V
Vasileios Belagiannis
Researcher at University of Ulm
Publications - 82
Citations - 5723
Vasileios Belagiannis is an academic researcher from University of Ulm. The author has contributed to research in topics: Computer science & Pose. The author has an hindex of 20, co-authored 73 publications receiving 3852 citations. Previous affiliations of Vasileios Belagiannis include Technische Universität München & University of Oxford.
Papers
More filters
Proceedings ArticleDOI
Deeper Depth Prediction with Fully Convolutional Residual Networks
TL;DR: A fully convolutional architecture, encompassing residual learning, to model the ambiguous mapping between monocular images and depth maps is proposed and a novel way to efficiently learn feature map up-sampling within the network is presented.
Posted Content
Deeper Depth Prediction with Fully Convolutional Residual Networks
TL;DR: In this article, a fully convolutional architecture, encompassing residual learning, is proposed to model the ambiguous mapping between monocular images and depth maps, which can be trained end-to-end and does not rely on post-processing techniques such as CRFs or other additional refinement steps.
Journal ArticleDOI
Point Transformer
TL;DR: Point Transformer as mentioned in this paper is a deep neural network that operates directly on unordered and unstructured point sets to extract local and global features and relate both representations by introducing the local-global attention mechanism.
Journal ArticleDOI
AggNet: Deep Learning From Crowds for Mitosis Detection in Breast Cancer Histology Images
Shadi Albarqouni,Christoph Baur,Felix Achilles,Vasileios Belagiannis,Stefanie Demirci,Nassir Navab +5 more
TL;DR: An experimental study on learning from crowds that handles data aggregation directly as part of the learning process of the convolutional neural network (CNN) via additional crowdsourcing layer (AggNet), which gives valuable insights into the functionality of deep CNN learning from crowd annotations and proves the necessity of data aggregation integration.
Proceedings ArticleDOI
Recurrent Human Pose Estimation
TL;DR: The result is a simple architecture that achieves performance on par with the state of the art, but without the complexity of a graphical model stage (or layers).