no code implementations • 4 Apr 2024 • Tianrui Guan, Ruiqi Xian, Xijun Wang, Xiyang Wu, Mohamed Elnoor, Daeun Song, Dinesh Manocha
We present AGL-NET, a novel learning-based method for global localization using LiDAR point clouds and satellite maps.
no code implementations • 14 Mar 2024 • Xiaoyu Liu, Paiheng Xu, Junda Wu, Jiaxin Yuan, Yifan Yang, YuHang Zhou, Fuxiao Liu, Tianrui Guan, Haoliang Wang, Tong Yu, Julian McAuley, Wei Ai, Furong Huang
Causal inference has shown potential in enhancing the predictive accuracy, fairness, robustness, and explainability of Natural Language Processing (NLP) models by capturing causal relationships among variables.
no code implementations • 15 Feb 2024 • Xiyang Wu, Ruiqi Xian, Tianrui Guan, Jing Liang, Souradip Chakraborty, Fuxiao Liu, Brian Sadler, Dinesh Manocha, Amrit Singh Bedi
However, such integration can introduce significant vulnerabilities, in terms of their susceptibility to adversarial attacks due to the language models, potentially leading to catastrophic consequences.
6 code implementations • 23 Oct 2023 • Tianrui Guan, Fuxiao Liu, Xiyang Wu, Ruiqi Xian, Zongxia Li, Xiaoyu Liu, Xijun Wang, Lichang Chen, Furong Huang, Yaser Yacoob, Dinesh Manocha, Tianyi Zhou
Our comprehensive case studies within HallusionBench shed light on the challenges of hallucination and illusion in LVLMs.
Ranked #1 on Visual Question Answering (VQA) on HallusionBench
1 code implementation • 9 Jun 2023 • Xiyang Wu, Rohan Chandra, Tianrui Guan, Amrit Singh Bedi, Dinesh Manocha
Our approach for intent-aware planning, iPLAN, allows agents to infer nearby drivers' intents solely from their local observations.
no code implementations • 21 May 2023 • Xijun Wang, Ruiqi Xian, Tianrui Guan, Dinesh Manocha
We evaluate our approach on datasets consisting of both ground camera videos and aerial videos, and scenes with single-agent and multi-agent actions.
Ranked #1 on Action Recognition on Okutama-Action
1 code implementation • ICCV 2023 • Tianrui Guan, Aswath Muthuselvam, Montana Hoover, Xijun Wang, Jing Liang, Adarsh Jagan Sathyamoorthy, Damon Conover, Dinesh Manocha
We present CrossLoc3D, a novel 3D place recognition method that solves a large-scale point matching problem in a cross-source setting.
Ranked #1 on 3D Place Recognition on CS-Campus3D
no code implementations • 2 Mar 2023 • Xijun Wang, Ruiqi Xian, Tianrui Guan, Celso M. de Melo, Stephen M. Nogar, Aniket Bera, Dinesh Manocha
We propose a novel approach for aerial video action recognition.
Ranked #1 on Action Recognition on RoCoG-v2
no code implementations • 16 Sep 2022 • Tianrui Guan, Ruitao Song, Zhixian Ye, Liangjun Zhang
We present a visual and inertial-based terrain classification network (VINet) for robotic navigation over different traversable surfaces.
1 code implementation • 21 Mar 2022 • Divya Kothandaraman, Tianrui Guan, Xijun Wang, Sean Hu, Ming Lin, Dinesh Manocha
Our formulation uses a novel Fourier object disentanglement method to innately separate out the human agent (which is typically small) from the background.
Ranked #1 on Action Recognition on UAV Human
1 code implementation • 24 Apr 2021 • Tianrui Guan, Jun Wang, Shiyi Lan, Rohan Chandra, Zuxuan Wu, Larry Davis, Dinesh Manocha
We present a novel architecture for 3D object detection, M3DeTR, which combines different point cloud representations (raw, voxels, bird-eye view) with different feature scales based on multi-scale feature pyramids.
Ranked #1 on 3D Object Detection on KITTI Cars Hard val
1 code implementation • 7 Mar 2021 • Tianrui Guan, Divya Kothandaraman, Rohan Chandra, Adarsh Jagan Sathyamoorthy, Kasun Weerakoon, Dinesh Manocha
We interface GANav with a deep reinforcement learning-based navigation algorithm and highlight its benefits in terms of navigation in real-world unstructured terrains.
Ranked #1 on Semantic Segmentation on RUGD
no code implementations • arXiv 2019 • Rohan Chandra, Tianrui Guan, Srujan Panuganti, Trisha Mittal, Uttaran Bhattacharya, Aniket Bera, Dinesh Manocha
In practice, our approach reduces the average prediction error by more than 54% over prior algorithms and achieves a weighted average accuracy of 91. 2% for behavior prediction.
Ranked #1 on Trajectory Prediction on ApolloScape
Robotics