Vision-Language Models(VLMs)을 활용한Zero-Shot Anomaly Detection(ZSAD)을 수행한 논문들을 리뷰합니다.- 리뷰 내용에 관해 수정해야하거나, 궁금한 부분 있으시다면 이메일([email protected])을 통해 연락 부탁드립니다.
- [Youtube] 링크에는 서울대학교 산업공학과 DSBA 연구실 유튜브에서 직접 제작한 리뷰 영상을 포함시켰습니다.
- [Github] 링크의 경우 official code가 아닐 수 있습니다.
| Title | Description | Conference | Year | Review | arXiv | Github | Youtube |
|---|---|---|---|---|---|---|---|
| WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentation | WinCLIP |
CVPR | 2023 | [Review] | [arXiv] | [Github] | -- |
| AnoVL: Adapting Vision-Language Models for Unified Zero-shot Anomaly Localization | AnoVL |
arXiv | 2023 | [Review] | [arXiv] | [Github] | -- |
| AnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly Detection | AnomalyCLIP |
ICLR | 2024 | [Review] | [arXiv] | [Github] | [Youtube] |
| PromptAD: Learning Prompts with only Normal Samples for Few-Shot Anomaly Detection | PromptAD |
CVPR | 2024 | [Reivew] | [arXiv] | [Github] | -- |
| AdaCLIP: Adapting CLIP with Hybrid Learnable Prompts for Zero-Shot Anomaly Detection | AdaCLIP |
ECCV | 2024 | [Review] | [arXiv] | [Github] | [Youtube] |
| VCP-CLIP: A visual context prompting model for zero-shot anomaly segmentation | VCP-CLIP |
ECCV | 2024 | [Review] | [arXiv] | [Github] | -- |
| FiLo: Zero-Shot Anomaly Detection by Fine-Grained Description and High-Quality Localization | FiLo |
ACM MM | 2024 | [Review] | [arXiv] | [Github] | -- |
| Fine-grained Abnormality Prompt Learning for Zero-Shot Anomaly Detection | FAPrompt |
arXiv | 2024 | [Review] | [arXiv] | [Github] | -- |
- Do LLMs Understand Visual Anomalies? Uncovering LLM Capabilities in Zero-shot Anomaly Detection(ALFA)