Physic-HM: Restoring Physical Generative Logic in Multimodal Anomaly Detection via Hierarchical Modulation
Xiao Liu, Junchen Jin, Yanjie Zhao, Zhixuan Xing

TL;DR
Physic-HM introduces a hierarchical, physics-informed multimodal anomaly detection framework that models process-to-result dependency, improving detection accuracy in complex manufacturing scenarios like robotic welding.
Contribution
The paper presents Physic-HM, a novel framework that explicitly incorporates physical generative logic and sensor guidance to enhance multimodal anomaly detection.
Findings
Achieves state-of-the-art I-AUROC of 90.7% on Weld-4M benchmark
Effectively models process-to-result dependency using hierarchical architecture
Utilizes sensor-guided modulation for improved feature extraction
Abstract
Multimodal Unsupervised Anomaly Detection (UAD) is critical for quality assurance in smart manufacturing, particularly in complex processes like robotic welding. However, existing methods often suffer from process-logic blindness, treating process modalities (e.g., real-time video, audio, and sensors) and result modalities (e.g., post-weld images) as symmetric feature sources, thereby ignoring the inherent unidirectional physical generative logic. Furthermore, the heterogeneity gap between high-dimensional visual data and low-dimensional sensor signals frequently leads to critical process context being drowned out. In this paper, we propose Physic-HM, a multimodal UAD framework that explicitly incorporates physical inductive bias to model the process-to-result dependency. Specifically, our framework incorporates two key innovations: a Sensor-Guided PHM Modulation mechanism that utilizes…
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Taxonomy
TopicsAnomaly Detection Techniques and Applications · Adversarial Robustness in Machine Learning · Human Pose and Action Recognition
