R. Mottaghi

Publications
Influence

Beyond PASCAL: A benchmark for 3D object detection in the wild

Y. Xiang, R. Mottaghi, S. Savarese
Computer Science
IEEE Winter Conference on Applications of…
24 March 2014

We introduce PASCAL3D+ dataset, which is a novel and challenging dataset for 3D object detection and pose estimation. Expand

The Role of Context for Object Detection and Semantic Segmentation in the Wild

R. Mottaghi, X. Chen, +5 authors A. Yuille
Computer Science
IEEE Conference on Computer Vision and Pattern…
23 June 2014

We propose a novel deformable part-based model, which exploits both local context around each candidate detection as well as global context at the level of the scene. Expand

Detect What You Can: Detecting and Representing Objects Using Holistic Models and Body Parts

X. Chen, R. Mottaghi, Xiaobai Liu, S. Fidler, R. Urtasun, A. Yuille
Computer Science
IEEE Conference on Computer Vision and Pattern…
8 June 2014

We propose a novel approach to i) handle large deformations and partial occlusions in animals (as examples of highly deformable objects), ii) describe them in terms of body parts, and iii) detect them when they are hard to detect (e.g., animals depicted at low resolution). Expand

Target-driven visual navigation in indoor scenes using deep reinforcement learning

Yuke Zhu, R. Mottaghi, +4 authors Ali Farhadi
Computer Science
IEEE International Conference on Robotics and…
16 September 2016

We proposed a deep reinforcement learning (DRL) framework for target-driven visual navigation. Expand

On Evaluation of Embodied Navigation Agents

Peter Anderson, Angel X. Chang, +8 authors A. Zamir
Computer Science
ArXiv
18 July 2018

We convened a working group to study empirical methodology in navigation research. Expand

AI2-THOR: An Interactive 3D Environment for Visual AI

Eric Kolve, R. Mottaghi, D. Gordon, Yuke Zhu, A. Gupta, Ali Farhadi
Computer Science
ArXiv
14 December 2017

We introduce The House Of inteRactions (THOR), a framework for visual AI research, available at this http URL AI2-THOR consists of near photo-realistic 3D indoor scenes, where AI agents can navigate… Expand

ObjectNet3D: A Large Scale Database for 3D Object Recognition

Y. Xiang, W. Kim, +6 authors S. Savarese
Computer Science
ECCV
8 October 2016

We contribute a large scale database for 3D object recognition, named ObjectNet3D, that consists of 100 categories, 90,127 images, 201,888 objects in these images and 44,147 3D shapes. Expand

Visual Semantic Navigation using Scene Priors

Wei Yang, X. Wang, Ali Farhadi, A. Gupta, R. Mottaghi
Computer Science
ICLR
27 September 2018

We propose to use Graph Convolutional Networks (GCNs) (Kipf & Welling, 2017) to incorporate the prior knowledge into a deep reinforcement learning framework. Expand

Learning to Learn How to Learn: Self-Adaptive Visual Navigation Using Meta-Learning

Mitchell Wortsman, Kiana Ehsani, M. Rastegari, Ali Farhadi, R. Mottaghi
Computer Science
IEEE/CVF Conference on Computer Vision and…
3 December 2018

In this paper we propose a self-adaptive visual navigation method (SAVN) which learns to adapt to new environments without any explicit supervision. Expand

Bottom-Up Segmentation for Top-Down Detection

S. Fidler, R. Mottaghi, A. Yuille, R. Urtasun
Computer Science
IEEE Conference on Computer Vision and Pattern…
23 June 2013

We propose a novel deformable part-based model which exploits region-based segmentation algorithms that compute candidate object regions by bottom-up clustering followed by ranking of those regions. Expand

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