21 citations · 21 across the 2 of their papers we have counts for
3 papers
Revisiting the Evaluation of Deep Neural Networks for Pedestrian Detection
Patrick Feifel, Benedikt Franke, Frank Bonarens +3
Reliable pedestrian detection represents a crucial step towards automated driving systems. However, the current performance benchmarks exhibit weaknesses. The currently applied met…
Lifelong Learning on Evolving Graphs Under the Constraints of Imbalanced Classes and New Classes
Lukas Galke, Iacopo Vagliano, Benedikt Franke +3
Lifelong graph learning deals with the problem of continually adapting graph neural network (GNN) models to changes in evolving graphs. We address two critical challenges of lifelo…
Lifelong Learning of Graph Neural Networks for Open-World Node Classification
Lukas Galke, Benedikt Franke, Tobias Zielke +1
Graph neural networks (GNNs) have emerged as the standard method for numerous tasks on graph-structured data such as node classification. However, real-world graphs are often evolv…