# Component-aware anomaly detection framework for adjustable and logical industrial visual inspection

arXiv: https://arxiv.org/abs/2305.08509 · HTML: https://mlsanity.com/paper/2305.08509/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 48 references that are in the map.
- First posted on arXiv 15 May 2023.

## Builds on (most cited first, 28 of 28)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [Towards Total Recall in Industrial Anomaly Detection](https://mlsanity.com/paper/2106.08265/) (2021)
- [CutPaste: Self-Supervised Learning for Anomaly Detection and Localization](https://mlsanity.com/paper/2104.04015/) (2021)
- [DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detection](https://mlsanity.com/paper/2108.07610/) (2021)
- [Invariant Information Clustering for Unsupervised Image Classification and Segmentation](https://mlsanity.com/paper/1807.06653/) (2018)
- [Anomaly Detection via Reverse Distillation from One-Class Embedding](https://mlsanity.com/paper/2201.10703/) (2022)
- [Uninformed Students: Student-Teacher Anomaly Detection with Discriminative Latent Embeddings](https://mlsanity.com/paper/1911.02357/) (2019)
- [Sub-Image Anomaly Detection with Deep Pyramid Correspondences](https://mlsanity.com/paper/2005.02357/) (2020)
- [Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders](https://mlsanity.com/paper/1807.02011/) (2018)
- [Unsupervised Semantic Segmentation by Distilling Feature Correspondences](https://mlsanity.com/paper/2203.08414/) (2022)
- [Cut and Learn for Unsupervised Object Detection and Instance Segmentation](https://mlsanity.com/paper/2301.11320/) (2023)
- [PiCIE: Unsupervised Semantic Segmentation using Invariance and Equivariance in Clustering](https://mlsanity.com/paper/2103.17070/) (2021)
- [EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies](https://mlsanity.com/paper/2303.14535/) (2023)
- [Multimodal Industrial Anomaly Detection via Hybrid Fusion](https://mlsanity.com/paper/2303.00601/) (2023)
- [Fully Convolutional Cross-Scale-Flows for Image-based Defect Detection](https://mlsanity.com/paper/2110.02855/) (2021)
- [Asymmetric Student-Teacher Networks for Industrial Anomaly Detection](https://mlsanity.com/paper/2210.07829/) (2022)
- [Modeling the Distribution of Normal Data in Pre-Trained Deep Features for Anomaly Detection](https://mlsanity.com/paper/2005.14140/) (2020)
- [Mixed supervision for surface-defect detection: from weakly to fully supervised learning](https://mlsanity.com/paper/2104.06064/) (2021)
- [Back to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly Detection](https://mlsanity.com/paper/2203.05550/) (2022)
- [IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturing](https://mlsanity.com/paper/2301.13359/) (2023)
- [Improved anomaly detection by training an autoencoder with skip connections on images corrupted with Stain-shaped noise](https://mlsanity.com/paper/2008.12977/) (2020)
- [Reconstruction from edge image combined with color and gradient difference for industrial surface anomaly detection](https://mlsanity.com/paper/2210.14485/) (2022)
- [Anomaly Clustering: Grouping Images into Coherent Clusters of Anomaly Types](https://mlsanity.com/paper/2112.11573/) (2021)
- [Set Features for Fine-grained Anomaly Detection](https://mlsanity.com/paper/2302.12245/) (2023)
- [Normal Reference Attention and Defective Feature Perception Network for Surface Defect Detection](https://mlsanity.com/paper/2211.10060/) (2022)

