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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Proceedings ArticleDOI
Covariance Pooling for Facial Expression Recognition
TL;DR: In this paper, a manifold network structure was used for covariance pooling to improve facial expression recognition. And the authors achieved a recognition accuracy of 58.14% on Static Facial Expressions in the Wild (SFEW2.0) and 87.0% on the validation set of Real-World Affective Faces (RAF) Database.
Proceedings ArticleDOI
Group Sparsity: The Hinge Between Filter Pruning and Decomposition for Network Compression
TL;DR: This paper analyzes two popular network compression techniques, i.e. filter pruning and low-rank decomposition, in a unified sense and proposes to compress the whole network jointly instead of in a layer-wise manner.
Proceedings ArticleDOI
Replacing Mobile Camera ISP with a Single Deep Learning Model
TL;DR: Huang et al. as discussed by the authors presented PyNET, a novel pyramidal CNN architecture designed for fine-grained image restoration that implicitly learns to perform all ISP steps such as image demosaicing, denoising, white balancing, color and contrast correction, etc.
Proceedings ArticleDOI
Semantic Instance Segmentation for Autonomous Driving
TL;DR: This work proposes a discriminative loss function, operating at pixel level, that encourages a convolutional network to produce a representation of the image that can easily be clustered into instances with a simple post-processing step.
IST project ATTEST - an evolutionary and optimised approach on 3D-TV
Christoph Fehn,Peter Kauff,M Op de Beeck,F Ernst,WA Wijnand IJsselsteijn,Marc Pollefeys,Luc Van Gool,Eyal Ofek,Ian Sexton +8 more
TL;DR: In this article, the authors present a system that allows for an evolutionary introduction of depth perception into the existing 2D digital TV framework, where all parts of the 3D processing chain are optimized to one another.