no code implementations • 9 Mar 2023 • Mao Ye, Gregory P. Meyer, Yuning Chai, Qiang Liu
Although halting a token is a non-differentiable operation, our method allows for differentiable end-to-end learning by leveraging an equivalent differentiable forward-pass.
no code implementations • 3 Jun 2020 • Nemanja Djuric, Henggang Cui, Zhaoen Su, Shangxuan Wu, Huahua Wang, Fang-Chieh Chou, Luisa San Martin, Song Feng, Rui Hu, Yang Xu, Alyssa Dayan, Sidney Zhang, Brian C. Becker, Gregory P. Meyer, Carlos Vallespi-Gonzalez, Carl K. Wellington
One of the critical pieces of the self-driving puzzle is understanding the surroundings of a self-driving vehicle (SDV) and predicting how these surroundings will change in the near future.
no code implementations • 21 May 2020 • Ankit Laddha, Shivam Gautam, Gregory P. Meyer, Carlos Vallespi-Gonzalez, Carl K. Wellington
We show that our approach significantly improves motion forecasting performance over the existing state-of-the-art.
no code implementations • 12 Mar 2020 • Gregory P. Meyer, Jake Charland, Shreyash Pandey, Ankit Laddha, Shivam Gautam, Carlos Vallespi-Gonzalez, Carl K. Wellington
In this work, we present LaserFlow, an efficient method for 3D object detection and motion forecasting from LiDAR.
no code implementations • 9 Mar 2020 • Shivam Gautam, Gregory P. Meyer, Carlos Vallespi-Gonzalez, Brian C. Becker
Accurate motion state estimation of Vulnerable Road Users (VRUs), is a critical requirement for autonomous vehicles that navigate in urban environments.
no code implementations • CVPR 2021 • Gregory P. Meyer
As a result, our interpretation provides an intuitive way to identify well-suited hyper-parameters by approximating the amount of noise in the data, which we demonstrate through a case study and experimentation on the Faster R-CNN and RetinaNet object detectors.
1 code implementation • 24 Oct 2019 • Gregory P. Meyer, Niranjan Thakurdesai
The capability to detect objects is a core part of autonomous driving.
no code implementations • 25 Apr 2019 • Gregory P. Meyer, Jake Charland, Darshan Hegde, Ankit Laddha, Carlos Vallespi-Gonzalez
In this paper, we present an extension to LaserNet, an efficient and state-of-the-art LiDAR based 3D object detector.
no code implementations • CVPR 2019 • Gregory P. Meyer, Ankit Laddha, Eric Kee, Carlos Vallespi-Gonzalez, Carl K. Wellington
The efficiency results from processing LiDAR data in the native range view of the sensor, where the input data is naturally compact.
no code implementations • ICCV 2015 • Gregory P. Meyer, Shalini Gupta, Iuri Frosio, Dikpal Reddy, Jan Kautz
We introduce a method for accurate three dimensional head pose estimation using a commodity depth camera.