5 papers · 1 filter
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…
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…
AttentiveGRU: Recurrent Spatio-Temporal Modeling for Advanced Radar-Based BEV Object Detection
Loveneet Saini, Mirko Meuter, Hasan Tercan +1
Bird's-eye view (BEV) object detection has become important for advanced automotive 3D radar-based perception systems. However, the inherently sparse and non-deterministic nature o…