activity
20162021
most citedGEOMetrics: Exploiting Geometric Structure for Graph-Encoded Objects

37 citations · 55 across the 6 of their papers we have counts for

collaborators

14 papers

cs.CV20212 cited

Active 3D Shape Reconstruction from Vision and Touch

Edward J. Smith, David Meger, Luis Pineda +4

Humans build 3D understandings of the world through active object exploration, using jointly their senses of vision and touch. However, in 3D shape reconstruction, most recent prog…

cs.LG2020

Post-Workshop Report on Science meets Engineering in Deep Learning, NeurIPS 2019, Vancouver

Levent Sagun, Caglar Gulcehre, Adriana Romero +2

Science meets Engineering in Deep Learning took place in Vancouver as part of the Workshop section of NeurIPS 2019. As organizers of the workshop, we created the following report i…

eess.IV2020

Active MR k-space Sampling with Reinforcement Learning

Luis Pineda, Sumana Basu, Adriana Romero +2

Deep learning approaches have recently shown great promise in accelerating magnetic resonance image (MRI) acquisition. The majority of existing work have focused on designing bette…

cs.CV2020

3D Shape Reconstruction from Vision and Touch

Edward J. Smith, Roberto Calandra, Adriana Romero +4

When a toddler is presented a new toy, their instinctual behaviour is to pick it upand inspect it with their hand and eyes in tandem, clearly searching over its surface to properly…

cs.CV20201 cited

Learning to adapt class-specific features across domains for semantic segmentation

Mikel Menta, Adriana Romero, Joost van de Weijer

Recent advances in unsupervised domain adaptation have shown the effectiveness of adversarial training to adapt features across domains, endowing neural networks with the capabilit…

cs.CV2019

On the Evaluation of Conditional GANs

Terrance DeVries, Adriana Romero, Luis Pineda +2

Conditional Generative Adversarial Networks (cGANs) are finding increasingly widespread use in many application domains. Despite outstanding progress, quantitative evaluation of su…