399 citations
- Helmholtz-Zentrum Dresden-RossendorfDE18 papers
- Technische Universität DresdenDE18 papers
- Fraunhofer Institute for Electronic Nano SystemsDE10 papers
- Coventry UniversityGB7 papers
- Leibniz Institute for Solid State and Materials ResearchDE7 papers
- Durham UniversityGB6 papers
- Friedrich Schiller University JenaDE6 papers
- Max Planck Institute for the Physics of Complex SystemsDE6 papers
- Centre National de la Recherche ScientifiqueFR5 papers
- Leipzig UniversityDE5 papers
- University of WarwickGB5 papers
- Charles UniversityCZ4 papers
13 papers · 1 filter
Data Fusion of Semantic and Depth Information in the Context of Object Detection
Md Abu Yusuf, Md Rezaul Karim Khan, Partha Pratim Saha +1
Considerable study has already been conducted regarding autonomous driving in modern era. An autonomous driving system must be extremely good at detecting objects surrounding the c…
FETCH: A Memory-Efficient Replay Approach for Continual Learning in Image Classification
Markus Weißflog, Peter Protzel, Peer Neubert
Class-incremental continual learning is an important area of research, as static deep learning methods fail to adapt to changing tasks and data distributions. In previous works, pr…
Local positional graphs and attentive local features for a data and runtime-efficient hierarchical place recognition pipeline
Fangming Yuan, Stefan Schubert, Peter Protzel +1
Large-scale applications of Visual Place Recognition (VPR) require computationally efficient approaches. Further, a well-balanced combination of data-based and training-free approa…
OmniPD: One-Step Person Detection in Top-View Omnidirectional Indoor Scenes
Jingrui Yu, Roman Seidel, Gangolf Hirtz
We propose a one-step person detector for topview omnidirectional indoor scenes based on convolutional neural networks (CNNs). While state of the art person detectors reach competi…
What makes visual place recognition easy or hard?
Stefan Schubert, Peer Neubert
Visual place recognition is a fundamental capability for the localization of mobile robots. It places image retrieval in the practical context of physical agents operating in a phy…
Does enhanced shape bias improve neural network robustness to common corruptions?
Chaithanya Kumar Mummadi, Ranjitha Subramaniam, Robin Hutmacher +3
Convolutional neural networks (CNNs) learn to extract representations of complex features, such as object shapes and textures to solve image recognition tasks. Recent work indicate…