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
Tracking with a mixed continuous-discrete Conditional Random Field
Stefano Pellegrini,Luc Van Gool +1 more
TL;DR: This work proposes here a multi-target tracking model that succeeds in uniformly including the mentioned sources of information and is amenable to further extensions and builds the model within the Conditional Random Field framework.
Proceedings ArticleDOI
Extremely Weak Supervised Image-to-Image Translation for Semantic Segmentation
TL;DR: The experiments show that an extremely weak supervised I2I translation solution using only one paired training sample can achieve a quantitative performance much better than the unsupervised CycleGAN model, and comparable to that of the supervised pix2pix model trained on thousands of pairs.
Journal ArticleDOI
CDDFuse: Correlation-Driven Dual-Branch Feature Decomposition for Multi-Modality Image Fusion
Zixiang Zhao,Haowen Bai,Jiangshe Zhang,Yulun Zhang,Shuang Xu,Zudi Lin,Radu Timofte,Luc Van Gool +7 more
TL;DR: Zhang et al. as mentioned in this paper proposed a Correlation-Driven feature Decomposition Fusion (CDDFuse) network, which uses Restormer blocks to extract cross-modality shallow features and introduces a dual-branch Transformer-CNN feature extractor with Lite Transformer (LT) blocks leveraging long-range attention to handle lowfrequency global features and Invertible Neural Networks (INN) blocks focusing on extracting high-frequency local information.
Journal ArticleDOI
3D-Aware Video Generation
Sherwin Bahmani,Jeong Joon Park,Despoina Paschalidou,Hao Tang,Gordon Wetzstein,Leonidas J. Guibas,Luc Van Gool,Radu Timofte +7 more
TL;DR: This work develops a GAN framework that synthesizes 3D video supervised only with monocular videos and learns a rich embedding of decomposable 3D structures and motions that enables new visual effects of spatio-temporal renderings while producing imagery with quality comparable to that of existing 3D or video GANs.