{"ID":2844621,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.05966","arxiv_id":"2511.05966","title":"Commonality in Few: Few-Shot Multimodal Anomaly Detection via Hypergraph-Enhanced Memory","abstract":"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.","short_abstract":"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 extractin...","url_abs":"https://arxiv.org/abs/2511.05966","url_pdf":"https://arxiv.org/pdf/2511.05966v2","authors":"[\"Yuxuan Lin\",\"Hanjing Yan\",\"Xuan Tong\",\"Yang Chang\",\"Huanzhen Wang\",\"Ziheng Zhou\",\"Shuyong Gao\",\"Yan Wang\",\"Wenqiang Zhang\"]","published":"2025-11-08T10:55:46Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false,"code_links":[{"ID":607311,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2844621,"paper_url":"https://arxiv.org/abs/2511.05966","paper_title":"Commonality in Few: Few-Shot Multimodal Anomaly Detection via Hypergraph-Enhanced Memory","repo_url":"https://github.com/Sunny5250/CIF","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}