Multimodal Industrial Anomaly Detection by Crossmodal Feature Mapping
Fuente:
arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866910516930150400 |
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| author | Costanzino, Alex Ramirez, Pierluigi Zama Lisanti, Giuseppe Di Stefano, Luigi |
| author_facet | Costanzino, Alex Ramirez, Pierluigi Zama Lisanti, Giuseppe Di Stefano, Luigi |
| contents | The paper explores the industrial multimodal Anomaly Detection (AD) task, which exploits point clouds and RGB images to localize anomalies. We introduce a novel light and fast framework that learns to map features from one modality to the other on nominal samples. At test time, anomalies are detected by pinpointing inconsistencies between observed and mapped features. Extensive experiments show that our approach achieves state-of-the-art detection and segmentation performance in both the standard and few-shot settings on the MVTec 3D-AD dataset while achieving faster inference and occupying less memory than previous multimodal AD methods. Moreover, we propose a layer-pruning technique to improve memory and time efficiency with a marginal sacrifice in performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_04521 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Multimodal Industrial Anomaly Detection by Crossmodal Feature Mapping Costanzino, Alex Ramirez, Pierluigi Zama Lisanti, Giuseppe Di Stefano, Luigi Computer Vision and Pattern Recognition The paper explores the industrial multimodal Anomaly Detection (AD) task, which exploits point clouds and RGB images to localize anomalies. We introduce a novel light and fast framework that learns to map features from one modality to the other on nominal samples. At test time, anomalies are detected by pinpointing inconsistencies between observed and mapped features. Extensive experiments show that our approach achieves state-of-the-art detection and segmentation performance in both the standard and few-shot settings on the MVTec 3D-AD dataset while achieving faster inference and occupying less memory than previous multimodal AD methods. Moreover, we propose a layer-pruning technique to improve memory and time efficiency with a marginal sacrifice in performance. |
| title | Multimodal Industrial Anomaly Detection by Crossmodal Feature Mapping |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2312.04521 |