title: AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection
summary: 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.
paper_authors._id: 68268c3408f7cb26defd82fc
paper_authors.name: Bin-Bin Gao
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-16T14:18:39.295Z
paper_authors._id: 648972ff99f6c45ff6bbd295
paper_authors.avatarUrl: /avatars/d1fe9d631a786462d55cfe43c565c88e.svg
paper_authors.fullname: Bin-Bin Gao
paper_authors.type: user
paper_authors.user: csgaobb
paper_authors._id: 68268c3408f7cb26defd82fd
paper_authors.name: Yue Zhu
paper_authors._id: 68268c3408f7cb26defd82fe
paper_authors.name: Jiangtao Yan
paper_authors._id: 68268c3408f7cb26defd82ff
paper_authors.name: Yuezhi Cai
paper_authors._id: 68268c3408f7cb26defd8300
paper_authors.name: Weixi Zhang
paper_authors._id: 68268c3408f7cb26defd8301
paper_authors.name: Meng Wang
paper_authors._id: 68268c3408f7cb26defd8302
paper_authors.name: Jun Liu
paper_authors._id: 68268c3408f7cb26defd8303
paper_authors.name: Yong Liu
paper_authors._id: 68268c3408f7cb26defd8304
paper_authors.name: Lei Wang
paper_authors._id: 68268c3408f7cb26defd8305
paper_authors.name: Chengjie Wang
paper_title: AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection
paper_summary: 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.
paper_ai_summary: AdaptCLIP uses adaptive representations and comparative learning to achieve zero-/few-shot anomaly detection across domains without additional fine-tuning.
paper_ai_keywords: pre-trained vision-language models CLIP adaptive visual representations adaptive textual representations comparative learning contextual features aligned residual features visual adapter textual adapter prompt-query adapter