AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection
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.
68268c3408f7cb26defd82fc
Bin-Bin Gao
admin_assigned
2025-05-16T14:18:39.295Z
648972ff99f6c45ff6bbd295
/avatars/d1fe9d631a786462d55cfe43c565c88e.svg
Bin-Bin Gao
user
csgaobb
68268c3408f7cb26defd82fd
Yue Zhu
68268c3408f7cb26defd82fe
Jiangtao Yan
68268c3408f7cb26defd82ff
Yuezhi Cai
68268c3408f7cb26defd8300
Weixi Zhang
68268c3408f7cb26defd8301
Meng Wang
68268c3408f7cb26defd8302
Jun Liu
68268c3408f7cb26defd8303
Yong Liu
68268c3408f7cb26defd8304
Lei Wang
68268c3408f7cb26defd8305
Chengjie Wang
AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection
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.
AdaptCLIP uses adaptive representations and comparative learning to achieve zero-/few-shot anomaly detection across domains without additional fine-tuning.
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