FIDeL detects failures in imitation learning by building compact nominal representations via optimal transport, applying conformal prediction thresholds, and using VLMs for semantic filtering, outperforming baselines by 5.3% AUROC and 17.38% accuracy on the new BotFails dataset.
Fastflow: Unsupervised anomaly detection and localization via 2d normalizing flows
7 Pith papers cite this work. Polarity classification is still indexing.
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Introduces scenarios and metrics for ambiguous normal samples in anomaly detection plus RePaste method achieving SOTA on the new metric on MVTec AD while retaining high AUROC and PRO.
A training-free method fits PCA to DINOv2 features from few normal images and detects anomalies via reconstruction residual, reaching SOTA one-shot AUROC of 97.1% image-level on MVTec-AD and 93.2% on VisA.
A new framework learns low-dimensional subspaces from nominal samples and reconstructs target deep embeddings via self-expressive linear combinations to localize anomalies, claiming SOTA on three benchmarks.
IndusAgent achieves state-of-the-art zero-shot performance on industrial anomaly benchmarks by using a custom Indus-CoT dataset, dynamic tool orchestration, and gated RL to optimize anomaly classification, localization, and reasoning.
ZSG-IAD is a zero-shot multimodal system that uses language-guided two-hop grounding and rule-based reinforcement learning to produce anomaly masks and explainable reports from industrial sensor data.
Unsupervised anomaly detection with pre-trained Anomalib models achieves F1 macro score over 0.95 on Raspberry Pi using 10 images and 90 seconds training time.
citing papers explorer
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Failure Identification in Imitation Learning Via Statistical and Semantic Filtering
FIDeL detects failures in imitation learning by building compact nominal representations via optimal transport, applying conformal prediction thresholds, and using VLMs for semantic filtering, outperforming baselines by 5.3% AUROC and 17.38% accuracy on the new BotFails dataset.
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Novel Anomaly Detection Scenarios and Evaluation Metrics to Address the Ambiguity in the Definition of Normal Samples
Introduces scenarios and metrics for ambiguous normal samples in anomaly detection plus RePaste method achieving SOTA on the new metric on MVTec AD while retaining high AUROC and PRO.
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SubspaceAD: Training-Free Few-Shot Anomaly Detection via Subspace Modeling
A training-free method fits PCA to DINOv2 features from few normal images and detects anomalies via reconstruction residual, reaching SOTA one-shot AUROC of 97.1% image-level on MVTec-AD and 93.2% on VisA.
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Subspace-Guided Feature Reconstruction for Unsupervised Anomaly Localization
A new framework learns low-dimensional subspaces from nominal samples and reconstructs target deep embeddings via self-expressive linear combinations to localize anomalies, claiming SOTA on three benchmarks.
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IndusAgent: Reinforcing Open-Vocabulary Industrial Anomaly Detection with Agentic Tools
IndusAgent achieves state-of-the-art zero-shot performance on industrial anomaly benchmarks by using a custom Indus-CoT dataset, dynamic tool orchestration, and gated RL to optimize anomaly classification, localization, and reasoning.
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ZSG-IAD: A Multimodal Framework for Zero-Shot Grounded Industrial Anomaly Detection
ZSG-IAD is a zero-shot multimodal system that uses language-guided two-hop grounding and rule-based reinforcement learning to produce anomaly masks and explainable reports from industrial sensor data.
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Leveraging Unsupervised Learning for Cost-Effective Visual Anomaly Detection
Unsupervised anomaly detection with pre-trained Anomalib models achieves F1 macro score over 0.95 on Raspberry Pi using 10 images and 90 seconds training time.