mlsanityData to 14 Oct 2026

CostFilter-AD: Enhancing Anomaly Detection through Matching Cost Filtering

arXiv 2505.01476 ↗First posted 2 May 2025eess.IV cs.AI cs.CVcited by 5

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Abstract (arXiv)

Unsupervised anomaly detection (UAD) seeks to localize the anomaly mask of an input image with respect to normal samples. Either by reconstructing normal counterparts (reconstruction-based) or by learning an image feature embedding space (embedding-based), existing approaches fundamentally rely on image-level or feature-level matching to derive anomaly scores. Often, such a matching process is inaccurate yet overlooked, leading to sub-optimal detection. To address this issue, we introduce the concept of cost filtering, borrowed from classical matching tasks, such as depth and flow estimation, into the UAD problem. We call this approach em CostFilter-AD. Specifically, we first construct a matching cost volume between the input and normal samples, comprising two spatial dimensions and one matching dimension that encodes potential matches. To refine this, we propose a cost volume filtering network, guided by the input observation as an attention query across multiple feature layers, which effectively suppresses matching noise while preserving edge structures and capturing subtle anomalies. Designed as a generic post-processing plug-in, CostFilter-AD can be integrated with either reconstruction-based or embedding-based methods. Extensive experiments on MVTec-AD and VisA benchmarks validate the generic benefits of CostFilter-AD for both single- and multi-class UAD tasks. Code and models will be released at https://github.com/ZHE-SAPI/CostFilter-AD.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 34 of its 59 references that are in the map. First posted on arXiv 2 May 2025.

Builds on

34 of 59 references.

References · 34

  1. 2021Emerging Properties in Self-Supervised Vision Transformerscited by 6,968
  2. 2018CBAM: Convolutional Block Attention Modulecited by 2,477
  3. 20163D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotationcited by 1,476
  4. 2021EfficientNetV2: Smaller Models and Faster Trainingcited by 1,135
  5. 2021Towards Total Recall in Industrial Anomaly Detectioncited by 776
  6. 2020PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localizationcited by 594
  7. 2019Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detectioncited by 564
  8. 2021DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detectioncited by 514
  9. 2022Anomaly Detection via Reverse Distillation from One-Class Embeddingcited by 421
  10. 2022SPot-the-Difference Self-Supervised Pre-training for Anomaly Detection and Segmentationcited by 400
  11. 2017End-to-End Learning of Geometry and Context for Deep Stereo Regressioncited by 360
  12. 2021CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flowscited by 321
22 more
  1. 2022A Unified Model for Multi-class Anomaly Detectioncited by 268
  2. 2023SimpleNet: A Simple Network for Image Anomaly Detection and Localizationcited by 256
  3. 2021VT-ADL: A Vision Transformer Network for Image Anomaly Detection and Localizationcited by 223
  4. 2022DeSTSeg: Segmentation Guided Denoising Student-Teacher for Anomaly Detectioncited by 154
  5. 2022Diffusion Probabilistic Model Made Slimcited by 121
  6. 2024RealNet: A Feature Selection Network with Realistic Synthetic Anomaly for Anomaly Detectioncited by 116
  7. 2024MambaAD: Exploring State Space Models for Multi-class Unsupervised Anomaly Detectioncited by 113
  8. 2024Dinomaly: The Less Is More Philosophy in Multi-Class Unsupervised Anomaly Detectioncited by 93
  9. 2022Rethinking Depth Estimation for Multi-View Stereo: A Unified Representationcited by 82
  10. 2022Omni-frequency Channel-selection Representations for Unsupervised Anomaly Detectioncited by 80
  11. 2024A Unified Anomaly Synthesis Strategy with Gradient Ascent for Industrial Anomaly Detection and Localizationcited by 74
  12. 2022PNI : Industrial Anomaly Detection using Position and Neighborhood Informationcited by 69
  13. 2024A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospectcited by 69
  14. 2021Deep One-Class Classification via Interpolated Gaussian Descriptorcited by 67
  15. 2023Hierarchical Vector Quantized Transformer for Multi-class Unsupervised Anomaly Detectioncited by 66
  16. 2024AnomalyDINO: Boosting Patch-based Few-shot Anomaly Detection with DINOv2cited by 53
  17. 2021Self-supervised Learning of Depth Inference for Multi-view Stereocited by 28
  18. 2024Text-Guided Variational Image Generation for Industrial Anomaly Detection and Segmentationcited by 11
  19. 2024Cross-Modal Learning for Anomaly Detection in Complex Industrial Process: Methodology and Benchmarkcited by 8
  20. 2023DIFT: Dynamic Iterative Field Transforms for Memory Efficient Optical Flowcited by 7
  21. 2023Unified Domain Adaptive Semantic Segmentationcited by 6
  22. 2022Geo-NI: Geometry-aware Neural Interpolation for Light Field Renderingcited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 4.

5 papers.

  1. 2025Dinomaly2: A Unified Framework for Unsupervised Image Anomaly Detectioncited by 6
  2. 2026Towards an Incremental Unified Multimodal Anomaly Detection: Augmenting Multimodal Denoising From an Information Bottleneck Perspectivecited by 4
  3. 2026RAID: Retrieval-Augmented Anomaly Detectioncited by 2
  4. 2026XMatchAD: A Cross-Modal Matching Perspective on Reconstruction-based Anomaly Detectioncited by 0
  5. 2026Understanding and Overcoming Cross-modal Fusion Bias in Multimodal Anomaly Detection From A Fisher Information Perspectivecited by 0