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
Simultaneous Object Recognition and Segmentation by Image Exploration
TL;DR: A novel Object Recognition approach which overcomes limitations in dealing with extensive clutter, dominant occlusion, large scale and viewpoint changes, and can extend any viewpoint invariant feature extractor.
Proceedings Article
Soft-to-hard vector quantization for end-to-end learning compressible representations
Eirikur Agustsson,Fabian Mentzer,Michael Tschannen,Lukas Cavigelli,Radu Timofte,Luca Benini,Luc Van Gool +6 more
TL;DR: In this article, a soft relaxation of quantization and entropy is proposed to learn compressible representations in deep architectures with an end-to-end training strategy, which achieves state-of-the-art performance for image compression and neural network compression.
Proceedings ArticleDOI
Learning Semantic Segmentation From Synthetic Data: A Geometrically Guided Input-Output Adaptation Approach
TL;DR: This work proposes an approach to cross-domain semantic segmentation with the auxiliary geometric information, which can also be easily obtained from virtual environments, and achieves a clear performance gain compared to the baselines and various competing methods.
Book ChapterDOI
Determination of Optical Flow and its Discontinuities using Non-Linear Diffusion
TL;DR: A new method for optical flow computation by means of a coupled set of non-linear diffusion equations that integrates the classical differential approach with the correlation type of motion detectors and is applicable to stereo matching.
Proceedings ArticleDOI
The Interestingness of Images
TL;DR: This work introduces a set of features computationally capturing the three main aspects of visual interestingness and builds an interestingness predictor from them, shown on three datasets with varying context, reflecting the prior knowledge of the viewers.