14 citations · 20 across the 6 of their papers we have counts for
7 papers
Multi-conditioned Graph Diffusion for Neural Architecture Search
Rohan Asthana, Joschua Conrad, Youssef Dawoud +2
Neural architecture search automates the design of neural network architectures usually by exploring a large and thus complex architecture search space. To advance the architecture…
SelectNAdapt: Support Set Selection for Few-Shot Domain Adaptation
Youssef Dawoud, Gustavo Carneiro, Vasileios Belagiannis
Generalisation of deep neural networks becomes vulnerable when distribution shifts are encountered between train (source) and test (target) domain data. Few-shot domain adaptation…
Knowing What to Label for Few Shot Microscopy Image Cell Segmentation
Youssef Dawoud, Arij Bouazizi, Katharina Ernst +2
In microscopy image cell segmentation, it is common to train a deep neural network on source data, containing different types of microscopy images, and then fine-tune it using a su…
Edge-Based Self-Supervision for Semi-Supervised Few-Shot Microscopy Image Cell Segmentation
Youssef Dawoud, Katharina Ernst, Gustavo Carneiro +1
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-consumi…
A Spatio-Temporal Multilayer Perceptron for Gesture Recognition
Adrian Holzbock, Alexander Tsaregorodtsev, Youssef Dawoud +2
Gesture recognition is essential for the interaction of autonomous vehicles with humans. While the current approaches focus on combining several modalities like image features, key…
Lightweight Monocular Depth Estimation through Guided Decoding
Michael Rudolph, Youssef Dawoud, Ronja Güldenring +2
We present a lightweight encoder-decoder architecture for monocular depth estimation, specifically designed for embedded platforms. Our main contribution is the Guided Upsampling B…
Few-Shot Microscopy Image Cell Segmentation
Youssef Dawoud, Julia Hornauer, Gustavo Carneiro +1
Automatic cell segmentation in microscopy images works well with the support of deep neural networks trained with full supervision. Collecting and annotating images, though, is not…