12 citations · 30 across the 6 of their papers we have counts for
7 papers · 1 filter
The LAIA Dataset: Labelled Attention for Intelligent Automobiles
A. Contreras, D. Porres, R. Abad +5
The development of autonomous vehicles (AVs) usually relies heavily on data-driven artificial intelligence (AI) models that require large volumes of sensor data with ground-truth a…
Addressing the Waypoint-Action Gap in End-to-End Autonomous Driving via Vehicle Motion Models
Jorge Daniel Rodríguez-Vidal, Gabriel Villalonga, Diego Porres +1
End-to-End Autonomous Driving (E2E-AD) systems are typically grouped by the nature of their outputs: (i) waypoint-based models that predict a future trajectory, and (ii) action-bas…
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…
Co-Training for Unsupervised Domain Adaptation of Semantic Segmentation Models
Jose L. Gómez, Gabriel Villalonga, Antonio M. López
Semantic image segmentation is a central and challenging task in autonomous driving, addressed by training deep models. Since this training draws to a curse of human-based image la…
Co-training for Deep Object Detection: Comparing Single-modal and Multi-modal Approaches
Jose L. Gómez, Gabriel Villalonga, Antonio M. López
Top-performing computer vision models are powered by convolutional neural networks (CNNs). Training an accurate CNN highly depends on both the raw sensor data and their associated…
Co-training for On-board Deep Object Detection
Gabriel Villalonga, Antonio M. Lopez
Providing ground truth supervision to train visual models has been a bottleneck over the years, exacerbated by domain shifts which degenerate the performance of such models. This w…
Temporal Coherence for Active Learning in Videos
Javad Zolfaghari Bengar, Abel Gonzalez-Garcia, Gabriel Villalonga +5
Autonomous driving systems require huge amounts of data to train. Manual annotation of this data is time-consuming and prohibitively expensive since it involves human resources. Th…