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Found 5 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 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.
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.
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.
