zero-shot anomaly detection
19 papers with code • 2 benchmarks • 2 datasets
MVTecAD
VisA
Most implemented papers
WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentation
openvinotoolkit/anomalib
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CVPR 2023
Visual anomaly classification and segmentation are vital for automating industrial quality inspection.
AnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly Detection
It is a crucial task when training data is not accessible due to various concerns, eg, data privacy, yet it is challenging since the models need to generalize to anomalies across different domains where the appearance of foreground objects, abnormal regions, and background features, such as defects/tumors on different products/organs, can vary significantly.
Towards Zero-shot Point Cloud Anomaly Detection: A Multi-View Projection Framework
Following zero-shot image anomaly detection methods, pre-trained VLMs are utilized to detect anomalies on these depth images.
MAEDAY: MAE for few and zero shot AnomalY-Detection
We propose using Masked Auto-Encoder (MAE), a transformer model self-supervisedly trained on image inpainting, for anomaly detection (AD).
Zero-Shot Anomaly Detection via Batch Normalization
aodongli/zero-shot-ad-via-batch-norm
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NeurIPS 2023
Anomaly detection (AD) plays a crucial role in many safety-critical application domains.
Bootstrap Fine-Grained Vision-Language Alignment for Unified Zero-Shot Anomaly Localization
hq-deng/AnoVL
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On top of the proposed AnoCLIP, we further introduce a test-time adaptation (TTA) mechanism to refine visual anomaly localization results, where we optimize a lightweight adapter in the visual encoder using AnoCLIP's pseudo-labels and noise-corrupted tokens.
GPT-4V-AD: Exploring Grounding Potential of VQA-oriented GPT-4V for Zero-shot Anomaly Detection
Large Multimodal Model (LMM) GPT-4V(ision) endows GPT-4 with visual grounding capabilities, making it possible to handle certain tasks through the Visual Question Answering (VQA) paradigm.
VisionGPT: LLM-Assisted Real-Time Anomaly Detection for Safe Visual Navigation
This paper explores the potential of Large Language Models(LLMs) in zero-shot anomaly detection for safe visual navigation.
FiLo: Zero-Shot Anomaly Detection by Fine-Grained Description and High-Quality Localization
Zero-shot anomaly detection (ZSAD) methods entail detecting anomalies directly without access to any known normal or abnormal samples within the target item categories.
From Zero to Hero: Cold-Start Anomaly Detection
This paper studies the realistic but underexplored cold-start setting where an anomaly detection model is initialized using zero-shot guidance, but subsequently receives a small number of contaminated observations (namely, that may include anomalies).
