5 papers
Parameter Efficient Multi-Class Intelligent Scheduling for Multimodal Online Distributed Industrial Anomaly Detection
Heqiang Wang, Weihong Yang, Zheyuan Yang +4
Industrial anomaly detection has attracted significant attention as a fundamental challenge in industrial systems. The rapid advancement of heterogeneous industrial sensors has dri…
High-Quality Proposal Encoding and Cascade Denoising for Imaginary Supervised Object Detection
Zhiyuan Chen, Yuelin Guo, Zitong Huang +3
Object detection models demand large-scale annotated datasets, which are costly and labor-intensive to create. This motivated Imaginary Supervised Object Detection (ISOD), where mo…
Mitigating Modality Quantity and Quality Imbalance in Multimodal Online Federated Learning
Heqiang Wang, Weihong Yang, Xiaoxiong Zhong +3
The Internet of Things (IoT) ecosystem produces massive volumes of multimodal data from diverse sources, including sensors, cameras, and microphones. With advances in edge intellig…
Multimodal Online Federated Learning with Modality Missing in Internet of Things
Heqiang Wang, Xiang Liu, Xiaoxiong Zhong +3
The Internet of Things (IoT) ecosystem generates vast amounts of multimodal data from heterogeneous sources such as sensors, cameras, and microphones. As edge intelligence continue…
Denoising and Adaptive Online Vertical Federated Learning for Sequential Multi-Sensor Data in Industrial Internet of Things
Heqiang Wang, Xiaoxiong Zhong, Kang Liu +2
With the continuous improvement in the computational capabilities of edge devices such as intelligent sensors in the Industrial Internet of Things, these sensors are no longer limi…