基于视觉变换器的隐私保护服装分类用于热舒适度估计
Privacy-Preserving Clothing Classification using Vision Transformer for Thermal Comfort Estimation
AI总结：
本文提出一种隐私保护的服装分类方法，利用视觉变换器（ViT）进行服装隔热估计，解决了传统方法在隐私保护下的准确性下降问题。
摘要：
本文提出了一种隐私保护的服装分类方案，以实现安全的居所中心控制（OCC）系统。尽管利用相机图像优化热舒适度的HVAC控制已广泛研究，但此前工作未考虑 occupant 图像的隐私保护。虽然已提出多种隐私保护方法用于图像分类，但传统方案会导致严重准确性下降。本文引入了基于视觉变换器（ViT）的隐私保护分类方法用于服装隔热估计。在使用按服装隔热分类的DeepFashion数据集进行实验时，传统基于像素的方法导致严重准确性下降，而本文方案在加密图像上保持高准确性，所有类别均无降级。
英文摘要：
A privacy-preserving clothing classification scheme is presented to enable secure occupant-centric control (OCC) systems. Although the utilization of camera images for HVAC control has been widely studied to optimize thermal comfort, privacy protection of occupant images has not been considered in prior works. While various privacy-preserving methods have been proposed for image classification, applying conventional schemes results in severe accuracy degradation. In this paper, we introduce a privacy-preserving classification method using Vision Transformer (ViT) applied to clothing insulation estimation. In an experiment using the DeepFashion dataset categorized by clothing insulation, while the conventional pixel-based method suffers a severe accuracy drop, our scheme maintains a high accuracy on encrypted images, showing no degradation from plain images across all categories.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
