Search Results for author: Gregory Hager

Found 6 papers, 4 papers with code

Video-based assessment of intraoperative surgical skill

no code implementations13 May 2022 Sanchit Hira, Digvijay Singh, Tae Soo Kim, Shobhit Gupta, Gregory Hager, Shameema Sikder, S. Swaroop Vedula

The neural network approach using attention mechanisms also showed high sensitivity and specificity.


Guiding Multi-Step Rearrangement Tasks with Natural Language Instructions

2 code implementations Conference On Robot Learning (CoRL) 2021 Elias Stengel-Eskin, Andrew Hundt, Zhuohong He, Aditya Murali, Nakul Gopalan, Matthew Gombolay, Gregory Hager

Our model completes block manipulation tasks with synthetic commands 530 more often than a UNet-based baseline, and learns to localize actions correctly while creating a mapping of symbols to perceptual input that supports compositional reasoning.

Instruction Following

Cumulative Assessment for Urban 3D Modeling

1 code implementation9 Jul 2021 Shea Hagstrom, Hee Won Pak, Stephanie Ku, Sean Wang, Gregory Hager, Myron Brown

Urban 3D modeling from satellite images requires accurate semantic segmentation to delineate urban features, multiple view stereo for 3D reconstruction of surface heights, and 3D model fitting to produce compact models with accurate surface slopes.

3D Reconstruction Semantic Segmentation

Robotic Surgery With Lean Reinforcement Learning

1 code implementation3 May 2021 Yotam Barnoy, Molly O'Brien, Will Wang, Gregory Hager

As far as we know, this is the first time an RL-based agent is taught from visual data in a surgical robotics environment.

Q-Learning reinforcement-learning +1

Learning from Synthetic Animals

2 code implementations CVPR 2020 Jiteng Mu, Weichao Qiu, Gregory Hager, Alan Yuille

Despite great success in human parsing, progress for parsing other deformable articulated objects, like animals, is still limited by the lack of labeled data.

Domain Adaptation Human Parsing +1

Self-supervised Learning for Dense Depth Estimation in Monocular Endoscopy

no code implementations25 Jun 2018 Xingtong Liu, Ayushi Sinha, Mathias Unberath, Masaru Ishii, Gregory Hager, Russell H. Taylor, Austin Reiter

We present a self-supervised approach to training convolutional neural networks for dense depth estimation from monocular endoscopy data without a priori modeling of anatomy or shading.

Anatomy Depth Estimation +2

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