Commonality in Few: Few-Shot Multimodal Anomaly Detection via Hypergraph-Enhanced Memory
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arXiv
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| Autores principales: | , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866909905096540160 |
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| author | Lin, Yuxuan Yan, Hanjing Tong, Xuan Chang, Yang Wang, Huanzhen Zhou, Ziheng Gao, Shuyong Wang, Yan Zhang, Wenqiang |
| author_facet | Lin, Yuxuan Yan, Hanjing Tong, Xuan Chang, Yang Wang, Huanzhen Zhou, Ziheng Gao, Shuyong Wang, Yan Zhang, Wenqiang |
| contents | Few-shot multimodal industrial anomaly detection is a critical yet underexplored task, offering the ability to quickly adapt to complex industrial scenarios. In few-shot settings, insufficient training samples often fail to cover the diverse patterns present in test samples. This challenge can be mitigated by extracting structural commonality from a small number of training samples. In this paper, we propose a novel few-shot unsupervised multimodal industrial anomaly detection method based on structural commonality, CIF (Commonality In Few). To extract intra-class structural information, we employ hypergraphs, which are capable of modeling higher-order correlations, to capture the structural commonality within training samples, and use a memory bank to store this intra-class structural prior. Firstly, we design a semantic-aware hypergraph construction module tailored for single-semantic industrial images, from which we extract common structures to guide the construction of the memory bank. Secondly, we use a training-free hypergraph message passing module to update the visual features of test samples, reducing the distribution gap between test features and features in the memory bank. We further propose a hyperedge-guided memory search module, which utilizes structural information to assist the memory search process and reduce the false positive rate. Experimental results on the MVTec 3D-AD dataset and the Eyecandies dataset show that our method outperforms the state-of-the-art (SOTA) methods in few-shot settings. Code is available at https://github.com/Sunny5250/CIF. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_05966 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Commonality in Few: Few-Shot Multimodal Anomaly Detection via Hypergraph-Enhanced Memory Lin, Yuxuan Yan, Hanjing Tong, Xuan Chang, Yang Wang, Huanzhen Zhou, Ziheng Gao, Shuyong Wang, Yan Zhang, Wenqiang Computer Vision and Pattern Recognition Few-shot multimodal industrial anomaly detection is a critical yet underexplored task, offering the ability to quickly adapt to complex industrial scenarios. In few-shot settings, insufficient training samples often fail to cover the diverse patterns present in test samples. This challenge can be mitigated by extracting structural commonality from a small number of training samples. In this paper, we propose a novel few-shot unsupervised multimodal industrial anomaly detection method based on structural commonality, CIF (Commonality In Few). To extract intra-class structural information, we employ hypergraphs, which are capable of modeling higher-order correlations, to capture the structural commonality within training samples, and use a memory bank to store this intra-class structural prior. Firstly, we design a semantic-aware hypergraph construction module tailored for single-semantic industrial images, from which we extract common structures to guide the construction of the memory bank. Secondly, we use a training-free hypergraph message passing module to update the visual features of test samples, reducing the distribution gap between test features and features in the memory bank. We further propose a hyperedge-guided memory search module, which utilizes structural information to assist the memory search process and reduce the false positive rate. Experimental results on the MVTec 3D-AD dataset and the Eyecandies dataset show that our method outperforms the state-of-the-art (SOTA) methods in few-shot settings. Code is available at https://github.com/Sunny5250/CIF. |
| title | Commonality in Few: Few-Shot Multimodal Anomaly Detection via Hypergraph-Enhanced Memory |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2511.05966 |