AdaptCLIP：适配CLIP以实现通用视觉异常检测
AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection
AI总结：
针对通用视觉异常检测现有方法灵活性不足的问题，提出AdaptCLIP方法，通过三个轻量适配器交替学习自适应表示、结合两类特征做对比学习，实现跨域零/少样本泛化，在12个基准上达SOTA。
摘要：
通用视觉异常检测旨在无需额外微调即可从全新或未见过的视觉域中识别异常，这在开放场景中至关重要。近期研究表明，CLIP等预训练视觉语言模型仅需零张或少量正常图像就展现出强泛化能力。然而，现有方法存在提示模板设计复杂、token交互繁琐或需额外微调的问题，导致灵活性受限。本研究提出一种简单有效的方法AdaptCLIP，基于两大核心见解：第一，应交替而非联合学习自适应视觉与文本表示；第二，查询图像与正常图像提示之间的对比学习应结合上下文特征与对齐残差特征，而非仅依赖残差特征。AdaptCLIP将CLIP模型作为基础服务，仅在其输入或输出端添加三个简单适配器：视觉适配器、文本适配器和提示-查询适配器。AdaptCLIP支持跨域的零样本/少样本泛化，且在基础数据集上训练后，在目标域上无需训练即可使用。AdaptCLIP在工业和医学领域的12个异常检测基准上取得了当前最优性能，显著优于现有主流方法。我们将在https://github.com/gaobb/AdaptCLIP上公开AdaptCLIP的代码与模型。
英文摘要：
Universal visual anomaly detection aims to identify anomalies from novel or unseen vision domains without additional fine-tuning, which is critical in open scenarios. Recent studies have demonstrated that pre-trained vision-language models like CLIP exhibit strong generalization with just zero or a few normal images. However, existing methods struggle with designing prompt templates, complex token interactions, or requiring additional fine-tuning, resulting in limited flexibility. In this work, we present a simple yet effective method called AdaptCLIP based on two key insights. First, adaptive visual and textual representations should be learned alternately rather than jointly. Second, comparative learning between query and normal image prompt should incorporate both contextual and aligned residual features, rather than relying solely on residual features. AdaptCLIP treats CLIP models as a foundational service, adding only three simple adapters, visual adapter, textual adapter, and prompt-query adapter, at its input or output ends. AdaptCLIP supports zero-/few-shot generalization across domains and possesses a training-free manner on target domains once trained on a base dataset. AdaptCLIP achieves state-of-the-art performance on 12 anomaly detection benchmarks from industrial and medical domains, significantly outperforming existing competitive methods. We will make the code and model of AdaptCLIP available at https://github.com/gaobb/AdaptCLIP.
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。
