Beyond Absolute Scores: Relative Edit-induced Difference for Generalizable Image Aesthetic Assessment

2026-06-04 · Source: Takara TLDR - Daily AI Papers · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Computer Vision · Depth: Expert, medium

Summary

The paper "Beyond Absolute Scores: Relative Edit-induced Difference for Generalizable Image Aesthetic Assessment" introduces RED-Aes, a novel framework for Image Aesthetic Assessment (IAA). Unlike traditional methods that regress absolute Mean Opinion Scores (MOS), RED-Aes addresses the dynamic nature of human aesthetic perception by learning visual factors that drive aesthetic changes. It achieves this by leveraging controllable image editing models to simulate human aesthetic reasoning. To support this, the authors constructed the RED-20k dataset, which includes editing-based image pairs, quantitative aesthetic differences, and Chain-of-Thought (CoT) reasoning. A three-stage training strategy, optimized solely through relative supervision guided by a relative ranking consistency reward, enables the model to learn generalizable aesthetic principles. Extensive experiments demonstrate that RED-Aes achieves strong performance on multiple public benchmarks, exhibiting superior generalization capabilities.

Key takeaway

For machine learning engineers developing or evaluating image aesthetic models, consider shifting from absolute Mean Opinion Score (MOS) regression to relative aesthetic difference learning. Your models will achieve superior generalization by explicitly learning visual factors that drive aesthetic changes, rather than fitting static score distributions. This approach, exemplified by RED-Aes, suggests building datasets with editing-based image pairs and Chain-of-Thought reasoning to capture dynamic human perception. You should explore relative supervision techniques to enhance model robustness across diverse scenarios.

Key insights

Human aesthetic perception is dynamic and relative, not absolute, driven by subconscious comparisons and visual factors.

Principles

• Aesthetic assessment benefits from relative comparisons over absolute scores.
• Causal reasoning about aesthetic differences improves model generalization.
• Controllable image editing can simulate human aesthetic reasoning.

Method

RED-Aes employs controllable image editing to learn visual factors driving aesthetic changes, using a three-stage training strategy with relative ranking consistency reward on the RED-20k dataset.

In practice

• Use editing-based image pairs for aesthetic difference learning.
• Incorporate Chain-of-Thought reasoning in aesthetic datasets.

Topics

• Image Aesthetic Assessment
• Generative Image Editing
• Relative Learning
• RED-20k Dataset
• Chain-of-Thought Reasoning
• Model Generalization

Code references

• BarretBa/ICTHP
• mdyao/PhotoAgent

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Takara TLDR - Daily AI Papers.