# UniSLAD: A Unified Framework for Structural and Logical Industrial Visual Anomaly Detection

arXiv: https://arxiv.org/abs/2606.20768 · HTML: https://mlsanity.com/paper/2606.20768/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 26 references that are in the map.
- First posted on arXiv 18 Jun 2026.

## Builds on (most cited first, 18 of 18)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detection](https://mlsanity.com/paper/2108.07610/) (2021)
- [Anomaly Detection via Reverse Distillation from One-Class Embedding](https://mlsanity.com/paper/2201.10703/) (2022)
- [EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies](https://mlsanity.com/paper/2303.14535/) (2023)
- [Towards Training-free Anomaly Detection with Vision and Language Foundation Models](https://mlsanity.com/paper/2503.18325/) (2025)
- [LogicAD: Explainable Anomaly Detection via VLM-based Text Feature Extraction](https://mlsanity.com/paper/2501.01767/) (2025)
- [Component-aware anomaly detection framework for adjustable and logical industrial visual inspection](https://mlsanity.com/paper/2305.08509/) (2023)
- [Learning Global-Local Correspondence with Semantic Bottleneck for Logical Anomaly Detection](https://mlsanity.com/paper/2303.05768/) (2023)
- [Few Shot Part Segmentation Reveals Compositional Logic for Industrial Anomaly Detection](https://mlsanity.com/paper/2312.13783/) (2023)
- [Contextual Affinity Distillation for Image Anomaly Detection](https://mlsanity.com/paper/2307.03101/) (2023)
- [Generating and Reweighting Dense Contrastive Patterns for Unsupervised Anomaly Detection](https://mlsanity.com/paper/2312.15911/) (2023)
- [SAM-LAD: Segment Anything Model Meets Zero-Shot Logic Anomaly Detection](https://mlsanity.com/paper/2406.00625/) (2024)
- [CSAD: Unsupervised Component Segmentation for Logical Anomaly Detection](https://mlsanity.com/paper/2408.15628/) (2024)
- [SLSG: Industrial Image Anomaly Detection by Learning Better Feature Embeddings and One-Class Classification](https://mlsanity.com/paper/2305.00398/) (2023)
- [PUAD: Frustratingly Simple Method for Robust Anomaly Detection](https://mlsanity.com/paper/2402.15143/) (2024)
- [Visual Anomaly Detection via Dual-Attention Transformer and Discriminative Flow](https://mlsanity.com/paper/2303.17882/) (2023)
- [Set Features for Anomaly Detection](https://mlsanity.com/paper/2311.14773/) (2023)

## Built on by (most cited first, 1 of 1)

- [Training-Free Logical and Structural Anomaly Detection via Calibrated Fusion](https://mlsanity.com/paper/2609.05091/) (2026)

## Abstract (arXiv)

Visual anomaly detection is a fundamental task in industrial automation. While existing approaches have achieved notable progress in identifying structural defects, the detection of logical anomalies remains relatively underexplored. In practice, structural and logical anomalies frequently co-occur in industrial workflows. Therefore, a solution capable of detecting both structural and logical anomalies is crucial for advancing comprehensive anomaly detection research. To address this limitation, we propose a unified framework, termed UniSLAD, which jointly addresses logical and structural anomalies without additional training, enabling a practical solution for dynamic industrial environments. First, we introduce a dual-feature extractor that synergistically integrates a Convolutional Neural Network (CNN) backbone for local texture perception with a Transformer backbone for global contextual reasoning, yielding richer and more comprehensive representations. Building on this foundation, we design dual-granularity feature representation modules. At the patch level, memory banks enhanced by the Mahalanobis Transform (MT) preserve representative features and support more discriminative anomaly scoring. At the image level, distribution maps are aggregated using Lower-Upper Mean (LUM) and Power Mean Pooling (PMP), yielding a more robust global representation than conventional average pooling. Extensive experiments on the two industrial benchmarks demonstrate that UniSLAD achieves competitive performance in comprehensive anomaly detection, achieving 99.4% and 93.1%, respectively. Furthermore, ablation studies verify the individual contributions and effectiveness of each proposed component.