Rethinking Text-to-Image as Semantic-Aware Data Augmentation for Indoor Scene Recognition

2026-06-17 · Source: Computer Vision and Pattern Recognition · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

A novel approach addresses the scarcity of training data for indoor image recognition by leveraging Stable Diffusion (SD) to generate synthetic images for data augmentation. This method provides a principled framework for synthesizing diverse and realistic indoor scenes, significantly enriching the training data pool for robust deep learning models. Experimental results on the MIT Indoor Scene dataset demonstrate that this technique enhances model training performance, particularly when authentic data is limited. Additionally, the research introduces a countermeasure against the misuse of SD-generated images, utilizing Diffusion Reconstruction Error (DIRE). This DIRE-based approach enables the training of robust classifiers with lightweight deep models, achieving 100% accuracy in recognizing SD-generated images using MobilenetV3.

Key takeaway

For computer vision engineers developing indoor scene recognition systems with limited data, you should consider integrating Stable Diffusion-generated synthetic images for data augmentation. This approach can significantly enhance model robustness and performance. Furthermore, implement the Diffusion Reconstruction Error (DIRE) countermeasure to ensure the integrity of your training data and prevent misuse, as it reliably identifies synthetic images, even with lightweight models like MobilenetV3.

Key insights

Using Stable Diffusion for semantic-aware data augmentation effectively addresses indoor scene recognition data scarcity and enables robust synthetic image detection.

Principles

• Synthetic data from Stable Diffusion can augment limited real datasets.
• Diffusion Reconstruction Error (DIRE) detects SD-generated images reliably.
• Lightweight models can achieve high accuracy with DIRE.

Method

Generate diverse indoor scenes using Stable Diffusion for data augmentation. Implement Diffusion Reconstruction Error (DIRE) to identify SD-generated images, enabling robust classifier training with lightweight models like MobilenetV3.

In practice

• Augment indoor scene datasets with Stable Diffusion outputs.
• Employ DIRE to verify authenticity of generated images.
• Train lightweight models for efficient synthetic image detection.

Topics

• Indoor Scene Recognition
• Data Augmentation
• Stable Diffusion
• Diffusion Reconstruction Error
• MobilenetV3
• Synthetic Data Detection

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

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computer Vision and Pattern Recognition.