21 citations · 30 across the 2 of their papers we have counts for
5 papers
Structural Knowledge Distillation for Object Detection
Philip de Rijk, Lukas Schneider, Marius Cordts +1
Knowledge Distillation (KD) is a well-known training paradigm in deep neural networks where knowledge acquired by a large teacher model is transferred to a small student. KD has pr…
Learning Stixel-based Instance Segmentation
Monty Santarossa, Lukas Schneider, Claudius Zelenka +3
Stixels have been successfully applied to a wide range of vision tasks in autonomous driving, recently including instance segmentation. However, due to their sparse occurrence in t…
Slanted Stixels: A way to represent steep streets
Daniel Hernandez-Juarez, Lukas Schneider, Pau Cebrian +6
This work presents and evaluates a novel compact scene representation based on Stixels that infers geometric and semantic information. Our approach overcomes the previous rather re…
Sparsity Invariant CNNs
Jonas Uhrig, Nick Schneider, Lukas Schneider +3
In this paper, we consider convolutional neural networks operating on sparse inputs with an application to depth upsampling from sparse laser scan data. First, we show that traditi…
Slanted Stixels: Representing San Francisco's Steepest Streets
Daniel Hernandez-Juarez, Lukas Schneider, Antonio Espinosa +5
In this work we present a novel compact scene representation based on Stixels that infers geometric and semantic information. Our approach overcomes the previous rather restrictive…