1 code implementation • ICCV 2021 • Ainaz Eftekhar, Alexander Sax, Roman Bachmann, Jitendra Malik, Amir Zamir
This paper introduces a pipeline to parametrically sample and render multi-task vision datasets from comprehensive 3D scans from the real world.
no code implementations • ICCV 2021 • Teresa Yeo, Oğuzhan Fatih Kar, Alexander Sax, Amir Zamir
We present a method for making neural network predictions robust to shifts from the training data distribution.
no code implementations • 13 Nov 2020 • Bryan Chen, Alexander Sax, Gene Lewis, Iro Armeni, Silvio Savarese, Amir Zamir, Jitendra Malik, Lerrel Pinto
Vision-based robotics often separates the control loop into one module for perception and a separate module for control.
1 code implementation • 7 Jun 2020 • Amir Zamir, Alexander Sax, Teresa Yeo, Oğuzhan Kar, Nikhil Cheerla, Rohan Suri, Zhangjie Cao, Jitendra Malik, Leonidas Guibas
Visual perception entails solving a wide set of tasks, e. g., object detection, depth estimation, etc.
2 code implementations • ECCV 2020 • Jeffrey O. Zhang, Alexander Sax, Amir Zamir, Leonidas Guibas, Jitendra Malik
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.
1 code implementation • 23 Dec 2019 • Alexander Sax, Jeffrey O. Zhang, Bradley Emi, Amir Zamir, Silvio Savarese, Leonidas Guibas, Jitendra Malik
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)?
1 code implementation • 31 Dec 2018 • Alexander Sax, Bradley Emi, Amir R. Zamir, Leonidas Guibas, Silvio Savarese, Jitendra Malik
This skill set (hereafter mid-level perception) provides the policy with a more processed state of the world compared to raw images.
5 code implementations • CVPR 2018 • Fei Xia, Amir Zamir, Zhi-Yang He, Alexander Sax, Jitendra Malik, Silvio Savarese
Developing visual perception models for active agents and sensorimotor control are cumbersome to be done in the physical world, as existing algorithms are too slow to efficiently learn in real-time and robots are fragile and costly.
1 code implementation • CVPR 2018 • Amir Zamir, Alexander Sax, William Shen, Leonidas Guibas, Jitendra Malik, Silvio Savarese
The product is a computational taxonomic map for task transfer learning.