1 code implementation • 9 Mar 2024 • Rohan Asthana, Joschua Conrad, Youssef Dawoud, Maurits Ortmanns, Vasileios Belagiannis
To advance the architecture search, we present a graph diffusion-based NAS approach that uses discrete conditional graph diffusion processes to generate high-performing neural network architectures.
Ranked #1 on Neural Architecture Search on NAS-Bench-301
1 code implementation • 9 Aug 2023 • Youssef Dawoud, Gustavo Carneiro, Vasileios Belagiannis
Few-shot domain adaptation mitigates this issue by adapting deep neural networks pre-trained on the source domain to the target domain using a randomly selected and annotated support set from the target domain.
1 code implementation • 18 Nov 2022 • Youssef Dawoud, Arij Bouazizi, Katharina Ernst, Gustavo Carneiro, Vasileios Belagiannis
In this paper, we argue that the random selection of unlabelled training target images to be annotated and included in the support set may not enable an effective fine-tuning process, so we propose a new approach to optimise this image selection process.
no code implementations • 3 Aug 2022 • Youssef Dawoud, Katharina Ernst, Gustavo Carneiro, Vasileios Belagiannis
Deep neural networks currently deliver promising results for microscopy image cell segmentation, but they require large-scale labelled databases, which is a costly and time-consuming process.
1 code implementation • 25 Apr 2022 • Adrian Holzbock, Alexander Tsaregorodtsev, Youssef Dawoud, Klaus Dietmayer, Vasileios Belagiannis
Gesture recognition is essential for the interaction of autonomous vehicles with humans.
Ranked #1 on Skeleton Based Action Recognition on Drive&Act
1 code implementation • 8 Mar 2022 • Michael Rudolph, Youssef Dawoud, Ronja Güldenring, Lazaros Nalpantidis, Vasileios Belagiannis
Similarly, on the KITTI dataset, inference is possible with up to 23. 7 fps on the Jetson Nano and 102. 9 fps on the Xavier NX.
1 code implementation • 29 Jun 2020 • Youssef Dawoud, Julia Hornauer, Gustavo Carneiro, Vasileios Belagiannis
Instead, we assume that we can access a plethora of annotated image data sets from different domains (sources) and a limited number of annotated image data sets from the domain of interest (target), where each domain denotes not only different image appearance but also a different type of cell segmentation problem.