1 code implementation • CVPR 2020 • Amir R. Zamir, Alexander Sax, Nikhil Cheerla, Rohan Suri, Zhangjie Cao, Jitendra Malik, Leonidas J. Guibas
Visual perception entails solving a wide set of tasks (e. g., object detection, depth estimation, etc).
Ranked #1 on Surface Normals Estimation on Taskonomy
1 code implementation • ICCV 2019 • Iro Armeni, Zhi-Yang He, JunYoung Gwak, Amir R. Zamir, Martin Fischer, Jitendra Malik, Silvio Savarese
Given a 3D mesh and registered panoramic images, we construct a graph that spans the entire building and includes semantics on objects (e. g., class, material, and other attributes), rooms (e. g., scene category, volume, etc.)
1 code implementation • ICML 2020 • Trevor Standley, Amir R. Zamir, Dawn Chen, Leonidas Guibas, Jitendra Malik, Silvio Savarese
Many computer vision applications require solving multiple tasks in real-time.
1 code implementation • ICLR 2019 • Yajie Bao, Yang Li, Shao-Lun Huang, Lin Zhang, Amir R. Zamir, Leonidas J. Guibas
An important question in task transfer learning is to determine task transferability, i. e. given a common input domain, estimating to what extent representations learned from a source task can help in learning a target task.
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.
9 code implementations • 18 Jul 2018 • Peter Anderson, Angel Chang, Devendra Singh Chaplot, Alexey Dosovitskiy, Saurabh Gupta, Vladlen Koltun, Jana Kosecka, Jitendra Malik, Roozbeh Mottaghi, Manolis Savva, Amir R. Zamir
Skillful mobile operation in three-dimensional environments is a primary topic of study in Artificial Intelligence.
1 code implementation • 23 Oct 2017 • Amir R. Zamir, Tilman Wekel, Pulkit Argrawal, Colin Weil, Jitendra Malik, Silvio Savarese
Though a large body of computer vision research has investigated developing generic semantic representations, efforts towards developing a similar representation for 3D has been limited.
3 code implementations • 3 Feb 2017 • Iro Armeni, Sasha Sax, Amir R. Zamir, Silvio Savarese
We present a dataset of large-scale indoor spaces that provides a variety of mutually registered modalities from 2D, 2. 5D and 3D domains, with instance-level semantic and geometric annotations.
1 code implementation • CVPR 2017 • Amir R. Zamir, Te-Lin Wu, Lin Sun, William Shen, Jitendra Malik, Silvio Savarese
Currently, the most successful learning models in computer vision are based on learning successive representations followed by a decision layer.
no code implementations • CVPR 2016 • Iro Armeni, Ozan Sener, Amir R. Zamir, Helen Jiang, Ioannis Brilakis, Martin Fischer, Silvio Savarese
In this paper, we propose a method for semantic parsing the 3D point cloud of an entire building using a hierarchical approach: first, the raw data is parsed into semantically meaningful spaces (e. g. rooms, etc) that are aligned into a canonical reference coordinate system.
no code implementations • 21 Apr 2016 • Haroon Idrees, Amir R. Zamir, Yu-Gang Jiang, Alex Gorban, Ivan Laptev, Rahul Sukthankar, Mubarak Shah
Additionally, we include a comprehensive empirical study evaluating the differences in action recognition between trimmed and untrimmed videos, and how well methods trained on trimmed videos generalize to untrimmed videos.
2 code implementations • CVPR 2016 • Ashesh Jain, Amir R. Zamir, Silvio Savarese, Ashutosh Saxena
The proposed method is generic and principled as it can be used for transforming any spatio-temporal graph through employing a certain set of well defined steps.
Ranked #4 on Skeleton Based Action Recognition on CAD-120