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Stable Diffusion used for data augmentation in indoor scene recognition

By PulseAugur Editorial · [1 sources] ·

Researchers have proposed a new method for indoor scene recognition by using Stable Diffusion to generate synthetic images for data augmentation. This approach addresses the scarcity of training data for indoor environments, which are often complex due to lighting and object arrangements. The study demonstrates that this technique can enhance the training of deep learning models, particularly when using limited real-world data. Additionally, a countermeasure called Diffusion Reconstruction Error (DIRE) was developed to detect synthetic images generated by Stable Diffusion, achieving 100% accuracy in identifying them with a MobileNetV3 model. AI

IMPACT This research could improve the training of AI models for specialized environments with limited data, while also providing tools to detect synthetic media.

RANK_REASON Academic paper detailing a new method for data augmentation and detection. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Stable Diffusion used for data augmentation in indoor scene recognition

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Academic paper detailing a new method for data augmentation and detection. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Trung-Nghia Le ·

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

    In the realm of computer vision, indoor image recognition presents challenges due to the intricate interplay of lighting conditions, occlusions, and diverse object arrangements within confined spaces. To address the lacks of training indoor images, we introduce a novel approach l…

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