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Cosine Similarity Distillation Vision Mixture-of-Experts for Intelligent Housing-Dimensional Urban Physical

Kun Zhao¹, Helei Ren¹, Wenbin He¹

¹School of Information Management, Qingdao University of Technology, Qingdao 266520, China.

Sensors (Basel, Switzerland)

|June 12, 2026

Summary

We introduce the Housing-dimensional Visual Inspection Image Dataset (HOUSED) and a hierarchical Vision Mixture of Experts (VMoE) framework for complex urban scene analysis. Our approach enhances accuracy and efficiency in intelligent housing inspections.

Keywords:

complex visual scenes image classification intelligent urban physical examination mixture of experts old residential

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Area of Science:

Computer Vision
Artificial Intelligence
Urban Planning

Background:

Intelligent housing requires evaluating complex visual scenes in aging communities.
Existing datasets and methods struggle with heterogeneous tasks and class imbalances in this domain.

Purpose of the Study:

To introduce a novel dataset and a hierarchical Vision Mixture of Experts (VMoE) framework for housing-dimensional visual inspection.
To address challenges of complex scenes, hierarchical labels, and data imbalance in urban aging communities.

Main Methods:

Developed the Housing-dimensional Visual Inspection Image Dataset (HOUSED) with hierarchical labeling.
Proposed a hierarchical VMoE framework featuring CS-DisVMoE module with CS-Soft routing.
Utilized FENNEL-based graph partitioning for expert initialization and a composite loss function (Supervised Contrastive Loss and Focal Loss).

Main Results:

The proposed framework achieved an average accuracy improvement of 4.3% over ViT-Tiny and 1.81% over the best VMoE baseline.
Demonstrated lower computational costs compared to baseline models.
Verified generalizability and competitive performance on mixed public vision datasets.

Conclusions:

The HOUSED dataset and hierarchical VMoE framework offer a significant advancement for intelligent housing inspections.
The proposed methods effectively handle complex visual scenes, hierarchical data, and class imbalances.
The framework shows promise for broader applications in complex-scene computer vision tasks.