Search Results for author: Tianrui Guan

Found 10 papers, 6 papers with code

PLAR: Prompt Learning for Action Recognition

no code implementations21 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.

Action Recognition Optical Flow Estimation

VINet: Visual and Inertial-based Terrain Classification and Adaptive Navigation over Unknown Terrain

no code implementations16 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.

Classification Scheduling

FAR: Fourier Aerial Video Recognition

1 code implementation21 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.

Action Recognition Disentanglement +1

M3DeTR: Multi-representation, Multi-scale, Mutual-relation 3D Object Detection with Transformers

1 code implementation24 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.

3D Object Detection object-detection

GANav: Efficient Terrain Segmentation for Robot Navigation in Unstructured Outdoor Environments

1 code implementation7 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.

Robot Navigation Semantic Segmentation

Forecasting Trajectory and Behavior of Road-Agents Using Spectral Clustering in Graph-LSTMs

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.


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