conftrace_

few-shot anomaly detection

11 papers

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Also known as

FSAD

Co-occurring keywords · papers mentioning both

vision-language model (6) anomaly segmentation (3) industrial anomaly detection (3) prompt learning (3) anomaly detection (3) few-shot learning (3) graph neural network (2) multimodal learning (2) anomaly personalization (1) visual anomaly detection (1)

Papers

Commonality in Few: Few-Shot Multimodal Anomaly Detection via Hypergraph-Enhanced Memory AAAI 2026 DE-CLIP: Few-Shot Anomaly Detection via Difference-Guided Embedding Editing ACL 2026 FastRef: Fast Prototype Refinement for Few-shot Industrial Anomaly Detection CVPR 2026 Sea-CLIP: Mining Semantic-Aware Representations for Few-Shot Anomaly Detection with CLIP WACV 2026 Kernel-Aware Graph Prompt Learning for Few-Shot Anomaly Detection AAAI 2025 One-for-All Few-Shot Anomaly Detection via Instance-Induced Prompt Learning ICLR 2025 UniVAD: A Training-free Unified Model for Few-shot Visual Anomaly Detection CVPR 2025 Multimodal Industrial Anomaly Detection by Crossmodal Feature Mapping CVPR 2024 One-to-Normal: Anomaly Personalization for Few-shot Anomaly Detection NeurIPS 2024 PromptAD: Learning Prompts with only Normal Samples for Few-Shot Anomaly Detection CVPR 2024 Pushing the Limits of Fewshot Anomaly Detection in Industry Vision: Graphcore ICLR 2023