70 citations
- Arizona State UniversityUS2 papers
- Carnegie Mellon UniversityUS2 papers
- Robert Bosch (Germany)DE2 papers
- Technion – Israel Institute of TechnologyIL2 papers
- University of AmsterdamNL2 papers
- University of BaselCH2 papers
- University of FreiburgDE2 papers
- Department of Physics, Mathematics and InformaticsBY1 paper
- Indian Institute of Technology KanpurIN1 paper
- Karlsruhe Institute of TechnologyDE1 paper
- Lawrence Livermore National LaboratoryUS1 paper
- Massachusetts Institute of TechnologyUS1 paper
9 papers · 1 filter
A System-driven Automatic Ground Truth Generation Method for DL Inner-City Driving Corridor Detectors
Jona Ruthardt, Thomas Michalke
Data-driven perception approaches are well-established in automated driving systems. In many fields even super-human performance is reached. Unlike prediction and planning approach…
Vision-Guided Forecasting -- Visual Context for Multi-Horizon Time Series Forecasting
Eitan Kosman, Dotan Di Castro
Autonomous driving gained huge traction in recent years, due to its potential to change the way we commute. Much effort has been put into trying to estimate the state of a vehicle.…
Self-labeled Conditional GANs
Mehdi Noroozi
This paper introduces a novel and fully unsupervised framework for conditional GAN training in which labels are automatically obtained from data. We incorporate a clustering networ…
You Only Need Adversarial Supervision for Semantic Image Synthesis
Vadim Sushko, Edgar Schönfeld, Dan Zhang +3
Despite their recent successes, GAN models for semantic image synthesis still suffer from poor image quality when trained with only adversarial supervision. Historically, additiona…
Improving Augmentation and Evaluation Schemes for Semantic Image Synthesis
Prateek Katiyar, Anna Khoreva
Despite data augmentation being a de facto technique for boosting the performance of deep neural networks, little attention has been paid to developing augmentation strategies for…
Depth Completion with RGB Prior
Yuri Feldman, Yoel Shapiro, Dotan Di Castro
Depth cameras are a prominent perception system for robotics, especially when operating in natural unstructured environments. Industrial applications, however, typically involve re…