ZUMA: Training-Free Zero-Shot Unified Multimodal Anomaly Detection
Abstract
Multimodal anomaly detection (MAD) aims to exploit both texture and spatial attributes to identify deviations from normal patterns in complex scenarios. However, zero-shot (ZS) settings arising from privacy concerns or confidentiality constraints present significant challenges to existing MAD methods. To address this issue, we introduce ZUMA, a training-free, Zero-shot Unified Multimodal Anomaly detection framework that unleashes CLIP's cross-modal potential to perform ZS MAD. To mitigate the domain gap between CLIP's pretraining space and point clouds, we propose cross-domain calibration (CDC), which efficiently bridges the manifold misalignment through source-domain semantic transfer and establishes a hybrid semantic space, enabling a joint embedding of 2D and 3D representations. Subsequently, ZUMA performs dynamic semantic interaction (DSI) to enable structural decoupling of anomaly regions in the high-dimensional embedding space constructed by CDC, where natural languages serve as semantic anchors to help DSI establish discriminative hyperplanes within hybrid modality representations. Within this framework, ZUMA enables plug-and-play detection of 2D, 3D or multimodal anomalies, without training or fine-tuning even for cross-dataset or incomplete-modality scenarios. Additionally, to further investigate the potential of the training-free ZUMA within the training-based paradigm, we develop ZUMA-FT, a fine-tuned variant that achieves notable improvements with minimal parameter trade-off. Extensive experiments are conducted on two MAD benchmarks, MVTec 3D-AD and Eyecandies. Notably, the training-free ZUMA achieves state-of-the-art (SOTA) performance on both datasets, outperforming existing ZS MAD methods, including training-based approaches. Moreover, ZUMA-FT further extends the performance boundary of ZUMA with only 6.75 M learnable parameters.
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Yao, Z., Wang, J., Ma, Y., Jiang, S., He, Y., Wang, Y., & Liu, M. (2026) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2026.3709382 · Cited by 1 · see citing works
Self-citationHe, Y., Tian, B., Ma, Y., Zhang, Y., Wang, X., Wang, Y., & Liu, M. (2026) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2026.3688727
Self-citationPrompt-Aware Feature Recomposition for Interactive Defect Segmentation
Meng, X., Ma, Y., Qu, H., Zhou, J., Gao, M., Wang, Y., & Liu, M. (2026) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2026.3687424
Self-citation
- Authors
- Ma, Y., Liu, M., Jiang, S., Zhou, J., Bian, Y., Wang, X., & Wang, Y.
- Affiliations
- Hunan University (CN)
- School of Artificial Intelligence and Robotics, Hunan University and National Engineering Research Center of Robot Visual Perception and Control Technology, Changsha, China
- Hunan Normal University (CN)
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2026-06
- Volume
- 48
- Issue
- 6
- Pages
- 6601-6614
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- 1939-3539
- OpenAlex
- W7126120740
- Cited by
- 4
- References
- 79
- Citations by year
- 2026: 4
- FWCI
- 13.67
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Pattern Analysis and Machine Intelligence (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Natural Science Foundation of China (62425305, 625B2070)
- Natural Science Foundation of Hunan Province (2024JJ3013)
- Science and Technology Innovation Program of Hunan Province (2023RC1048)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- multimodal anomaly detection, zero-shot anomaly detection, training-free detection, CLIP, cross-modal representation, point clouds, semantic embeddings
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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