What Do Vision-Language Models Encode for Personalized Image Aesthetics Assessment?

Hitomi Yanaka; Koki Ryu

arxiv: 2604.11374 · v1 · submitted 2026-04-13 · 💻 cs.CV · cs.CL

What Do Vision-Language Models Encode for Personalized Image Aesthetics Assessment?

Koki Ryu , Hitomi Yanaka This is my paper

Pith reviewed 2026-05-10 16:01 UTC · model grok-4.3

classification 💻 cs.CV cs.CL

keywords vision-language modelspersonalized image aesthetics assessmentinternal representationslinear probesaesthetic attributesdecoder layersimage assessment

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The pith

Vision-language models encode diverse aesthetic attributes that enable effective personalized image aesthetics assessment with simple linear models.

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

The paper examines the internal representations of vision-language models to determine if they contain the multi-level aesthetic information necessary for assessing images based on personal preferences. It finds that various aesthetic attributes are encoded and move through the model to the language decoder layers. The work then shows that these representations can be used directly with lightweight linear models to achieve personalization without any fine-tuning of the VLM. This matters because it suggests a way to model subjective tastes efficiently, which has real-world applications in image recommendation and editing systems tailored to users.

Core claim

VLMs encode diverse aesthetic attributes that propagate into the language decoder layers. Building on these representations, simple linear models can perform PIAA effectively. Aesthetic information transfer varies across layers in different VLM architectures and across image domains.

What carries the argument

Internal representations of vision-language models where aesthetic attributes are encoded and propagated into language decoder layers, used as input to linear probes for user-specific prediction.

If this is right

Personalized image aesthetics assessment can be performed efficiently without fine-tuning or retraining the full VLM for each user.
The approach applies across different VLM architectures and image domains.
Layer-wise analysis reveals how aesthetic information flows through the model.
Subjective preferences can be modeled using existing pre-trained VLMs without new labeled training from scratch.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Similar linear probing on frozen representations could extend to other subjective visual tasks such as emotion recognition or style preference prediction.
VLM training objectives might be adjusted in future work to strengthen encoding of subjective attributes.
Real-time user adaptation becomes feasible by maintaining a small linear head per user on top of a shared VLM backbone.

Load-bearing premise

The aesthetic attributes detected in VLM representations are sufficient for individual personalization and linear probes on frozen representations capture user-specific variation without needing task-specific fine-tuning or additional supervision.

What would settle it

An experiment showing that linear models trained on VLM layer features predict individual aesthetic ratings no better than a non-personalized baseline when tested on held-out users with distinct preferences.

Figures

Figures reproduced from arXiv: 2604.11374 by Hitomi Yanaka, Koki Ryu.

**Figure 1.** Figure 1: Overview of PIAA using VLM representations. s and W denote user-specific aesthetics scores and linear estimators respectively. Aesthetic attributes encoded in VLM hidden representations are linearly transformed to predict user-specific aesthetic scores without model fine-tuning. In PIAA, image-level aesthetic attributes such as lighting and color have been leveraged to better reflect individual preferenc… view at source ↗

**Figure 3.** Figure 3: Layer-wise probing performance for the Content attribute in Qwen3-VL 2B and Gemma 3 4B. all V and LT layers of Qwen3-VL 2B are further visualized in [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗

**Figure 4.** Figure 4: Layer-wise PIAA performance across V, LV, and LT representations for multiple models and datasets. attributes and 5 user attributes based on the annotations available in PARA. After pretraining, we perform personalized finetuning using 100 images per user and evaluate performance on 50 held-out images per user, consistent with our main experimental protocol. Cross-Domain Transfer to LAPIS We further ev… view at source ↗

**Figure 5.** Figure 5: Examples of the LAPIS images assigned high (top) and low (bottom) scores by different methods for a [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗

**Figure 6.** Figure 6: Distribution of Spearman correlation between [PITH_FULL_IMAGE:figures/full_fig_p009_6.png] view at source ↗

