most citedSafeCritic: Collision-Aware Trajectory Prediction

19 citations · 34 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CV20204 cited

PointMixup: Augmentation for Point Clouds

Yunlu Chen, Vincent Tao Hu, Efstratios Gavves +4

This paper introduces data augmentation for point clouds by interpolation between examples. Data augmentation by interpolation has shown to be a simple and effective approach in th…

eess.IV20191 cited

Tracking-Assisted Segmentation of Biological Cells

Deepak K. Gupta, Nathan de Bruijn, Andreas Panteli +1

U-Net and its variants have been demonstrated to work sufficiently well in biological cell tracking and segmentation. However, these methods still suffer in the presence of complex…

cs.LG201919 cited

SafeCritic: Collision-Aware Trajectory Prediction

Tessa van der Heiden, Naveen Shankar Nagaraja, Christian Weiss +1

Navigating complex urban environments safely is a key to realize fully autonomous systems. Predicting future locations of vulnerable road users, such as pedestrians and cyclists, t…

stat.ML20194 cited

Increasing Expressivity of a Hyperspherical VAE

Tim R. Davidson, Jakub M. Tomczak, Efstratios Gavves

Learning suitable latent representations for observed, high-dimensional data is an important research topic underlying many recent advances in machine learning. While traditionally…

cs.CV20192 cited

3D Neighborhood Convolution: Learning Depth-Aware Features for RGB-D and RGB Semantic Segmentation

Yunlu Chen, Thomas Mensink, Efstratios Gavves

A key challenge for RGB-D segmentation is how to effectively incorporate 3D geometric information from the depth channel into 2D appearance features. We propose to model the effect…

cs.CV20194 cited

Spherical Regression: Learning Viewpoints, Surface Normals and 3D Rotations on n-Spheres

Shuai Liao, Efstratios Gavves, Cees G. M. Snoek

Many computer vision challenges require continuous outputs, but tend to be solved by discrete classification. The reason is classification's natural containment within a probabilit…