zero-shot anomaly detection

Benchmarks archive 2025-07-28

No benchmark for this task in the archive.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

2 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Most implemented papers archive 2025-07-28

19 shown of 19 papers with code (35 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentation
  26 Mar 2023 4 repositories listed Syntology ran 5 of 12 samples · 7 unverified
  Visual anomaly classification and segmentation are vital for automating industrial quality inspection.
• AnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly Detection
  29 Oct 2023 3 repositories listed Syntology ran 14 of 32 samples · 18 unverified
  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…
• Towards Zero-shot Point Cloud Anomaly Detection: A Multi-View Projection Framework
  20 Sep 2024 2 repositories listed
  Following zero-shot image anomaly detection methods, pre-trained VLMs are utilized to detect anomalies on these depth images.
• Crane: Context-Guided Prompt Learning and Attention Refinement for Zero-Shot Anomaly Detections
  15 Apr 2025 1 repository listed
  Anomaly Detection (AD) involves identifying deviations from normal data distributions and is critical in fields such as medical diagnostics and industrial defect detection.
• Bayesian Prompt Flow Learning for Zero-Shot Anomaly Detection
  13 Mar 2025 1 repository listed Syntology ran 8 of 12 samples · 4 unverified · 12 pointer-only (licence)
  Specifically, a prompt flow module is designed to learn both image-specific and image-agnostic distributions, which are jointly utilized to regularize the text prompt space and enhance the model's generalization on…
• AA-CLIP: Enhancing Zero-shot Anomaly Detection via Anomaly-Aware CLIP
  9 Mar 2025 1 repository listed
  To address this problem, we propose Anomaly-Aware CLIP (AA-CLIP), which enhances CLIP's anomaly discrimination ability in both text and visual spaces while preserving its generalization capability.
• GlocalCLIP: Object-agnostic Global-Local Prompt Learning for Zero-shot Anomaly Detection
  9 Nov 2024 1 repository listed
  Finally, we introduce glocal contrastive learning to improve the complementary learning of global and local prompts, effectively detecting anomalous patterns across various domains.
• AnomalyNCD: Towards Novel Anomaly Class Discovery in Industrial Scenarios
  18 Oct 2024 1 repository listed
  Previous methods directly cluster anomalies but often struggle due to the lack of anomaly-prior knowledge.
• Fine-grained Abnormality Prompt Learning for Zero-shot Anomaly Detection
  14 Oct 2024 1 repository listed
  To this end, we introduce a novel compound abnormality prompting module in FAPrompt to learn a set of complementary, decomposed abnormality prompts, where each abnormality prompt is formed by a compound of shared normal…
• FADE: Few-shot/zero-shot Anomaly Detection Engine using Large Vision-Language Model
  31 Aug 2024 1 repository listed
  In this paper, we propose the Few-shot/zero-shot Anomaly Detection Engine (FADE) which leverages the vision-language CLIP model and adjusts it for the purpose of industrial anomaly detection.
• AdaCLIP: Adapting CLIP with Hybrid Learnable Prompts for Zero-Shot Anomaly Detection
  22 Jul 2024 1 repository listed Syntology ran 19 of 28 samples · 9 unverified
  Two types of learnable prompts are proposed: static and dynamic.
• VCP-CLIP: A visual context prompting model for zero-shot anomaly segmentation
  17 Jul 2024 1 repository listed
  In this end, we propose a visual context prompting model (VCP-CLIP) for ZSAS task based on CLIP.
• From Zero to Hero: Cold-Start Anomaly Detection
  30 May 2024 1 repository listed
  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…
• FiLo: Zero-Shot Anomaly Detection by Fine-Grained Description and High-Quality Localization
  21 Apr 2024 1 repository listed Syntology ran 5 of 12 samples · 7 unverified
  Zero-shot anomaly detection (ZSAD) methods entail detecting anomalies directly without access to any known normal or abnormal samples within the target item categories.
• VisionGPT: LLM-Assisted Real-Time Anomaly Detection for Safe Visual Navigation
  19 Mar 2024 1 repository listed
  This paper explores the potential of Large Language Models(LLMs) in zero-shot anomaly detection for safe visual navigation.
• GPT-4V-AD: Exploring Grounding Potential of VQA-oriented GPT-4V for Zero-shot Anomaly Detection
  5 Nov 2023 1 repository listed
  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.
• Bootstrap Fine-Grained Vision-Language Alignment for Unified Zero-Shot Anomaly Localization
  30 Aug 2023 1 repository listed Syntology ran 4 of 11 samples · 7 unverified
  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…
• Zero-Shot Anomaly Detection via Batch Normalization
  15 Feb 2023 1 repository listed Syntology ran 1 of 1 samples · 0 unverified
  Anomaly detection (AD) plays a crucial role in many safety-critical application domains.
• MAEDAY: MAE for few and zero shot AnomalY-Detection
  25 Nov 2022 1 repository listed
  We propose using Masked Auto-Encoder (MAE), a transformer model self-supervisedly trained on image inpainting, for anomaly detection (AD).

Syntology lines on 7 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.