L
Luc Van Gool
Researcher at Katholieke Universiteit Leuven
Publications - 1458
Citations - 137230
Luc Van Gool is an academic researcher from Katholieke Universiteit Leuven. The author has contributed to research in topics: Computer science & Segmentation. The author has an hindex of 133, co-authored 1307 publications receiving 107743 citations. Previous affiliations of Luc Van Gool include Microsoft & ETH Zurich.
Papers
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Book ChapterDOI
DHP: Differentiable Meta Pruning via HyperNetworks
TL;DR: This paper introduces a differentiable pruning method via hypernetworks for automatic network pruning, and extensive experiments are conducted on various networks for image classification, single image super-resolution, and denoising.
Proceedings ArticleDOI
An object-dependent hand pose prior from sparse training data
TL;DR: A prior for hand pose estimation that integrates the direct relation between a manipulating hand and a 3d object is proposed and integrated into a unified belief propagation framework for tracking and synthesis.
Book ChapterDOI
Large Scale Holistic Video Understanding
Ali Diba,Mohsen Fayyaz,Vivek Sharma,Manohar Paluri,Jürgen Gall,Rainer Stiefelhagen,Luc Van Gool,Luc Van Gool +7 more
TL;DR: A new spatio-temporal deep neural network architecture called "Holistic Appearance and Temporal Network"~(HATNet) that builds on fusing 2D and 3D architectures into one by combining intermediate representations of appearance and temporal cues is introduced.
Journal Article
A Three-Layered Approach to Facade Parsing
TL;DR: In this article, a three-layered approach for semantic segmentation of building facades is proposed, starting from an oversegmentation of a facade, which employs the recently introduced machine learning technique Recursive Neural Networks (RNN) to obtain a probabilistic interpretation of each segment.
Journal ArticleDOI
Fast PRISM: Branch and Bound Hough Transform for Object Class Detection
TL;DR: This paper addresses the task of efficient object class detection by means of the Hough transform by demonstrating PRISM’s flexibility by two complementary implementations: a generatively trained Gaussian Mixture Model as well as a discriminatively trained histogram approach.