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ARXIV:2603.24037 · ADVERTISING AI · SUBMITTED 26 MAR · 20:30 UTC · FRESHNESS STALE
ARXIV:2603.24037ADVERTISING AISUBMITTED 26 MAR · 20:30 UTCFRESHNESS STALEKaiyuan Ji · Yixuan Gao · Lu Sun · Yushuo Zheng · Zijian Chen · Jianbo Zhang · +4 at arXiv
A framework and multimodal LLM for objective, scalable, and interpretable assessment of advertising image aesthetics to improve commercial conversion rates.
Opportunity summary
Pain A framework and multimodal LLM for objective, scalable, and interpretable assessment of advertising image aesthetics to improve commercial conversion rates.
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Blocker Evidence unverified
A framework and multimodal LLM for objective, scalable, and interpretable assessment of advertising image aesthetics to improve commercial conversion rates. To address these challenges, we present A^3 (Advertising Aesthetic Assessment), a comprehensive framework encompassing…
Advertising images significantly impact commercial conversion rates and brand equity, yet current evaluation methods rely on subjective judgments, lacking scalability, standardized criteria, and interpretability. To address these challenges, we present A^3 (Advertising Aesthetic Assessment),…
ScienceToStartup currently rates this 8.0/10 on the public viability pass. Extensive experiments on A^3-Bench demonstrate that A^3-Align achieves superior alignment with A^3-Law compared to existing models, and this alignment generalizes well to quality advertisement…
Advertising AI moved forward this cycle; last verified April 2026. Public score 8.0/10. Implementation evidence is present through a linked repository.
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mobile layout uses overflow-hidden min-w-0 break-wordsOpportunity summary
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A framework and multimodal LLM for objective, scalable, and interpretable assessment of advertising image aesthetics to improve commercial conversion rates.
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10.48550/arXiv.2603.24037A framework and multimodal LLM for objective, scalable, and interpretable assessment of advertising image aesthetics to improve commercial conversion rates.
Abstract
Advertising images significantly impact commercial conversion rates and brand equity, yet current evaluation methods rely on subjective judgments, lacking scalability, standardized criteria, and interpretability. To address these challenges, we present A^3 (Advertising Aesthetic Assessment), a comprehensive framework encompassing four components: a paradigm (A^3-Law), a dataset (A^3-Dataset), a multimodal large language model (A^3-Align), and a benchmark (A^3-Bench). Central to A^3 is a theory-driven paradigm, A^3-Law, comprising three hierarchical stages: (1) Perceptual Attention, evaluating perceptual image signals for their ability to attract attention; (2) Formal Interest, assessing formal composition of image color and spatial layout in evoking interest; and (3) Desire Impact, measuring desire evocation from images and their persuasive impact. Building on A^3-Law, we construct A^3-Dataset with 120K instruction-response pairs from 30K advertising images, each richly annotated with multi-dimensional labels and Chain-of-Thought (CoT) rationales. We further develop A^3-Align, trained under A^3-Law with CoT-guided learning on A^3-Dataset. Extensive experiments on A^3-Bench demonstrate that A^3-Align achieves superior alignment with A^3-Law compared to existing models, and this alignment generalizes well to quality advertisement selection and prescriptive advertisement critique, indicating its potential for broader deployment. Dataset, code, and models can be found at: https://github.com/euleryuan/A3-Align.
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Dimensions overall score 8.0
PROBLEM
A framework and multimodal LLM for objective, scalable, and interpretable assessment of advertising image aesthetics to improve commercial conversion rates. To address these challenges, we present A^3 (Advertising Aesthetic Assessment), a comprehensive framework encompassing fou...
METHOD
Advertising images significantly impact commercial conversion rates and brand equity, yet current evaluation methods rely on subjective judgments, lacking scalability, standardized criteria, and interpretability. To address these challenges, we present A^3 (Advertising Aesthetic...
RESULT
ScienceToStartup currently rates this 8.0/10 on the public viability pass. Extensive experiments on A^3-Bench demonstrate that A^3-Align achieves superior alignment with A^3-Law compared to existing models, and this alignment generalizes well to quality advertisement selection a...
WHY NOW
Advertising AI moved forward this cycle; last verified April 2026. Public score 8.0/10. Implementation evidence is present through a linked repository.
current evaluation methods rely on subjective judgments, lacking scalability, standardized criteria, and interpretability
Directly stated in the abstract as the problem being addressed
partial
Central to A^3 is a theory-driven paradigm, A^3-Law, comprising three hierarchical stages: (1) Perceptual Attention, evaluating perceptual image signals for their ability to attract attention; (2) Formal Interest, assessing formal composition of image color and spatial layout in evoking interest; and (3) Desire Impact, measuring desire evocation from images and their persuasive impact
Explicitly described in the abstract with clear details about the three stages
partial
we construct A^3-Dataset with 120K instruction-response pairs from 30K advertising images, each richly annotated with multi-dimensional labels and Chain-of-Thought (CoT) rationales
Specific numeric details provided in the abstract
partial
A^3-Align achieves superior alignment with A^3-Law compared to existing models
Directly stated in abstract but without specific comparative metrics
partial
this alignment generalizes well to quality advertisement selection and prescriptive advertisement critique
Directly stated in abstract but without specific performance metrics
partial
indicating its potential for broader deployment
Implied in the abstract but not explicitly stated as a definitive claim
partial
Advertising images significantly impact commercial conversion rates and brand equity
Directly stated as motivation for the research
partial
We further develop A^3-Align, trained under A^3-Law with CoT-guided learning on A^3-Dataset
Directly stated in the abstract with specific training methodology
partial
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A framework and multimodal LLM for objective, scalable, and interpretable assessment of advertising image aesthetics to improve commercial conversion rates.
Segment
Advertising AI
Adoption evidence
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Commercial read
8.0/10 public viability
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Technical feasibility
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No public implementation surface observed.
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Write integration checklist from prototype path and target workflow.
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