444 citations · 493 across the 2 of their papers we have counts for
8 papers · 1 filter
Weakly-Supervised Semantic Segmentation by Learning Label Uncertainty
Robby Neven, Davy Neven, Bert De Brabandere +2
Since the rise of deep learning, many computer vision tasks have seen significant advancements. However, the downside of deep learning is that it is very data-hungry. Especially fo…
Instance Segmentation by Jointly Optimizing Spatial Embeddings and Clustering Bandwidth
Davy Neven, Bert De Brabandere, Marc Proesmans +1
Current state-of-the-art instance segmentation methods are not suited for real-time applications like autonomous driving, which require fast execution times at high accuracy. Altho…
A Three-Player GAN: Generating Hard Samples To Improve Classification Networks
Simon Vandenhende, Bert De Brabandere, Davy Neven +1
We propose a Three-Player Generative Adversarial Network to improve classification networks. In addition to the game played between the discriminator and generator, a competition i…
Sparse and noisy LiDAR completion with RGB guidance and uncertainty
Wouter Van Gansbeke, Davy Neven, Bert De Brabandere +1
This work proposes a new method to accurately complete sparse LiDAR maps guided by RGB images. For autonomous vehicles and robotics the use of LiDAR is indispensable in order to ac…
End-to-end Lane Detection through Differentiable Least-Squares Fitting
Wouter Van Gansbeke, Bert De Brabandere, Davy Neven +2
Lane detection is typically tackled with a two-step pipeline in which a segmentation mask of the lane markings is predicted first, and a lane line model (like a parabola or spline)…
Towards End-to-End Lane Detection: an Instance Segmentation Approach
Davy Neven, Bert De Brabandere, Stamatios Georgoulis +2
Modern cars are incorporating an increasing number of driver assist features, among which automatic lane keeping. The latter allows the car to properly position itself within the r…