18 citations · 22 across the 6 of their papers we have counts for
6 papers
FedS2R: One-Shot Federated Domain Generalization for Synthetic-to-Real Semantic Segmentation in Autonomous Driving
Tao Lian, Jose L. Gómez, Antonio M. López
Federated domain generalization has shown promising progress in image classification by enabling collaborative training across multiple clients without sharing raw data. However, i…
Back2Color: Domain-Adaptive Synthetic-to-Real Monocular Depth Estimation for Dynamic Traffic Scenes
Yufan Zhu, Chongzhi Ran, Mingtao Feng +3
Accurate monocular depth estimation is a fundamental component of vision-based perception systems in intelligent transportation applications. Despite recent progress, unsupervised…
UDA4Inst: Unsupervised Domain Adaptation for Instance Segmentation
Yachan Guo, Yi Xiao, Danna Xue +2
Instance segmentation is crucial for autonomous driving, but is hindered by the lack of annotated real-world data due to expensive labeling costs. Unsupervised Domain Adaptation (U…
Guiding Attention in End-to-End Driving Models
Diego Porres, Yi Xiao, Gabriel Villalonga +2
Vision-based end-to-end driving models trained by imitation learning can lead to affordable solutions for autonomous driving. However, training these well-performing models usually…
All for One, and One for All: UrbanSyn Dataset, the third Musketeer of Synthetic Driving Scenes
Jose L. Gómez, Manuel Silva, Antonio Seoane +5
We introduce UrbanSyn, a photorealistic dataset acquired through semi-procedurally generated synthetic urban driving scenarios. Developed using high-quality geometry and materials,…
CARLA-BSP: a simulated dataset with pedestrians
Maciej Wielgosz, Antonio M. López, Muhammad Naveed Riaz
We present a sample dataset featuring pedestrians generated using the ARCANE framework, a new framework for generating datasets in CARLA (0.9.13). We provide use cases for pedestri…