SimCLIP: Refining Image-Text Alignment with Simple Prompts for Zero-/Few-shot Anomaly Detection

C. Y. Deng; Haote Xu; Xiaolu Chen; Huiming Xu; Xiaotong Tu; Xinghao Ding; Yue Huang

doi:10.1145/3664647.3681376

ScienceGate Book Chapters

JOURNAL ARTICLE

SimCLIP: Refining Image-Text Alignment with Simple Prompts for Zero-/Few-shot Anomaly Detection

C. Y. Deng Haote Xu Xiaolu Chen Huiming Xu Xiaotong Tu Xinghao Ding Yue Huang

Year: 2024 Pages: 1761-1770

DOI: 10.1145/3664647.3681376

Get Full-Text PDF Get Analytical Report

Keywords:

Simple (philosophy) Shot (pellet) Zero (linguistics) Anomaly (physics) Computer science Refining (metallurgy) Anomaly detection Image (mathematics) Artificial intelligence Computer vision Pattern recognition (psychology) Physics Materials science Condensed matter physics

Metrics

Cited By

0.64

FWCI (Field Weighted Citation Impact)

Refs

0.70

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Anomaly Detection Techniques and Applications

Physical Sciences → Computer Science → Artificial Intelligence

Domain Adaptation and Few-Shot Learning

Physical Sciences → Computer Science → Artificial Intelligence

Multimodal Machine Learning Applications

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

SimCLIP: Refining Image-Text Alignment with Simple Prompts for Zero-/Few-shot Anomaly Detection

Metrics

Citation History

Topics

Related Documents

PromptAD: Zero-shot Anomaly Detection using Text Prompts

Zero/few-shot anomaly localisation using language prompts

DiCLIP: Integrating DINOv2 and CLIP for Zero-Shot and Few-Shot Anomaly Detection with Versatile Combination Prompts

Zero-shot Generalist Graph Anomaly Detection with Unified Neighborhood Prompts

Zero-shot Generalist Graph Anomaly Detection with Unified Neighborhood Prompts