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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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Multi-Level Attribute-Guided-Based Adaptive Multi-Dilated Convolutional Network for Image Aesthetic Assessment.

Sumei Li¹, Mingxuan Xie¹, Wei Xiang^2,3

¹School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China.

Journal of Imaging

|December 24, 2025

Summary

This summary is machine-generated.

This study introduces a novel Multi-level Attribute-Guided Adaptive Multi-Dilated Convolutional Network (MAADN) for image aesthetic assessment. MAADN improves accuracy by considering hierarchical attribute interactions and mitigating preprocessing distortions.

Keywords:

aesthetic attributes attention mechanism dilated convolution image aesthetics assessment

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Area of Science:

Computer Vision
Artificial Intelligence
Image Processing

Background:

Image aesthetic assessment (IAA) is vital for research and applications.
Existing IAA methods neglect hierarchical attribute-feature interactions and suffer from aspect ratio distortion during preprocessing, losing aesthetic information.

Purpose of the Study:

To propose a novel network, MAADN, that addresses limitations in IAA.
To leverage multi-level attribute features for guiding aesthetic assessment.
To reduce the negative impact of image preprocessing via adaptive dilated convolution.

Main Methods:

Developed a dual-branch architecture: one for multi-level attribute feature extraction, another for attribute-guided aesthetic feature learning.
Introduced an Attention-based Attribute-Guided Aesthetic Module (AGAM) using visual attention.
Designed an Adaptive Multi-Dilate Rate Convolution Module (AMDM) for adaptive fusion of dilated convolution features.

Main Results:

The proposed MAADN model significantly outperforms current state-of-the-art IAA approaches.
Experimental results validate the effectiveness of the dual-branch architecture and adaptive modules.
Visual analysis confirms MAADN's precise localization of aesthetically critical regions.

Conclusions:

MAADN effectively addresses hierarchical interactions and preprocessing distortions in IAA.
The adaptive nature of the proposed modules enhances flexibility and performance.
MAADN offers a robust and accurate solution for image aesthetic assessment.