**Figure 7.** Figure 7: Prompt template used for the Few-shot PIAA [PITH_FULL_IMAGE:figures/full_fig_p013_7.png] view at source ↗

**Figure 9.** Figure 9: Distribution of aesthetic attribute annotations [PITH_FULL_IMAGE:figures/full_fig_p014_9.png] view at source ↗

**Figure 10.** Figure 10: Spearman correlation between aesthetic at [PITH_FULL_IMAGE:figures/full_fig_p015_10.png] view at source ↗

**Figure 11.** Figure 11: Example images from AADB with applied augmentations. These results indicate that Linear-Hidden consistently outperforms competing methods across bootstrap resamples, providing strong statistical evidence for the performance improvements reported in the main paper. C Additional Experiments C.1 Probing with Augmented Images Since the aesthetic attributes used for probing on AADB exhibit non-negligible cor… view at source ↗

**Figure 12.** Figure 12: Probing performance under different image [PITH_FULL_IMAGE:figures/full_fig_p018_12.png] view at source ↗

**Figure 13.** Figure 13: Layer-wise probing performance across V and LT layers for Qwen3-VL 2B on PARA. relations. As a result, these probing results alone do not provide strong evidence for the presence of diverse and disentangled aesthetic attributes encoded in VLM representations. C.3 Prompt Sensitivity of the Probing Results While Section 3 demonstrates that VLMs encode diverse aesthetic attributes, it remains unclear to wh… view at source ↗

**Figure 14.** Figure 14: User-averaged Spearman correlation obtained by Linear-Hidden PIAA using combined representations from the vision encoder (Vi) and language decoder (LTj ). 22 [PITH_FULL_IMAGE:figures/full_fig_p022_14.png] view at source ↗

read the original abstract

Personalized image aesthetics assessment (PIAA) is an important research problem with practical real-world applications. While methods based on vision-language models (VLMs) are promising candidates for PIAA, it remains unclear whether they internally encode rich, multi-level aesthetic attributes required for effective personalization. In this paper, we first analyze the internal representations of VLMs to examine the presence and distribution of such aesthetic attributes, and then leverage them for lightweight, individual-level personalization without model fine-tuning. Our analysis reveals that VLMs encode diverse aesthetic attributes that propagate into the language decoder layers. Building on these representations, we demonstrate that simple linear models can perform PIAA effectively. We further analyze how aesthetic information is transferred across layers in different VLM architectures and across image domains. Our findings provide insights into how VLMs can be utilized for modeling subjective, individual aesthetic preferences. Our code is available at https://github.com/ynklab/vlm-latent-piaa.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

VLMs already carry aesthetic signals into their decoder layers that let simple linear probes do no-fine-tune PIAA, but the probes may mostly learn user offsets rather than image-by-image personal differences.

read the letter

The paper shows that vision-language models encode diverse aesthetic attributes that reach the language decoder layers, and that linear models trained on those frozen representations can handle personalized image aesthetics assessment without any fine-tuning of the VLM itself. They also track how the signal moves across layers and image domains in different architectures. That combination is the concrete new piece: prior interpretability work on VLMs exists, but the direct zero-fine-tune application to PIAA plus the layer-wise propagation analysis is a fresh use case. Releasing the code helps anyone who wants to check or extend it. The layer analysis itself looks like the strongest part; it gives a practical map of where subjective aesthetic information lives inside these models, which is useful for anyone trying to read out similar properties without retraining. The softer spot is the effectiveness claim for true personalization. The stress-test concern lands: because VLMs are trained on aggregate data, a linear head could succeed by fitting a constant per-user bias instead of learning how the same image should be scored differently by different people. If the experiments do not include user-mean baselines or test whether the probe produces meaningfully different outputs for the same image across users, the results will overstate how much individual variation is actually captured. The abstract gives no numbers, so the full tables will decide how much weight to give the claim. This paper is for researchers who work on VLM internals or on cheap adaptation of large models for subjective tasks like recommendation or creative tools. A reader who wants to understand or reuse the internal representations for personalization will find the layer breakdown and code release worthwhile. It has enough substance and a clear practical angle to deserve a serious referee, though the evaluation will need tighter controls on what the linear probes are actually learning versus simple biases.