## Built on by (most cited first, 22 of 22)

- [A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospect](https://mlsanity.com/paper/2401.16402/) (2024)
- [ClipSAM: CLIP and SAM Collaboration for Zero-Shot Anomaly Segmentation](https://mlsanity.com/paper/2401.12665/) (2024)
- [Towards Generic Anomaly Detection and Understanding: Large-scale Visual-linguistic Model (GPT-4V) Takes the Lead](https://mlsanity.com/paper/2311.02782/) (2023)
- [UniVAD: A Training-free Unified Model for Few-shot Visual Anomaly Detection](https://mlsanity.com/paper/2412.03342/) (2024)
- [LogicAD: Explainable Anomaly Detection via VLM-based Text Feature Extraction](https://mlsanity.com/paper/2501.01767/) (2025)
- [Few Shot Part Segmentation Reveals Compositional Logic for Industrial Anomaly Detection](https://mlsanity.com/paper/2312.13783/) (2023)
- [CSAD: Unsupervised Component Segmentation for Logical Anomaly Detection](https://mlsanity.com/paper/2408.15628/) (2024)
- [LogicQA: Logical Anomaly Detection with Vision Language Model Generated Questions](https://mlsanity.com/paper/2503.20252/) (2025)
- [Foundation Models and Transformers for Anomaly Detection: A Survey](https://mlsanity.com/paper/2507.15905/) (2025)
- [AnomalyMoE: Towards a Language-free Generalist Model for Unified Visual Anomaly Detection](https://mlsanity.com/paper/2508.06203/) (2025)
- [PUAD: Frustratingly Simple Method for Robust Anomaly Detection](https://mlsanity.com/paper/2402.15143/) (2024)
- [SALAD -- Semantics-Aware Logical Anomaly Detection](https://mlsanity.com/paper/2509.02101/) (2025)
- [FAIR: Frequency-aware Image Restoration for Industrial Visual Anomaly Detection](https://mlsanity.com/paper/2309.07068/) (2023)
- [Beyond Academic Benchmarks: Critical Analysis and Best Practices for Visual Industrial Anomaly Detection](https://mlsanity.com/paper/2503.23451/) (2025)
- [UniSLAD: A Unified Framework for Structural and Logical Industrial Visual Anomaly Detection](https://mlsanity.com/paper/2606.20768/) (2026)
- [Using Global Land Cover Product as Prompt for Cropland Mapping via Visual Foundation Model](https://mlsanity.com/paper/2310.10219/) (2023)
- [LADMIM: Logical Anomaly Detection with Masked Image Modeling in Discrete Latent Space](https://mlsanity.com/paper/2410.10234/) (2024)
- [Double Helix Diffusion for Cross-Domain Anomaly Image Generation](https://mlsanity.com/paper/2509.12787/) (2025)
- [Explainable Visual Anomaly Detection via Concept Bottleneck Models](https://mlsanity.com/paper/2511.20088/) (2025)
- [Semantic-Deviation-Anchored Multi-Branch Fusion for Unsupervised Anomaly Detection and Localization in Unstructured Conveyor-Belt Coal Scenes](https://mlsanity.com/paper/2602.07694/) (2026)
- [UniVAD v2: Unified Visual Anomaly Detection via Support-Conditioned Boundary Construction](https://mlsanity.com/paper/2606.29714/) (2026)
- [Training-Free Logical and Structural Anomaly Detection via Calibrated Fusion](https://mlsanity.com/paper/2609.05091/) (2026)

## Abstract (arXiv)

Industrial visual inspection aims at detecting surface defects in products during the manufacturing process. Although existing anomaly detection models have shown great performance on many public benchmarks, their limited adjustability and ability to detect logical anomalies hinder their broader use in real-world settings. To this end, in this paper, we propose a novel component-aware anomaly detection framework (ComAD) which can simultaneously achieve adjustable and logical anomaly detection for industrial scenarios. Specifically, we propose to segment images into multiple components based on a lightweight and nearly training-free unsupervised semantic segmentation model. Then, we design an interpretable logical anomaly detection model through modeling the metrological features of each component and their relationships. Despite its simplicity, our framework achieves state-of-the-art performance on image-level logical anomaly detection. Meanwhile, segmenting a product image into multiple components provides a novel perspective for industrial visual inspection, demonstrating great potential in model customization, noise resistance, and anomaly classification. The code will be available at https://github.com/liutongkun/ComAD.