289 citations · 774 across the 16 of their papers we have counts for
16 papers
Sliced Wasserstein Generative Models
Jiqing Wu, Zhiwu Huang, Dinesh Acharya +4
In generative modeling, the Wasserstein distance (WD) has emerged as a useful metric to measure the discrepancy between generated and real data distributions. Unfortunately, it is…
Fast video object segmentation with Spatio-Temporal GANs
Sergi Caelles, Albert Pumarola, Francesc Moreno-Noguer +2
Learning descriptive spatio-temporal object models from data is paramount for the task of semi-supervised video object segmentation. Most existing approaches mainly rely on models…
Learning Accurate, Comfortable and Human-like Driving
Simon Hecker, Dengxin Dai, Luc Van Gool
Autonomous vehicles are more likely to be accepted if they drive accurately, comfortably, but also similar to how human drivers would. This is especially true when autonomous and h…
RayNet: Learning Volumetric 3D Reconstruction with Ray Potentials
Despoina Paschalidou, Ali Osman Ulusoy, Carolin Schmitt +2
In this paper, we consider the problem of reconstructing a dense 3D model using images captured from different views. Recent methods based on convolutional neural networks (CNN) al…
AENet: Learning Deep Audio Features for Video Analysis
Naoya Takahashi, Michael Gygli, Luc Van Gool
We propose a new deep network for audio event recognition, called AENet. In contrast to speech, sounds coming from audio events may be produced by a wide variety of sources. Furthe…
Deep Temporal Linear Encoding Networks
Ali Diba, Vivek Sharma, Luc Van Gool
The CNN-encoding of features from entire videos for the representation of human actions has rarely been addressed. Instead, CNN work has focused on approaches to fuse spatial and t…