80 citations · 316 across the 7 of their papers we have counts for
7 papers · 1 filter
GAUDI: A Neural Architect for Immersive 3D Scene Generation
Miguel Angel Bautista, Pengsheng Guo, Samira Abnar +9
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. We tackle thi…
ARKitScenes: A Diverse Real-World Dataset For 3D Indoor Scene Understanding Using Mobile RGB-D Data
Gilad Baruch, Zhuoyuan Chen, Afshin Dehghan +8
Scene understanding is an active research area. Commercial depth sensors, such as Kinect, have enabled the release of several RGB-D datasets over the past few years which spawned n…
License Plate Detection and Recognition Using Deeply Learned Convolutional Neural Networks
Syed Zain Masood, Guang Shu, Afshin Dehghan +1
This work details Sighthounds fully automated license plate detection and recognition system. The core technology of the system is built using a sequence of deep Convolutional Neur…
DAGER: Deep Age, Gender and Emotion Recognition Using Convolutional Neural Network
Afshin Dehghan, Enrique G. Ortiz, Guang Shu +1
This paper describes the details of Sighthound's fully automated age, gender and emotion recognition system. The backbone of our system consists of several deep convolutional neura…
View Independent Vehicle Make, Model and Color Recognition Using Convolutional Neural Network
Afshin Dehghan, Syed Zain Masood, Guang Shu +1
This paper describes the details of Sighthound's fully automated vehicle make, model and color recognition system. The backbone of our system is a deep convolutional neural network…
Binary Quadratic Programing for Online Tracking of Hundreds of People in Extremely Crowded Scenes
Afshin Dehghan, Mubarak Shah
Multi-object tracking has been studied for decades. However, when it comes to tracking pedestrians in extremely crowded scenes, we are limited to only few works. This is an importa…
Deep Tracking: Visual Tracking Using Deep Convolutional Networks
Meera Hahn, Si Chen, Afshin Dehghan
In this paper, we study a discriminatively trained deep convolutional network for the task of visual tracking. Our tracker utilizes both motion and appearance features that are ext…