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Hugging Face Trending Papers

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

Vision foundation models have enabled strong training-free anomaly detection (AD). However, most existing approaches rely primarily on independent local patch features, leaving the global contextual information encoded by Vision Transformers (ViTs) underexploited.

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arXiv Computer Vision
Sep 4

PL-SCEA: Reconfiguring Pretrained Attention for Few-Shot Industrial Anomaly Detection

The paper introduces PL‑SCEA, a method that reconfigures the attention mechanism of frozen Vision Foundation Models to better detect and localize anomalies in industrial images with few training examples. PL‑SCEA preserves the semantic context of pretrained query‑key attention while adding token‑adaptive self‑correlations over contextualized value features, then applies positive‑correlation filtering and power‑law reweighting to highlight task‑relevant relationships. The resulting features are fed into a lightweight variational autoencoder to produce reconstruction‑based anomaly scores, achieving competitive image‑level detection and strong pixel‑level localization on MVTec AD and VisA datasets.

By Xiaoyu Yang, Qixing Wu, Huixian Zhao, Changlong Jin
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arXiv Computer Vision
Aug 27

See More, Detect Less? Taming Information Leakage in Multi-View Anomaly Detection

The paper identifies a problem in multi‑view anomaly detection called cross‑view information leakage, where fusing multiple inspection views can cause normal features to mask anomalies during reconstruction. To address this, the authors propose GLAD, a framework that uses a Global‑Local Attention Driven approach, combining vision foundation model features with two fusion modules: Multi‑view Merging Attention for local, weighted fusion and Object‑Guided Attention for global context aggregation. Experiments on Real‑IAD and MANTA‑Tiny demonstrate that GLAD outperforms existing methods across various metrics, underscoring the importance of restricting information flow to preserve the reconstruction gap.

By Shang-Fu Chen, Kuan-Chuan Peng, Jhih-Ciang Wu, Wen-Huang Cheng, Kai-Lung Hua
benchmarks
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arXiv Computer Vision
Aug 24

Crane: Context-Guided Prompt Learning and Attention Refinement for Zero-Shot Anomaly Detection

Crane is a CLIP‑based framework for zero‑shot anomaly detection that enhances dense localization by adapting the vision encoder with a correlation‑based attention module and conditioning learnable prompts on global image context. It further fuses anomaly‑relevant patch features into the global representation for more sensitive image‑level detection, and a variant called Crane+ leverages DINOv2 spatial correlations for stronger pixel‑level performance. Across seven industrial benchmarks, Crane raises mean image‑level AP by 4.5% and Crane+ boosts mean pixel‑level AUPRO by 9.0%.

By Alireza Salehi, Mohammadreza Salehi, Reshad Hosseini, Cees G. M. Snoek, Makoto Yamada, Mohammad Sabokrou
multimodalbenchmarkssafety
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arXiv Computer Vision
Sep 7

Training-Free Logical and Structural Anomaly Detection via Calibrated Fusion

The paper introduces a training‑free anomaly detector that simultaneously handles structural and logical defects by calibrating heterogeneous anomaly cues with statistics from normal images. This calibration aligns frozen representations, allowing their fusion without extra training or part‑level supervision. The resulting method achieves state‑of‑the‑art AUROC scores on MVTec‑LOCO and remains competitive on MVTec‑AD.

By Changyi Li, Miao Yu, Kai Dong, Yu Xiao
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