Referee Report

2 major / 1 minor

Summary. The paper analyzes internal representations of vision-language models (VLMs) to determine whether they encode rich, multi-level aesthetic attributes suitable for personalized image aesthetics assessment (PIAA). It reports that such attributes are present and propagate into language decoder layers across architectures and domains. Building on this, it shows that simple linear models applied to frozen VLM representations can perform effective PIAA without any model fine-tuning, and provides layer-wise and domain-specific transfer analyses to support insights into modeling subjective individual preferences. Code is released for reproducibility.

Significance. If the empirical results hold under rigorous controls, the work would be significant for demonstrating a lightweight, fine-tuning-free route to personalization in subjective vision tasks using off-the-shelf VLMs. This could reduce compute barriers for PIAA applications and offer mechanistic insights into how aesthetic information flows through VLM layers. The public code release is a clear strength that supports verification and extension.

major comments (2)

[Abstract] Abstract and experimental results: the central claim that 'simple linear models can perform PIAA effectively' is load-bearing for the contribution, yet the abstract provides no quantitative metrics, baseline comparisons (e.g., per-user mean predictors), user counts, or dataset details. Without evidence that performance exceeds what could be obtained by fitting user-specific constants or global biases on aggregate-pretrained representations, it remains unclear whether the probes capture image-conditioned, user-differentiated aesthetics rather than average shifts.
[Analysis of Representations] Representation analysis sections: the layer-wise propagation findings do not include controls (such as user-label shuffling or same-image cross-user activation comparisons) to establish that decoder-layer activations contain user-specific signal beyond aggregate aesthetics. This directly affects the claim that the encoded attributes 'propagate into the language decoder layers' in a manner usable for individual personalization.

minor comments (1)

[Abstract] The abstract would benefit from a brief statement of the specific VLM architectures, datasets, and evaluation metrics used, to allow readers to gauge the scope of the claims immediately.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the thoughtful and constructive comments. We address each major point below and will revise the manuscript to incorporate additional details and controls as suggested.

read point-by-point responses

Referee: [Abstract] Abstract and experimental results: the central claim that 'simple linear models can perform PIAA effectively' is load-bearing for the contribution, yet the abstract provides no quantitative metrics, baseline comparisons (e.g., per-user mean predictors), user counts, or dataset details. Without evidence that performance exceeds what could be obtained by fitting user-specific constants or global biases on aggregate-pretrained representations, it remains unclear whether the probes capture image-conditioned, user-differentiated aesthetics rather than average shifts.

Authors: We agree that the abstract would benefit from including key quantitative evidence to support the central claim. In the revised version, we will expand the abstract to report the number of users, dataset details, and performance metrics showing that our linear probes outperform per-user mean predictors and global bias baselines. These comparisons, already present in the experimental results, confirm that the VLM representations capture image-conditioned and user-differentiated aesthetic signals rather than mere average shifts. We will ensure the abstract concisely reflects these findings. revision: yes
Referee: [Analysis of Representations] Representation analysis sections: the layer-wise propagation findings do not include controls (such as user-label shuffling or same-image cross-user activation comparisons) to establish that decoder-layer activations contain user-specific signal beyond aggregate aesthetics. This directly affects the claim that the encoded attributes 'propagate into the language decoder layers' in a manner usable for individual personalization.

Authors: We acknowledge the value of these explicit controls for isolating user-specific signals. Our existing layer-wise and cross-domain analyses demonstrate propagation of aesthetic attributes into decoder layers and their utility for personalization via linear probes. To strengthen this, we will add user-label shuffling experiments and same-image cross-user activation comparisons in the revision. These will show that decoder-layer activations contain user-specific information beyond aggregate aesthetics, further validating the personalization claims. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical probing of frozen VLM representations with no self-referential derivations or fitted predictions

full rationale

The paper conducts layer-wise analysis of VLM internal representations for aesthetic attributes and applies simple linear probes for PIAA on frozen models. No equations, parameter fits, or derivations are presented that reduce any claimed result to its own inputs by construction. The work relies on external datasets and standard probing techniques rather than self-citation chains or ansatzes that presuppose the target outcome. This is a standard empirical analysis paper whose central claims are testable against held-out user data and do not collapse into tautology.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review; no explicit free parameters, axioms, or invented entities are stated. The central claim implicitly assumes that linear probes on frozen VLM states can isolate user-specific aesthetic signals.

