Remove the Ambiguity: Few-shot Multimodal Anomaly Detection Using Crossmodal Feature Replacer

Yuan Guo, Wanqi Zhang, Xu Wang
Proceedings of the 43rd International Conference on Machine Learning, PMLR 306:38677-38700, 2026.

Abstract

A key challenge in reconstruction-based multimodal anomaly detection is the one-to-many crossmodal mapping problem: a single 3D feature may correspond to multiple plausible RGB appearances, causing deterministic crossmodal regression to collapse valid targets into over-smoothed reconstructions and thereby weaken anomaly discrimination. In this paper, we propose Crossmodal Feature Replacer (CFR), a self-supervised framework that addresses this failure mode through selective inference-time feature replacement. CFR first learns bidirectional cyclic mappings for coarse crossmodal reconstruction, then identifies unreliable reconstructed features and selectively replaces them with high-confidence normal features to correct ambiguity-induced reconstruction failures. Extensive experiments on MVTec 3D-AD and Eyecandies under few-shot settings show that CFR consistently outperforms prior methods. In the challenging 1-shot setting, CFR achieves AUPRO scores of 92.3 and 82.7 at 30% FPR, together with image-level AUROC scores of 74.0 and 75.9, on MVTec 3D-AD and Eyecandies, respectively. Code is available at https://github.com/Yuan-Honoka-Guo/CFR.

Cite this Paper

BibTeX
@InProceedings{pmlr-v306-guo26ai, title = {Remove the Ambiguity: Few-shot Multimodal Anomaly Detection Using Crossmodal Feature Replacer}, author = {Guo, Yuan and Zhang, Wanqi and Wang, Xu}, booktitle = {Proceedings of the 43rd International Conference on Machine Learning}, pages = {38677--38700}, year = {2026}, editor = {Zhang, Tong and Dudik, Miroslav and Jaggi, Martin and Agarwal, Alekh and Li, Sharon and Schuurmans, Dale and Zhu, Jerry and Berkenkamp, Felix and Dong, Hanze and Bietti, Alberto}, volume = {306}, series = {Proceedings of Machine Learning Research}, month = {06--11 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v306/main/assets/guo26ai/guo26ai.pdf}, url = {https://proceedings.mlr.press/v306/guo26ai.html}, abstract = {A key challenge in reconstruction-based multimodal anomaly detection is the one-to-many crossmodal mapping problem: a single 3D feature may correspond to multiple plausible RGB appearances, causing deterministic crossmodal regression to collapse valid targets into over-smoothed reconstructions and thereby weaken anomaly discrimination. In this paper, we propose Crossmodal Feature Replacer (CFR), a self-supervised framework that addresses this failure mode through selective inference-time feature replacement. CFR first learns bidirectional cyclic mappings for coarse crossmodal reconstruction, then identifies unreliable reconstructed features and selectively replaces them with high-confidence normal features to correct ambiguity-induced reconstruction failures. Extensive experiments on MVTec 3D-AD and Eyecandies under few-shot settings show that CFR consistently outperforms prior methods. In the challenging 1-shot setting, CFR achieves AUPRO scores of 92.3 and 82.7 at 30% FPR, together with image-level AUROC scores of 74.0 and 75.9, on MVTec 3D-AD and Eyecandies, respectively. Code is available at https://github.com/Yuan-Honoka-Guo/CFR.} }
Endnote
%0 Conference Paper %T Remove the Ambiguity: Few-shot Multimodal Anomaly Detection Using Crossmodal Feature Replacer %A Yuan Guo %A Wanqi Zhang %A Xu Wang %B Proceedings of the 43rd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2026 %E Tong Zhang %E Miroslav Dudik %E Martin Jaggi %E Alekh Agarwal %E Sharon Li %E Dale Schuurmans %E Jerry Zhu %E Felix Berkenkamp %E Hanze Dong %E Alberto Bietti %F pmlr-v306-guo26ai %I PMLR %P 38677--38700 %U https://proceedings.mlr.press/v306/guo26ai.html %V 306 %X A key challenge in reconstruction-based multimodal anomaly detection is the one-to-many crossmodal mapping problem: a single 3D feature may correspond to multiple plausible RGB appearances, causing deterministic crossmodal regression to collapse valid targets into over-smoothed reconstructions and thereby weaken anomaly discrimination. In this paper, we propose Crossmodal Feature Replacer (CFR), a self-supervised framework that addresses this failure mode through selective inference-time feature replacement. CFR first learns bidirectional cyclic mappings for coarse crossmodal reconstruction, then identifies unreliable reconstructed features and selectively replaces them with high-confidence normal features to correct ambiguity-induced reconstruction failures. Extensive experiments on MVTec 3D-AD and Eyecandies under few-shot settings show that CFR consistently outperforms prior methods. In the challenging 1-shot setting, CFR achieves AUPRO scores of 92.3 and 82.7 at 30% FPR, together with image-level AUROC scores of 74.0 and 75.9, on MVTec 3D-AD and Eyecandies, respectively. Code is available at https://github.com/Yuan-Honoka-Guo/CFR.
APA
Guo, Y., Zhang, W. & Wang, X.. (2026). Remove the Ambiguity: Few-shot Multimodal Anomaly Detection Using Crossmodal Feature Replacer. Proceedings of the 43rd International Conference on Machine Learning, in Proceedings of Machine Learning Research 306:38677-38700 Available from https://proceedings.mlr.press/v306/guo26ai.html.

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