AdapTS: 为多类和持续视觉异常检测设计的轻量级教师-学生方法
AdapTS: Lightweight Teacher-Student Approach for Multi-Class and Continual Visual Anomaly Detection
AI总结：
本文提出AdapTS，一种用于多类和持续学习的教师-学生框架，通过共享冻结主干和轻量级可训练适配器减少内存开销，实验证明其在边缘部署中的高效性。
摘要：
视觉异常检测（VAD）对工业检测至关重要，但现有方法多局限于单类场景，无法满足现实环境中的多类和持续学习需求。尽管教师-学生（TS）架构高效，但其在持续学习场景中尚未被探索。为此，我们提出AdapTS，一种统一的TS框架，专为多类和持续学习场景设计，优化了边缘部署。AdapTS通过使用单一共享冻结主干并注入轻量级可训练适配器，消除了对两种不同架构的需求。训练通过分割引导目标和合成Perlin噪声增强，而基于原型的任务识别机制在推理时动态选择适配器，准确率达99%。在MVTec AD和VisA上的实验表明，AdapTS在多类和持续学习场景中与现有TS方法性能相当，同时大幅减少内存开销。我们的最轻量级变体AdapTS-S仅需额外8 MB内存，比STFPM（95 MB）、RD4AD（360 MB）和DeSTSeg（1120 MB）分别减少13倍、48倍和149倍，使其成为复杂工业环境边缘部署的高效解决方案。
英文摘要：
Visual Anomaly Detection (VAD) is crucial for industrial inspection, yet most existing methods are limited to single-category scenarios, failing to address the multi-class and continual learning demands of real-world environments. While Teacher-Student (TS) architectures are efficient, they remain unexplored for the Continual Setting. To bridge this gap, we propose AdapTS, a unified TS framework designed for multi-class and continual settings, optimized for edge deployment. AdapTS eliminates the need for two different architectures by utilizing a single shared frozen backbone and injecting lightweight trainable adapters into the student pathway. Training is enhanced via a segmentation-guided objective and synthetic Perlin noise, while a prototype-based task identification mechanism dynamically selects adapters at inference with 99\% accuracy. Experiments on MVTec AD and VisA demonstrate that AdapTS matches the performance of existing TS methods across multi-class and continual learning scenarios, while drastically reducing memory overhead. Our lightest variant, AdapTS-S, requires only 8 MB of additional memory, 13x less than STFPM (95 MB), 48x less than RD4AD (360 MB), and 149x less than DeSTSeg (1120 MB), making it a highly scalable solution for edge deployment in complex industrial environments.
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。
