用视觉变压器实现穿衣分类，保护隐私同时保持高精度。
Privacy-Preserving Clothing Classification using Vision Transformer for Thermal Comfort Estimation
- 采用视觉变压器处理加密图像，实现隐私保护下的穿衣分类
- 在DeepFashion数据集上，加密图像分类准确率与原始图像无差异
- 适合需要保护用户隐私的智能空调控制系统
提出一种隐私保护型衣物分类方案，用于支持以用户为中心的环境控制（OCC）系统。尽管摄像头图像在暖通空调（HVAC）控制中被广泛用于优化热舒适性，但以往研究未考虑对人员图像的隐私保护。虽然已有多种隐私保护图像分类方法，但传统方案会导致显著的准确率下降。本文引入基于视觉变压器（ViT）的隐私保护分类方法，应用于衣物隔热估算。在使用按衣物隔热等级划分的DeepFashion数据集进行的实验中，传统像素级方法在加密图像上出现严重准确率下降，而本方案在所有类别上均保持高准确率，加密图像与原始图像的性能无差异。
原文摘要 · Abstract (English)
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.
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