Hugging Face Trending Papers June 28, 2026

Anomaly Factory 3D: A Modular Framework for Diverse Pseudo-Anomaly Synthesis in Unsupervised 3D Anomaly Detection

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Detecting and localizing defects in 3D point clouds is challenging because abnormal samples are scarce and diverse, while training is often limited to normal data. We propose Anomaly Factory 3D (AF3AD), a modular framework that synthesizes diverse pseudo-anomalies from normal point clouds to expand the training data for unsupervised 3D anomaly detection methods that rely on pseudo-anomalies.

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arXiv AI

Jun 30

Anomaly Factory 3D: A Modular Framework for Diverse Pseudo-Anomaly Synthesis in Unsupervised 3D Anomaly Detection

arXiv:2606. 29181v1 Announce Type: cross Abstract: Detecting and localizing defects in 3D point clouds is challenging because abnormal samples are scarce and diverse, while training is often limited to normal data.

By Ali Balapour, Faraz Hach

arXiv AI

Jul 21

O-VAD: Industrial Video Anomaly Detection through Object-Centric Tracking and Reasoning

arXiv:2607. 18142v1 Announce Type: cross Abstract: Industrial Video Anomaly Detection (IVAD) aims to identify anomalous objects and events in an industrial process, which is crucial for modern manufacturing and quality control systems.

By Mei Yuan, Qi Long, Qifeng Wu, Zhenyang Li, Yizhou Zhao, Lei Wang, Yang Liu, Min Xu

agents fine-tuning multimodal

arXiv Machine Learning

Jun 16

Anomaly Detection via Mean Shift Density Enhancement

arXiv:2602. 03293v2 Announce Type: replace Abstract: Unsupervised anomaly detection stands as an important problem in machine learning.

By Pritam Kar, Rahul Bordoloi, Olaf Wolkenhauer, Saptarshi Bej

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Hugging Face Trending Papers

Jul 2

LiZAD: A Lightweight Zero-Shot Anomaly Detection Framework for Industrial Manufacturing

In modern high-throughput industrial production lines, product configurations and visual characteristics frequently change, making it impractical to collect and annotate data for every new scenario. This dynamic setting makes Zero-Shot Anomaly Detection (ZSAD) particularly suitable, as it enables defect detection without requiring training on target-specific samples.

rag diffusion efficiency benchmarks

arXiv Machine Learning

Jun 16

Rethinking Structural Anomaly Detection: From Decision Boundaries to Projection Operators

arXiv:2606. 15280v1 Announce Type: new Abstract: Most existing anomaly detection methods rely on estimating a probability density or learning an enclosing decision boundary, implicitly assuming that normal data occupies a region of non-zero volume in the ambient space.

By Alexander Bauer

safety

arXiv AI

Jul 28

DuoAD: Leveraging [CLS] Dual Characteristics for Training-Free Few-Shot Anomaly Detection

arXiv:2607. 23924v1 Announce Type: cross Abstract: Vision foundation models have enabled strong training-free anomaly detection (AD).

By Jyun-Ze Tang, Po-Han Huang, Ming-Ching Chang, Chih-Fan Hsu, Jeng-Lin Li

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Related stories

Anomaly Factory 3D: A Modular Framework for Diverse Pseudo-Anomaly Synthesis in Unsupervised 3D Anomaly Detection

O-VAD: Industrial Video Anomaly Detection through Object-Centric Tracking and Reasoning

Anomaly Detection via Mean Shift Density Enhancement

LiZAD: A Lightweight Zero-Shot Anomaly Detection Framework for Industrial Manufacturing

Rethinking Structural Anomaly Detection: From Decision Boundaries to Projection Operators

DuoAD: Leveraging [CLS] Dual Characteristics for Training-Free Few-Shot Anomaly Detection