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arXiv Computer Vision By Seyoung Jeong, Jong Pil Yun, Sang Jun Lee

GRC-Net: Global Representation Consistency Network for Unsupervised Multimodal Anomaly Detection

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GRC‑Net is a new unsupervised multimodal anomaly detection framework that uses a global‑attention MLP to enforce consistency across patch embeddings and a stable reconstruction module to reduce noise. By capturing holistic context through a global token, it minimizes discrepancies between original and predicted embeddings, leading to more reliable reconstruction errors. Experiments on MVTec 3D‑AD and Eyecandies show that GRC‑Net outperforms existing methods at both image and pixel levels, with clearer distinctions between normal and anomalous regions.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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