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
Deep visual words: Improved fisher vector for image classification
TL;DR: This work presents a scheme to discover better visual words with CNNs, to obtain improved Fisher vector features and outperformed the state-of-the-art: scene classification MIT indoor, object categorization PASCAL VOC 2007 and Stanford40 human actions.
Posted Content
Analogical Image Translation for Fog Generation.
TL;DR: AIT achieves this zero-shot image translation capability by coupling a supervised training scheme in the synthetic domain, a cycle consistency strategy in the real domain, an adversarial training scheme between the two domains, and a novel network design.
Posted Content
Scaling Semantic Segmentation Beyond 1K Classes on a Single GPU
TL;DR: This paper proposes a novel training methodology to train and scale the existing semantic segmentation models for a large number of semantic classes without increasing the memory overhead, and proposes an approximation method for ground-truth class probability, and uses it to compute cross-entropy loss.
Posted Content
mDALU: Multi-Source Domain Adaptation and Label Unification with Partial Datasets
TL;DR: This paper formulates this as a multi-source domain adaptation and label unification problem, and proposes a novel method for it, which outperforms all competing methods significantly.
Journal ArticleDOI
Privacy in video surveilled spaces
Torsten Spindler,Christoph Wartmann,Ludger Hovestadt,Daniel Roth,Luc Van Gool,Andreas Steffen +5 more
TL;DR: A system prototype for self-determination and privacy enhancement in video surveilled areas by integrating computer vision and cryptographic techniques into networked building automation systems is presented.