activity
20162026
most citedDeepPBM: Deep Probabilistic Background Model Estimation from Video Sequences

9 citations · 15 across the 7 of their papers we have counts for

collaborators

9 papers

cs.RO2026

BeyondSight: Object Permanence for End-to-End Autonomous Driving

Sandro Papais, Letian Wang, Mudit Jain +2

Autonomous driving operates in partially observable environments where actors may become fully occluded by other vehicles or infrastructure. Most end-to-end driving systems implici…

cs.CV2023★ 3 cited

XKD: Knowledge Distillation Across Modalities, Tasks and Stages for Multi-Camera 3D Object Detection

Marvin Klingner, Shubhankar Borse, Varun Ravi Kumar +4

Recent advances in 3D object detection (3DOD) have obtained remarkably strong results for LiDAR-based models. In contrast, surround-view 3DOD models based on multiple camera images…

cs.LG2020★ 2 cited

Deep Markov Spatio-Temporal Factorization

Amirreza Farnoosh, Behnaz Rezaei, Eli Zachary Sennesh +6

We introduce deep Markov spatio-temporal factorization (DMSTF), a generative model for dynamical analysis of spatio-temporal data. Like other factor analysis methods, DMSTF approxi…

cs.CV2020

G-LBM:Generative Low-dimensional Background Model Estimation from Video Sequences

Behnaz Rezaei, Amirreza Farnoosh, Sarah Ostadabbas

In this paper, we propose a computationally tractable and theoretically supported non-linear low-dimensional generative model to represent real-world data in the presence of noise…

cs.CV2019

Target-Specific Action Classification for Automated Assessment of Human Motor Behavior from Video

Behnaz Rezaei, Yiorgos Christakis, Bryan Ho +4

Objective monitoring and assessment of human motor behavior can improve the diagnosis and management of several medical conditions. Over the past decade, significant advances have…

cs.CV2019★ 9 cited

DeepPBM: Deep Probabilistic Background Model Estimation from Video Sequences

Amirreza Farnoosh, Behnaz Rezaei, Sarah Ostadabbas

This paper presents a novel unsupervised probabilistic model estimation of visual background in video sequences using a variational autoencoder framework. Due to the redundant natu…