title: MetaUAS: Universal Anomaly Segmentation with One-Prompt Meta-Learning
summary: Zero- and few-shot visual anomaly segmentation relies on powerful
  vision-language models that detect unseen anomalies using manually designed
  textual prompts. However, visual representations are inherently independent of
  language. In this paper, we explore the potential of a pure visual foundation
  model as an alternative to widely used vision-language models for universal
  visual anomaly segmentation. We present a novel paradigm that unifies anomaly
  segmentation into change segmentation. This paradigm enables us to leverage
  large-scale synthetic image pairs, featuring object-level and local region
  changes, derived from existing image datasets, which are independent of target
  anomaly datasets. We propose a one-prompt Meta-learning framework for Universal
  Anomaly Segmentation (MetaUAS) that is trained on this synthetic dataset and
  then generalizes well to segment any novel or unseen visual anomalies in the
  real world. To handle geometrical variations between prompt and query images,
  we propose a soft feature alignment module that bridges paired-image change
  perception and single-image semantic segmentation. This is the first work to
  achieve universal anomaly segmentation using a pure vision model without
  relying on special anomaly detection datasets and pre-trained visual-language
  models. Our method effectively and efficiently segments any anomalies with only
  one normal image prompt and enjoys training-free without guidance from
  language. Our MetaUAS significantly outperforms previous zero-shot, few-shot,
  and even full-shot anomaly segmentation methods. The code and pre-trained
  models are available at https://github.com/gaobb/MetaUAS.
paper_authors._id: 682547e6501a31b392e78f6a
paper_authors.name: Bin-Bin Gao
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2025-05-15T01:54:50.125Z
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_title: MetaUAS: Universal Anomaly Segmentation with One-Prompt Meta-Learning
paper_summary: Zero- and few-shot visual anomaly segmentation relies on powerful
  vision-language models that detect unseen anomalies using manually designed
  textual prompts. However, visual representations are inherently independent of
  language. In this paper, we explore the potential of a pure visual foundation
  model as an alternative to widely used vision-language models for universal
  visual anomaly segmentation. We present a novel paradigm that unifies anomaly
  segmentation into change segmentation. This paradigm enables us to leverage
  large-scale synthetic image pairs, featuring object-level and local region
  changes, derived from existing image datasets, which are independent of target
  anomaly datasets. We propose a one-prompt Meta-learning framework for Universal
  Anomaly Segmentation (MetaUAS) that is trained on this synthetic dataset and
  then generalizes well to segment any novel or unseen visual anomalies in the
  real world. To handle geometrical variations between prompt and query images,
  we propose a soft feature alignment module that bridges paired-image change
  perception and single-image semantic segmentation. This is the first work to
  achieve universal anomaly segmentation using a pure vision model without
  relying on special anomaly detection datasets and pre-trained visual-language
  models. Our method effectively and efficiently segments any anomalies with only
  one normal image prompt and enjoys training-free without guidance from
  language. Our MetaUAS significantly outperforms previous zero-shot, few-shot,
  and even full-shot anomaly segmentation methods. The code and pre-trained
  models are available at https://github.com/gaobb/MetaUAS.
paper_ai_summary: A pure vision model using meta-learning achieves universal anomaly segmentation without language prompts or specialized anomaly datasets.
paper_ai_keywords: visual anomaly segmentation zero-shot few-shot vision-language models meta-learning universal anomaly segmentation MetaUAS soft feature alignment change segmentation object-level changes local region changes semantic segmentation pre-trained