Search Results for author:
Found 3 papers, 1 papers with code
VALERIE22 -- A photorealistic, richly metadata annotated dataset of urban environments
The VALERIE tool pipeline is a synthetic data generator developed with the goal to contribute to the understanding of domain-specific factors that influence perception performance of DNNs (deep neural networks).
Increasing pedestrian detection performance through weighting of detection impairing factors
The resulting detector is then used to calibrate an empirical weighting loss, which weights samples of real training data and their corresponding detection impairing factors.
Ranked #1 on
Pedestrian Detection
on CityPersons
(using extra training data)
VConv-DAE: Deep Volumetric Shape Learning Without Object Labels
Prior work has shown encouraging results on problems ranging from shape completion to recognition.
