Search Results for author:
Found 7 papers, 3 papers with code
Radar-Based Recognition of Static Hand Gestures in American Sign Language
This emphasizes the practicality of our methodology in overcoming data scarcity challenges and advancing the field of automatic gesture recognition in VR and HCI applications.
Open-World Lifelong Graph Learning
We study the problem of lifelong graph learning in an open-world scenario, where a model needs to deal with new tasks and potentially unknown classes.
Concept for an Automatic Annotation of Automotive Radar Data Using AI-segmented Aerial Camera Images
This paper presents an approach to automatically annotate automotive radar data with AI-segmented aerial camera images.
Super-Resolution Radar Imaging with Sparse Arrays Using a Deep Neural Network Trained with Enhanced Virtual Data
The key to this performance is that the DNN is trained using realistic simulation data that perfectly mimic a given sparse antenna radar array hardware as the input.
Implementation of Real-Time Automotive SAR Imaging
This paper presents measures to reduce the computation time of automotive synthetic aperture radar (SAR) imaging to achieve real-time capability.
Achieving Efficient and Realistic Full-Radar Simulations and Automatic Data Annotation by exploiting Ray Meta Data of a Radar Ray Tracing Simulator
This approach allows not only almost perfect annotations possible, but also allows the annotation of exotic effects, such as multi-path effects or to label signal parts originating from different parts of an object.
Lifelong Learning on Evolving Graphs Under the Constraints of Imbalanced Classes and New Classes
The combination of these two challenges is particularly relevant since newly emerging classes typically resemble only a tiny fraction of the data, adding to the already skewed class distribution.
