18 citations · 30 across the 3 of their papers we have counts for
8 papers
Omnidata: A Scalable Pipeline for Making Multi-Task Mid-Level Vision Datasets from 3D Scans
Ainaz Eftekhar, Alexander Sax, Roman Bachmann +2
This paper introduces a pipeline to parametrically sample and render multi-task vision datasets from comprehensive 3D scans from the real world. Changing the sampling parameters al…
Robust Policies via Mid-Level Visual Representations: An Experimental Study in Manipulation and Navigation
Bryan Chen, Alexander Sax, Gene Lewis +5
Vision-based robotics often separates the control loop into one module for perception and a separate module for control. It is possible to train the whole system end-to-end (e.g. w…
Robust Learning Through Cross-Task Consistency
Amir Zamir, Alexander Sax, Teresa Yeo +6
Visual perception entails solving a wide set of tasks, e.g., object detection, depth estimation, etc. The predictions made for multiple tasks from the same image are not independen…
Learning to Navigate Using Mid-Level Visual Priors
Alexander Sax, Jeffrey O. Zhang, Bradley Emi +4
How much does having visual priors about the world (e.g. the fact that the world is 3D) assist in learning to perform downstream motor tasks (e.g. navigating a complex environment)…
Side-Tuning: A Baseline for Network Adaptation via Additive Side Networks
Jeffrey O Zhang, Alexander Sax, Amir Zamir +2
When training a neural network for a desired task, one may prefer to adapt a pre-trained network rather than starting from randomly initialized weights. Adaptation can be useful in…
Mid-Level Visual Representations Improve Generalization and Sample Efficiency for Learning Visuomotor Policies
Alexander Sax, Bradley Emi, Amir R. Zamir +3
How much does having visual priors about the world (e.g. the fact that the world is 3D) assist in learning to perform downstream motor tasks (e.g. delivering a package)? We study t…