Paper-Review

• DSBA 연구실 석사과정 동안 리뷰를 한 논문을 정리합니다.
  • Image Anomaly Detection
  • Prompt Learning
  • Vision-Language Models(VLMs)
• 리뷰 내용에 관해 수정해야하거나, 궁금한 부분 있으시다면 이메일(junyeong_son@korea.ac.kr)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]	[Youtube]
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]	[Youtube]

To DO

• [ ]

Reference

• https://github.com/M-3LAB/awesome-industrial-anomaly-detection