5 papers
Bidirectional Adaptive Transformers for Multimodal Anomaly Detection
Yuxin Jiang, Yunkang Cao, Weiming Shen
The effective integration of features derived from RGB images and 3D point clouds presents a substantial challenge in the field of multimodal anomaly detection (MAD). Existing MAD…
URA-Net: Uncertainty-Integrated Anomaly Perception and Restoration Attention Network for Unsupervised Anomaly Detection
Wei Luo, Peng Xing, Yunkang Cao +3
Unsupervised anomaly detection plays a pivotal role in industrial defect inspection and medical image analysis, with most methods relying on the reconstruction framework. However,…
VTFusion: A Vision-Text Multimodal Fusion Network for Few-Shot Anomaly Detection
Yuxin Jiang, Yunkang Cao, Yuqi Cheng +2
Few-Shot Anomaly Detection (FSAD) has emerged as a critical paradigm for identifying irregularities using scarce normal references. While recent methods have integrated textual sem…
Boosting Global-Local Feature Matching via Anomaly Synthesis for Multi-Class Point Cloud Anomaly Detection
Yuqi Cheng, Yunkang Cao, Dongfang Wang +2
Point cloud anomaly detection is essential for various industrial applications. The huge computation and storage costs caused by the increasing product classes limit the applicatio…
Weighted Joint Maximum Mean Discrepancy Enabled Multi-Source-Multi-Target Unsupervised Domain Adaptation Fault Diagnosis
Zixuan Wang, Haoran Tang, Haibo Wang +4
Despite the remarkable results that can be achieved by data-driven intelligent fault diagnosis techniques, they presuppose the same distribution of training and test data as well a…