can be quoted as reducing to inputs, and the score is 0.

Assumptions & free parameters 0 free parameters · 2 assumptions · 0 invented entities

The central claim (the taxonomy) relies on the assumption that the surveyed literature is described accurately and that the four proposed data-availability categories cleanly partition all existing methods. No free parameters or invented entities are introduced, as this is a survey.

assumptions (2)

domain assumption The surveyed papers are accurately described and their reported results are taken at face value.
The survey synthesizes hundreds of papers; any mischaracterization could affect the taxonomy's placement.
ad hoc to paper The four data-availability paradigms are mutually exclusive and exhaustive.
This is the survey's own organizing assumption; it is asserted in the Taxonomy paragraph of Section III and underlies the entire structure.

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Cite this review

Pith. "Pith review of Adapting Vision-Language Models Without Labels: A Comprehensive Survey." pith.science (2026). https://pith.science/paper/SBWR6XBW

@misc{pith2026250805547,
  author       = {Pith},
  title        = {Pith review of: Adapting Vision-Language Models Without Labels: A Comprehensive Survey},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/SBWR6XBW}},
  note         = {Machine review of arXiv:2508.05547}
}

read the original abstract

Vision-Language Models (VLMs) have demonstrated remarkable generalization capabilities across a wide range of tasks. However, their performance often remains suboptimal when directly applied to specific downstream scenarios without task-specific adaptation. To enhance their utility while preserving data efficiency, recent research has increasingly focused on unsupervised adaptation methods that do not rely on labeled data. Despite the growing interest in this area, there remains a lack of a unified, task-oriented survey dedicated to unsupervised VLM adaptation. To bridge this gap, we present a comprehensive and structured overview of the field. We propose a taxonomy based on the availability and nature of unlabeled visual data, categorizing existing approaches into four key paradigms: Data-Free Transfer (no data), Unsupervised Domain Transfer (abundant data), Episodic Test-Time Adaptation (batch data), and Online Test-Time Adaptation (streaming data). Within this framework, we analyze core methodologies and adaptation strategies associated with each paradigm, aiming to establish a systematic understanding of the field. Additionally, we review representative benchmarks across diverse applications and highlight open challenges and promising directions for future research. An actively maintained repository of relevant literature is available at https://github.com/tim-learn/Awesome-LabelFree-VLMs.

Figures

Figures reproduced from arXiv: 2508.05547 by the authors.

**Figure 1.** Illustration of our taxonomy on unsupervised adaptation with VLMs. We categorize existing unsupervised methods into [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗

**Figure 2.** Taxonomy of unsupervised adaptation paradigms for vision-language models (VLMs). [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 3.** Three representative strategies of the data-free transfer paradigm. TABLE I: Popular strategies along with their representative works of data-free transfer. Strategies Representative Works Text Augmentation DCLIP [15], CuPL [21], CHiLS [88], TaI [89]. Image Utilization ReCo [28], SuS-X [90], Priming [91], GenCL [92]. Network Modification MaskCLIP [16], CALIP [93], SCLIP [94], ProxyCLIP [95]. relies exclusively on te… view at source ↗

Figures from the paper (3 more)

**Figure 4.** Figure 4: Three representative strategies of the unsupervised domain transfer paradigm. TABLE II: Popular strategies along with their representative works of unsupervised domain transfer. Strategies Representative Works Self-Training UPL [17], LaFTer [118], MUST [26], ReCLIP [11…

**Figure 5.** Figure 5: Three representative strategies of the episodic test-time adaptation paradigm. TABLE III: Popular strategies along with their representative works of episodic test-time adaptation. Strategies Representative Works Entropy Minimization TPT [18], DiffTPT [85], R-TPT [148]…

**Figure 6.** Figure 6: Three representative strategies of the online test-time adaptation paradigm. TABLE IV: Popular strategies along with their representative works of online test-time adaptation. Strategies Representative Works Pseudo-Labeling DART [198], CLIPArTT [199], CLIP-OT [200], WA…

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Forward citations

Cited by 4 Pith papers

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