papers

Publications (5)

eess.SP2022

Label-Aware Ranked Loss for robust People Counting using Automotive in-cabin Radar

Lorenzo Servadei, Huawei Sun, Julius Ott +6

In this paper, we introduce the Label-Aware Ranked loss, a novel metric loss function. Compared to the state-of-the-art Deep Metric Learning losses, this function takes advantage o…

cs.LG2023

MEET: A Monte Carlo Exploration-Exploitation Trade-off for Buffer Sampling

Julius Ott, Lorenzo Servadei, Jose Arjona-Medina +7

Data selection is essential for any data-based optimization technique, such as Reinforcement Learning. State-of-the-art sampling strategies for the experience replay buffer improve…

cs.LG2022

Utilizing Explainable AI for improving the Performance of Neural Networks

Huawei Sun, Lorenzo Servadei, Hao Feng +3

Nowadays, deep neural networks are widely used in a variety of fields that have a direct impact on society. Although those models typically show outstanding performance, they have…

eess.SP2022

Cross-modal Learning of Graph Representations using Radar Point Cloud for Long-Range Gesture Recognition

Souvik Hazra, Hao Feng, Gamze Naz Kiprit +5

Gesture recognition is one of the most intuitive ways of interaction and has gathered particular attention for human computer interaction. Radar sensors possess multiple intrinsic…

cs.CV2022

Radar Image Reconstruction from Raw ADC Data using Parametric Variational Autoencoder with Domain Adaptation

Michael Stephan, Thomas Stadelmayer, Avik Santra +3

This paper presents a parametric variational autoencoder-based human target detection and localization framework working directly with the raw analog-to-digital converter data from…