Search Results for author: Felipe Codevilla

Found 10 papers, 6 papers with code

Scaling Self-Supervised End-to-End Driving with Multi-View Attention Learning

no code implementations7 Feb 2023 Yi Xiao, Felipe Codevilla, Diego Porres, Antonio M. Lopez

With only self-supervised training data, our model yields almost expert performance in CARLA's Nocrash metrics and could be rival to the SOTA models requiring large amounts of human labeled data.

Imitation Learning Inductive Bias +1

Learned Image Compression for Machine Perception

no code implementations3 Nov 2021 Felipe Codevilla, Jean Gabriel Simard, Ross Goroshin, Chris Pal

Compression that ensures high accuracy on computer vision tasks such as image segmentation, classification, and detection therefore has the potential for significant impact across a wide variety of settings.

Image Compression Image Reconstruction +2

Action-Based Representation Learning for Autonomous Driving

1 code implementation21 Aug 2020 Yi Xiao, Felipe Codevilla, Christopher Pal, Antonio M. Lopez

Human drivers produce a vast amount of data which could, in principle, be used to improve autonomous driving systems.

Autonomous Driving Representation Learning

Multimodal End-to-End Autonomous Driving

no code implementations7 Jun 2019 Yi Xiao, Felipe Codevilla, Akhil Gurram, Onay Urfalioglu, Antonio M. López

On the other hand, we find end-to-end driving approaches that try to learn a direct mapping from input raw sensor data to vehicle control signals.

Autonomous Driving Imitation Learning +3

On Offline Evaluation of Vision-based Driving Models

1 code implementation ECCV 2018 Felipe Codevilla, Antonio M. López, Vladlen Koltun, Alexey Dosovitskiy

We show that the correlation of offline evaluation with driving quality can be significantly improved by selecting an appropriate validation dataset and suitable offline metrics.

Autonomous Driving

Single Image Restoration for Participating Media Based on Prior Fusion

no code implementations6 Mar 2016 Joel D. O. Gaya, Felipe Codevilla, Amanda C. Duarte, Paulo L. Drews-Jr, Silvia S. Botelho

Differently from the related work that only deal with a medium, we obtain generality by using an image formation model and a fusion of new image priors.

Image Restoration

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