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Hugging Face Trending Papers

CDGP: Contrastive Dual Gaussian Processes for Weakly Supervised Anomaly Segmentation

The paper introduces Contrastive Dual Gaussian Processes (CDGP), a weakly supervised approach for anomaly segmentation in industrial visual inspection. CDGP models normal and anomaly predictive distributions over dense tokens, using a posterior-dominance statistic that normalizes predictive-mean differences by joint uncertainty to provide spatial evidence and image-level confidence. The method achieves top performance on MVTec AD~2, KSDD2, and VisA datasets without requiring pixel-level annotations or test-time fitting.

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Hugging Face Trending Papers
Jul 14

Statistical Non-linear Reconstruction Loss for Image Anomaly Detection

Reconstruction-based methods are a cornerstone of unsupervised image anomaly detection, but they remain vulnerable to \emph{outlier leakage}, where standard mean squared error (MSE) loss drives the model to faithfully reconstruct anomalous patterns. We propose a Non-linear Reconstruction Loss that applies a sigmoid-based squashing function to suppress high-magnitude features, preventing outliers from dominating optimization while preserving sensitivity to normal patterns.

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arXiv Computer Vision
Sep 4

ISP-AD: A Large-Scale Real-World Dataset for Advancing Industrial Anomaly Detection with Synthetic and Real Defects

The paper introduces ISP-AD, the largest publicly available industrial anomaly detection dataset, featuring both synthetic and real defects from a factory floor. It focuses on challenging, small, weakly contrasted surface defects within highly variable structured patterns, addressing the bias of existing datasets toward ideal imaging conditions. Experiments demonstrate that even a small amount of weakly labeled real defects improves model generalization and that synthetic defects can serve as a useful cold‑start baseline for scalable training.

By Paul J. Krassnig, Dieter P. Gruber
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arXiv Machine Learning
Oct 8

Efficient Patch-Based Anomaly Detection Fused with Diffusion Driven Generative Modeling for Semiconductor Wafer Bin Map Open Set Anomaly Detection

The paper introduces a hybrid one‑class anomaly detector for semiconductor wafer bin maps that combines a patch‑based student‑teacher model (EfficientAD) with a denoising diffusion probabilistic model (DDPM). Trained on only 700 normal wafers, the fused system achieves an AUROC of 0.9985 on 18,658 held‑out wafers, reducing misclassifications from 852 (DDPM) and 1,412 (EfficientAD) to 618, with statistically significant improvements. Analysis reveals that the two modules make weakly overlapping errors, and that fixed‑weight fusion captures only 40‑70 % of the potential correction, highlighting the need for sample‑adaptive fusion and imbalance‑aware evaluation.

By Limon Bin Hossain, Md Sadib Rahman Ananta
diffusionbenchmarks
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arXiv AI
Oct 1

TED:Text-Axis Evidence Decomposition for Prompted Anomaly Localization

The paper introduces TED (Text-Axis Evidence Decomposition), a post‑hoc scoring method that improves anomaly localization in CLIP‑based detectors without altering the backbone or prompts. TED evaluates whether ambiguous responses are better supported by defect patches or normal patches, thereby distinguishing true defects from visually complex normal regions. Experiments show that TED significantly enhances pixel‑level localization across frozen VLM backbones and adapted hosts, especially under hard‑false‑positive competition.

By JinYoung Kim, Geonho Kim, GiJeong Park, Geonu Lee, YoungJoon Yoo
llmsmultimodal
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