1 citations · 2 across the 6 of their papers we have counts for
6 papers
MUFASA: Multi-View Fusion and Adaptation Network with Spatial Awareness for Radar Object Detection
Xiangyuan Peng, Miao Tang, Huawei Sun +3
In recent years, approaches based on radar object detection have made significant progress in autonomous driving systems due to their robustness under adverse weather compared to L…
Enhanced Radar Perception via Multi-Task Learning: Towards Refined Data for Sensor Fusion Applications
Huawei Sun, Hao Feng, Gianfranco Mauro +4
Radar and camera fusion yields robustness in perception tasks by leveraging the strength of both sensors. The typical extracted radar point cloud is 2D without height information d…
Temporal Decisions: Leveraging Temporal Correlation for Efficient Decisions in Early Exit Neural Networks
Max Sponner, Lorenzo Servadei, Bernd Waschneck +2
Deep Learning is becoming increasingly relevant in Embedded and Internet-of-things applications. However, deploying models on embedded devices poses a challenge due to their resour…
Efficient Post-Training Augmentation for Adaptive Inference in Heterogeneous and Distributed IoT Environments
Max Sponner, Lorenzo Servadei, Bernd Waschneck +2
Early Exit Neural Networks (EENNs) present a solution to enhance the efficiency of neural network deployments. However, creating EENNs is challenging and requires specialized domai…
Temporal Patience: Efficient Adaptive Deep Learning for Embedded Radar Data Processing
Max Sponner, Julius Ott, Lorenzo Servadei +3
Radar sensors offer power-efficient solutions for always-on smart devices, but processing the data streams on resource-constrained embedded platforms remains challenging. This pape…
Multi-Task Cross-Modality Attention-Fusion for 2D Object Detection
Huawei Sun, Hao Feng, Georg Stettinger +2
Accurate and robust object detection is critical for autonomous driving. Image-based detectors face difficulties caused by low visibility in adverse weather conditions. Thus, radar…