3 papers
cs.CV2022
A Low Memory Footprint Quantized Neural Network for Depth Completion of Very Sparse Time-of-Flight Depth Maps
Xiaowen Jiang, Valerio Cambareri, Gianluca Agresti +4
Sparse active illumination enables precise time-of-flight depth sensing as it maximizes signal-to-noise ratio for low power budgets. However, depth completion is required to produc…
cs.CV2020
Unsupervised Domain Adaptation for Mobile Semantic Segmentation based on Cycle Consistency and Feature Alignment
Marco Toldo, Umberto Michieli, Gianluca Agresti +1
The supervised training of deep networks for semantic segmentation requires a huge amount of labeled real world data. To solve this issue, a commonly exploited workaround is to use…
cs.CV2019
Adversarial Learning and Self-Teaching Techniques for Domain Adaptation in Semantic Segmentation
Umberto Michieli, Matteo Biasetton, Gianluca Agresti +1
Deep learning techniques have been widely used in autonomous driving systems for the semantic understanding of urban scenes. However, they need a huge amount of labeled data for tr…