activity
20202026
most citedCassandra: Detecting Trojaned Networks from Adversarial Perturbations

5 citations · 11 across the 9 of their papers we have counts for

collaborators

9 papers

eess.SY2026

Dynamics-matched Physical Reservoir Computing for Undersensed Traffic Prediction

Michael McCreesh, Rohit Gupta, Stephen L. Smith

Machine learning methods are increasingly used for traffic prediction in applications such as autonomous driving. Such predictions must be both highly accurate and immediately avai…

cs.CV2025

Class Prototypes based Contrastive Learning for Classifying Multi-Label and Fine-Grained Educational Videos

Rohit Gupta, Anirban Roy, Claire Christensen +6

The recent growth in the consumption of online media by children during early childhood necessitates data-driven tools enabling educators to filter out appropriate educational cont…

eess.SY2023

Real-time Learning of Driving Gap Preference for Personalized Adaptive Cruise Control

Zhouqiao Zhao, Xishun Liao, Amr Abdelraouf +4

Advanced Driver Assistance Systems (ADAS) are increasingly important in improving driving safety and comfort, with Adaptive Cruise Control (ACC) being one of the most widely used.…

cs.LG2023★ 2 cited

Interaction-Aware Personalized Vehicle Trajectory Prediction Using Temporal Graph Neural Networks

Amr Abdelraouf, Rohit Gupta, Kyungtae Han

Accurate prediction of vehicle trajectories is vital for advanced driver assistance systems and autonomous vehicles. Existing methods mainly rely on generic trajectory predictions…

cs.CV2023

MDAR: Multi-View Multi-Scale Driver Action Recognition with Vision Transformer

Yunsheng Ma, Liangqi Yuan, Amr Abdelraouf +4

Ensuring traffic safety and preventing accidents is a critical goal in daily driving, where the advancement of computer vision technologies can be leveraged to achieve this goal. I…

cs.CV2023

CEMFormer: Learning to Predict Driver Intentions from In-Cabin and External Cameras via Spatial-Temporal Transformers

Yunsheng Ma, Wenqian Ye, Xu Cao +4

Driver intention prediction seeks to anticipate drivers' actions by analyzing their behaviors with respect to surrounding traffic environments. Existing approaches primarily focus…

eess.SY2022★ 4 cited

Driver Digital Twin for Online Prediction of Personalized Lane Change Behavior

Xishun Liao, Xuanpeng Zhao, Ziran Wang +5

Connected and automated vehicles (CAVs) are supposed to share the road with human-driven vehicles (HDVs) in a foreseeable future. Therefore, considering the mixed traffic environme…

cs.CV2020★ 5 cited

Cassandra: Detecting Trojaned Networks from Adversarial Perturbations

Xiaoyu Zhang, Ajmal Mian, Rohit Gupta +2

Deep neural networks are being widely deployed for many critical tasks due to their high classification accuracy. In many cases, pre-trained models are sourced from vendors who may…

cs.CV2020

RescueNet: Joint Building Segmentation and Damage Assessment from Satellite Imagery

Rohit Gupta, Mubarak Shah

Accurate and fine-grained information about the extent of damage to buildings is essential for directing Humanitarian Aid and Disaster Response (HADR) operations in the immediate a…