activity
20182022
most citedLearning High-Speed Flight in the Wild

351 citations · 446 across the 8 of their papers we have counts for

collaborators

16 papers

cs.RO2021351 cited

Learning High-Speed Flight in the Wild

Antonio Loquercio, Elia Kaufmann, René Ranftl +3

Quadrotors are agile. Unlike most other machines, they can traverse extremely complex environments at high speeds. To date, only expert human pilots have been able to fully exploit…

cs.LG2021

An Analysis of Super-Net Heuristics in Weight-Sharing NAS

Kaicheng Yu, René Ranftl, Mathieu Salzmann

Weight sharing promises to make neural architecture search (NAS) tractable even on commodity hardware. Existing methods in this space rely on a diverse set of heuristics to design…

cs.LG20211 cited

Landmark Regularization: Ranking Guided Super-Net Training in Neural Architecture Search

Kaicheng Yu, Rene Ranftl, Mathieu Salzmann

Weight sharing has become a de facto standard in neural architecture search because it enables the search to be done on commodity hardware. However, recent works have empirically s…

cs.CV2021

Vision Transformers for Dense Prediction

René Ranftl, Alexey Bochkovskiy, Vladlen Koltun

We introduce dense vision transformers, an architecture that leverages vision transformers in place of convolutional networks as a backbone for dense prediction tasks. We assemble…

cs.RO2020

Deep Drone Acrobatics

Elia Kaufmann, Antonio Loquercio, René Ranftl +3

Performing acrobatic maneuvers with quadrotors is extremely challenging. Acrobatic flight requires high thrust and extreme angular accelerations that push the platform to its physi…

cs.CV2020

High-dimensional Convolutional Networks for Geometric Pattern Recognition

Christopher Choy, Junha Lee, Rene Ranftl +2

Many problems in science and engineering can be formulated in terms of geometric patterns in high-dimensional spaces. We present high-dimensional convolutional networks (ConvNets)…