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Found 12 papers, 2 papers with code
Enhanced Radar Perception via Multi-Task Learning: Towards Refined Data for Sensor Fusion Applications
Radar and camera fusion yields robustness in perception tasks by leveraging the strength of both sensors.
Temporal Decisions: Leveraging Temporal Correlation for Efficient Decisions in Early Exit Neural Networks
These findings highlight the importance of considering temporal correlation in sensor data to improve the termination decision.
Efficient Post-Training Augmentation for Adaptive Inference in Heterogeneous and Distributed IoT Environments
For an ECG classification task, it was able to terminate all samples early, reducing the mean inference energy by 74. 9% and computations by 78. 3%.
Temporal Patience: Efficient Adaptive Deep Learning for Embedded Radar Data Processing
Radar sensors offer power-efficient solutions for always-on smart devices, but processing the data streams on resource-constrained embedded platforms remains challenging.
Multi-Task Cross-Modality Attention-Fusion for 2D Object Detection
In addition, we introduce a Multi-Task Cross-Modality Attention-Fusion Network (MCAF-Net) for object detection, which includes two new fusion blocks.
Detection of Sensor-To-Sensor Variations using Explainable AI
This study proposes a novel approach for detecting sensor-to-sensor variations in sensing devices using the explainable AI (XAI) method of SHapley Additive exPlanations (SHAP).
Uncertainty-based Meta-Reinforcement Learning for Robust Radar Tracking
There, we show that our method outperforms related Meta-RL approaches on unseen tracking scenarios in peak performance by 16% and the baseline by 35% while detecting OOD data with an F1-Score of 72%.
MEET: A Monte Carlo Exploration-Exploitation Trade-off for Buffer Sampling
This is enabled by the uncertainty estimation of the Q-Value function, which guides the sampling to explore more significant transitions and, thus, learn a more efficient policy.
Utilizing Explainable AI for improving the Performance of Neural Networks
To address this, Explainable Artificial Intelligence (XAI) has been developing as a field that aims to improve the transparency of the model and increase their trustworthiness.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
Cross-modal Learning of Graph Representations using Radar Point Cloud for Long-Range Gesture Recognition
Gesture recognition is one of the most intuitive ways of interaction and has gathered particular attention for human computer interaction.
Unsupervised Domain Adaptation across FMCW Radar Configurations Using Margin Disparity Discrepancy
Commercial radar sensing is gaining relevance and machine learning algorithms constitute one of the key components that are enabling the spread of this radio technology into areas like surveillance or healthcare.
Label-Aware Ranked Loss for robust People Counting using Automotive in-cabin Radar
In this paper, we introduce the Label-Aware Ranked loss, a novel metric loss function.
