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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Journal ArticleDOI
Recognizing emotions expressed by body pose
TL;DR: This work constructs a biologically plausible hierarchy of neural detectors, which can discriminate seven basic emotional states from static views of associated body poses, and is evaluated against human test subjects on a recent set of stimuli manufactured for research on emotional body language.
Book ChapterDOI
SCAN: Learning to Classify Images Without Labels
TL;DR: Wang et al. as mentioned in this paper proposed a two-step approach where feature learning and clustering are decoupled, and obtained semantically meaningful features as a prior in a learnable clustering approach.
Proceedings ArticleDOI
Handling Occlusions with Franken-Classifiers
TL;DR: By reusing computations among different training stages, 16 occlusion-specific classifiers can be trained at only one tenth the cost of one full training, and also test time cost grows sub-linearly.
Proceedings ArticleDOI
Sparse Variation Dictionary Learning for Face Recognition with a Single Training Sample per Person
Meng Yang,Luc Van Gool,Lei Zhang +2 more
TL;DR: A sparse variation dictionary from a generic training set is learned to improve the query sample representation by STSPP and can be easily integrated into the framework of sparse representation based classification so that various variations in face images can be better handled.
Posted Content
DSLR-Quality Photos on Mobile Devices with Deep Convolutional Networks
TL;DR: In this article, a residual convolutional neural network was proposed to translate ordinary photos into DSLR-quality images by combining content, color, and texture losses, where the first two losses are defined analytically, while the texture loss is learned in an adversarial fashion.