1 code implementation • 27 Jul 2022 • Miguel Angel Bautista, Pengsheng Guo, Samira Abnar, Walter Talbott, Alexander Toshev, Zhuoyuan Chen, Laurent Dinh, Shuangfei Zhai, Hanlin Goh, Daniel Ulbricht, Afshin Dehghan, Josh Susskind
We introduce GAUDI, a generative model capable of capturing the distribution of complex and realistic 3D scenes that can be rendered immersively from a moving camera.
Ranked #1 on
Image Generation
on ARKitScenes
1 code implementation • 17 Nov 2021 • Gilad Baruch, Zhuoyuan Chen, Afshin Dehghan, Tal Dimry, Yuri Feigin, Peter Fu, Thomas Gebauer, Brandon Joffe, Daniel Kurz, Arik Schwartz, Elad Shulman
It is not only the first RGB-D dataset that is captured with a now widely available depth sensor, but to our best knowledge, it also is the largest indoor scene understanding data released.
no code implementations • 21 Mar 2017 • Syed Zain Masood, Guang Shu, Afshin Dehghan, Enrique. G. Ortiz
This work details Sighthounds fully automated license plate detection and recognition system.
2 code implementations • 14 Feb 2017 • Afshin Dehghan, Enrique. G. Ortiz, Guang Shu, Syed Zain Masood
This paper describes the details of Sighthound's fully automated age, gender and emotion recognition system.
1 code implementation • 6 Feb 2017 • Afshin Dehghan, Syed Zain Masood, Guang Shu, Enrique. G. Ortiz
The backbone of our system is a deep convolutional neural network that is not only computationally inexpensive, but also provides state-of-the-art results on several competitive benchmarks.
no code implementations • 30 Mar 2016 • Afshin Dehghan, Mubarak Shah
In this paper, we propose a tracker that addresses the aforementioned problems and is capable of tracking hundreds of people efficiently.
no code implementations • 13 Dec 2015 • Meera Hahn, Si Chen, Afshin Dehghan
In this paper, we study a discriminatively trained deep convolutional network for the task of visual tracking.
no code implementations • CVPR 2015 • Afshin Dehghan, Shayan Modiri Assari, Mubarak Shah
Data association is the backbone to many multiple object tracking (MOT) methods.
no code implementations • CVPR 2015 • Afshin Dehghan, Yicong Tian, Philip H. S. Torr, Mubarak Shah
In this paper we show that multiple object tracking (MOT) can be formulated in a framework, where the detection and data-association are performed simultaneously.
no code implementations • CVPR 2014 • Afshin Dehghan, Haroon Idrees, Mubarak Shah
A video captures a sequence and interactions of concepts that can be static, for instance, objects or scenes, or dynamic, such as actions.
no code implementations • CVPR 2014 • Afshin Dehghan, Enrique. G. Ortiz, Ruben Villegas, Mubarak Shah
Recent years have seen a major push for face recognition technology due to the large expansion of image sharing on social networks.
no code implementations • CVPR 2013 • Guang Shu, Afshin Dehghan, Mubarak Shah
In general, our method takes detection bounding boxes of a generic detector as input and generates the detection output with higher average precision and precise object regions.