1 code implementation • 17 Apr 2024 • Chuheng Wei, Guoyuan Wu, Matthew J. Barth, Amr Abdelraouf, Rohit Gupta, Kyungtae Han
Reliable prediction of vehicle trajectories at signalized intersections is crucial to urban traffic management and autonomous driving systems.
1 code implementation • 7 Dec 2023 • Yunsheng Ma, Can Cui, Xu Cao, Wenqian Ye, Peiran Liu, Juanwu Lu, Amr Abdelraouf, Rohit Gupta, Kyungtae Han, Aniket Bera, James M. Rehg, Ziran Wang
Autonomous driving (AD) has made significant strides in recent years.
1 code implementation • 25 Oct 2023 • Jessica Echterhoff, An Yan, Kyungtae Han, Amr Abdelraouf, Rohit Gupta, Julian McAuley
In the context of human-assisted or autonomous driving, explainability models can help user acceptance and understanding of decisions made by the autonomous vehicle, which can be used to rationalize and explain driver or vehicle behavior.
no code implementations • 10 Sep 2023 • Zhouqiao Zhao, Xishun Liao, Amr Abdelraouf, Kyungtae Han, Rohit Gupta, Matthew J. Barth, Guoyuan Wu
In the online component, driver feedback is used to update the driving gap preference in real time.
no code implementations • 14 Aug 2023 • Amr Abdelraouf, Rohit Gupta, Kyungtae Han
Accurate prediction of vehicle trajectories is vital for advanced driver assistance systems and autonomous vehicles.
1 code implementation • 13 May 2023 • Yunsheng Ma, Liangqi Yuan, Amr Abdelraouf, Kyungtae Han, Rohit Gupta, Zihao Li, Ziran Wang
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.
no code implementations • 13 May 2023 • Yunsheng Ma, Wenqian Ye, Xu Cao, Amr Abdelraouf, Kyungtae Han, Rohit Gupta, Ziran Wang
Driver intention prediction seeks to anticipate drivers' actions by analyzing their behaviors with respect to surrounding traffic environments.
no code implementations • CVPR 2023 • Rohit Gupta, Anirban Roy, Claire Christensen, Sujeong Kim, Sarah Gerard, Madeline Cincebeaux, Ajay Divakaran, Todd Grindal, Mubarak Shah
We learn a class prototype for each class and a loss function is employed to minimize the distances between a class prototype and the samples from the class.
no code implementations • 23 Nov 2022 • Rohit Gupta, Naveed Akhtar, Gaurav Kumar Nayak, Ajmal Mian, Mubarak Shah
By using a nearly disjoint dataset to train the substitute model, our method removes the requirement that the substitute model be trained using the same dataset as the target model, and leverages queries to the target model to retain the fooling rate benefits provided by query-based methods.
no code implementations • 2 Nov 2022 • Xishun Liao, Xuanpeng Zhao, Ziran Wang, Zhouqiao Zhao, Kyungtae Han, Rohit Gupta, Matthew J. Barth, Guoyuan Wu
The proposed system is first evaluated on a human-in-the-loop co-simulation platform, and then in a field implementation with three passenger vehicles connected through the 4G/LTE cellular network.
no code implementations • 22 Jul 2022 • Rohit Gupta, Naveed Akhtar, Ajmal Mian, Mubarak Shah
We establish that this is a result of the presence of false negative pairs in the training process, which increases model sensitivity to input perturbations.
no code implementations • 14 Oct 2021 • Ishan Dave, Naman Biyani, Brandon Clark, Rohit Gupta, Yogesh Rawat, Mubarak Shah
This technical report presents our approach "Knights" to solve the action recognition task on a small subset of Kinetics-400 i. e. Kinetics400ViPriors without using any extra-data.
1 code implementation • 20 Jan 2021 • Ishan Dave, Rohit Gupta, Mamshad Nayeem Rizve, Mubarak Shah
However, prior work on contrastive learning for video data has not explored the effect of explicitly encouraging the features to be distinct across the temporal dimension.
Ranked #9 on Self-supervised Video Retrieval on UCF101
no code implementations • 10 Dec 2020 • Rohit Gupta, Satyajit Jena
A good understanding of the transverse momentum $(p_T)$ spectra is pivotal in the study of QCD matter created during the heavy-ion collision.
High Energy Physics - Phenomenology
no code implementations • 28 Jul 2020 • Xiao-Yu Zhang, Ajmal Mian, Rohit Gupta, Nazanin Rahnavard, Mubarak Shah
We also propose an anomaly detection method to identify the target class in a Trojaned network.
Ranked #1 on Adversarial Defense on TrojAI Round 1
no code implementations • 15 Apr 2020 • 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 aftermath of any natural calamity.
no code implementations • 12 Nov 2019 • Rohit Gupta, Laurent Besacier, Marc Dymetman, Matthias Gallé
Character-based translation has several appealing advantages, but its performance is in general worse than a carefully tuned BPE baseline.
no code implementations • WS 2019 • Rohit Gupta, Patrik Lambert, Raj Nath Patel, John Tinsley
As a commercial provider of machine translation, we are constantly training engines for a variety of uses, languages, and content types.
no code implementations • 26 Nov 2018 • Dominika Tkaczyk, Rohit Gupta, Riccardo Cinti, Joeran Beel
We propose ParsRec, a meta-learning based recommender-system that recommends the potentially most effective parser for a given reference string.
no code implementations • WS 2013 • Raj Nath Patel, Rohit Gupta, Prakash B. Pimpale, Sasikumar M
Reordering is a preprocessing stage for Statistical Machine Translation (SMT) system where the words of the source sentence are reordered as per the syntax of the target language.