1.4k citations · 2.6k across the 155 of their papers we have counts for
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DARN: a Deep Adversial Residual Network for Intrinsic Image Decomposition
Louis Lettry, Kenneth Vanhoey, Luc Van Gool
We present a new deep supervised learning method for intrinsic decomposition of a single image into its albedo and shading components. Our contributions are based on a new fully co…
Deep Learning on Lie Groups for Skeleton-based Action Recognition
Zhiwu Huang, Chengde Wan, Thomas Probst +1
In recent years, skeleton-based action recognition has become a popular 3D classification problem. State-of-the-art methods typically first represent each motion sequence as a high…
What Is Around The Camera?
Stamatios Georgoulis, Konstantinos Rematas, Tobias Ritschel +3
How much does a single image reveal about the environment it was taken in? In this paper, we investigate how much of that information can be retrieved from a foreground object, com…
Building Deep Networks on Grassmann Manifolds
Zhiwu Huang, Jiqing Wu, Luc Van Gool
Learning representations on Grassmann manifolds is popular in quite a few visual recognition tasks. In order to enable deep learning on Grassmann manifolds, this paper proposes a d…
One-Shot Video Object Segmentation
Sergi Caelles, Kevis-Kokitsi Maninis, Jordi Pont-Tuset +3
This paper tackles the task of semi-supervised video object segmentation, i.e., the separation of an object from the background in a video, given the mask of the first frame. We pr…
Efficient Volumetric Fusion of Airborne and Street-Side Data for Urban Reconstruction
András Bódis-Szomorú, Hayko Riemenschneider, Luc Van Gool
Airborne acquisition and on-road mobile mapping provide complementary 3D information of an urban landscape: the former acquires roof structures, ground, and vegetation at a large s…