10 papers
EXCODER: EXplainable Classification Of DiscretE time series Representations
Yannik Hahn, Antonin Königsfeld, Hasan Tercan +1
Deep learning has significantly improved time series classification, yet the lack of explainability in these models remains a major challenge. While Explainable AI (XAI) techniques…
Graph Query Networks for Object Detection with Automotive Radar
Loveneet Saini, Hasan Tercan, Tobias Meisen
Object detection with 3D radar is essential for 360-degree automotive perception, but radar's long wavelengths produce sparse and irregular reflections that challenge traditional g…
Efficient Inter-Task Attention for Multitask Transformer Models
Christian Bohn, Thomas Kurbiel, Klaus Friedrichs +2
In both Computer Vision and the wider Deep Learning field, the Transformer architecture is well-established as state-of-the-art for many applications. For Multitask Learning, howev…
Out of Distribution Detection for Efficient Continual Learning in Quality Prediction for Arc Welding
Yannik Hahn, Jan Voets, Antonin Koenigsfeld +2
Modern manufacturing relies heavily on fusion welding processes, including gas metal arc welding (GMAW). Despite significant advances in machine learning-based quality prediction,…
Rethinking Backbone Design for Lightweight 3D Object Detection in LiDAR
Adwait Chandorkar, Hasan Tercan, Tobias Meisen
Recent advancements in LiDAR-based 3D object detection have significantly accelerated progress toward the realization of fully autonomous driving in real-world environments. Despit…
Detection Transformers Under the Knife: A Neuroscience-Inspired Approach to Ablations
Nils Hütten, Florian Hölken, Hasan Tercan +1
In recent years, Explainable AI has gained traction as an approach to enhancing model interpretability and transparency, particularly in complex models such as detection transforme…