pith-pipeline@v0.9.0 · 5458 in / 1021 out tokens · 47917 ms · 2026-05-10T16:01:59.301025+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

5 extracted references · 5 canonical work pages · 1 internal anchor

[1]

In Proceedings of the IEEE international conference on computer vision, pages 3514–3523

Aesthetic critiques generation for photos. In Proceedings of the IEEE international conference on computer vision, pages 3514–3523. Alex Clark. 2015. Pillow (pil fork) documentation. Mohamed El Banani, Amit Raj, Kevis-Kokitsi Mani- nis, Abhishek Kar, Yuanzhen Li, Michael Rubinstein, Deqing Sun, Leonidas Guibas, Justin Johnson, and Varun Jampani. 2024. Pro...

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ordinary

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work page arXiv 2024
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work page internal anchor Pith review Pith/arXiv arXiv 2019
[5]

Assess the aesthetics of this image

0.18.0, Pillow (Clark, 2015) 12.0.0, and OpenCV (Bradski, 2000) 4.11.0.86. Evaluation metrics were computed using scikit-learn (Pe- 14 Overall Score BalacingElements ColorHarmony Content DoF Light MotionBlur Object Repetition RuleOfThirds SymmetryVividColor Overall Score BalacingElements ColorHarmony Content DoF Light MotionBlur Object Repetition RuleOfTh...

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In Proceedings of the IEEE international conference on computer vision, pages 3514–3523

Aesthetic critiques generation for photos. In Proceedings of the IEEE international conference on computer vision, pages 3514–3523. Alex Clark. 2015. Pillow (pil fork) documentation. Mohamed El Banani, Amit Raj, Kevis-Kokitsi Mani- nis, Abhishek Kar, Yuanzhen Li, Michael Rubinstein, Deqing Sun, Leonidas Guibas, Justin Johnson, and Varun Jampani. 2024. Pro...

work page 2015

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InProceedings of the IEEE/CVF international conference on computer vi- sion workshops, pages 0–0

Aesthetic image captioning from weakly- labelled photographs. InProceedings of the IEEE/CVF international conference on computer vi- sion workshops, pages 0–0. Simon Hentschel, Konstantin Kobs, and Andreas Hotho

work page

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ordinary

Clip knows image aesthetics.Frontiers in Artificial Intelligence, 5:976235. Yipo Huang, Quan Yuan, Xiangfei Sheng, Zhichao Yang, Haoning Wu, Pengfei Chen, Yuzhe Yang, Leida Li, and Weisi Lin. 2024. Aesbench: An ex- pert benchmark for multimodal large language mod- els on image aesthetics perception.arXiv preprint arXiv:2401.08276. Omri Kaduri, Shai Bagon,...

work page arXiv 2024

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DINOv3

Ava: A large-scale database for aesthetic vi- sual analysis. In2012 IEEE Conference on Computer Vision and Pattern Recognition, pages 2408–2415. Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Köpf, Edward Yang, Zach DeVito, Martin Ra...

work page internal anchor Pith review Pith/arXiv arXiv 2019

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Assess the aesthetics of this image

0.18.0, Pillow (Clark, 2015) 12.0.0, and OpenCV (Bradski, 2000) 4.11.0.86. Evaluation metrics were computed using scikit-learn (Pe- 14 Overall Score BalacingElements ColorHarmony Content DoF Light MotionBlur Object Repetition RuleOfThirds SymmetryVividColor Overall Score BalacingElements ColorHarmony Content DoF Light MotionBlur Object Repetition RuleOfTh...

work page 2015