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
Learning Filter Basis for Convolutional Neural Network Compression
TL;DR: This paper tries to reduce the number of parameters of CNNs by learning a basis of the filters in convolutional layers, and validate the proposed solution for multiple CNN architectures on image classification and image super-resolution benchmarks.
Proceedings ArticleDOI
Night-to-Day Image Translation for Retrieval-based Localization
TL;DR: ToDayGAN as mentioned in this paper uses a modified image-translation model to alter nighttime driving images to a more useful daytime representation, and then compares the translated night images to obtain a pose estimate for the night image using the known 6-DOF position of the closest day image.
Proceedings ArticleDOI
DynamoNet: Dynamic Action and Motion Network
TL;DR: A novel unified spatio-temporal 3D-CNN architecture (DynamoNet) that jointly optimizes the video classification and learning motion representation by predicting future frames as a multi-task learning problem is introduced.
Posted Content
Video Object Segmentation with Episodic Graph Memory Networks
TL;DR: This work exploits an episodic memory network, organized as a fully connected graph, to store frames as nodes and capture cross-frame correlations by edges and yields a neat yet principled framework, which can generalize well both one-shot and zero-shot video object segmentation tasks.
Proceedings ArticleDOI
Practical Full Resolution Learned Lossless Image Compression
TL;DR: L3C as discussed by the authors is a fully parallelizable hierarchical probabilistic model for adaptive entropy coding which is optimized end-to-end for the compression task, and it outperforms the popular engineered codecs, PNG, WebP and JPEG 2000.