168 citations · 316 across the 18 of their papers we have counts for
5 papers · 1 filter
On Offline Evaluation of Vision-based Driving Models
Felipe Codevilla, Antonio M. López, Vladlen Koltun +1
Autonomous driving models should ideally be evaluated by deploying them on a fleet of physical vehicles in the real world. Unfortunately, this approach is not practical for the vas…
Joint Coarse-And-Fine Reasoning for Deep Optical Flow
Victor Vaquero, German Ros, Francesc Moreno-Noguer +2
We propose a novel representation for dense pixel-wise estimation tasks using CNNs that boosts accuracy and reduces training time, by explicitly exploiting joint coarse-and-fine re…
Metric Learning for Novelty and Anomaly Detection
Marc Masana, Idoia Ruiz, Joan Serrat +2
When neural networks process images which do not resemble the distribution seen during training, so called out-of-distribution images, they often make wrong predictions, and do so…
Is the Pedestrian going to Cross? Answering by 2D Pose Estimation
Zhijie Fang, Antonio M. López
Our recent work suggests that, thanks to nowadays powerful CNNs, image-based 2D pose estimation is a promising cue for determining pedestrian intentions such as crossing the road i…
Monocular Depth Estimation by Learning from Heterogeneous Datasets
Akhil Gurram, Onay Urfalioglu, Ibrahim Halfaoui +2
Depth estimation provides essential information to perform autonomous driving and driver assistance. Especially, Monocular Depth Estimation is interesting from a practical point of…