no code implementations • 18 Feb 2023 • Jingzong Li, Yik Hong Cai, Libin Liu, Yu Mao, Chun Jason Xue, Hong Xu
Our main contributions are two-fold: First, we design a 2D-to-3D transformation pipeline that takes as input the point cloud data from LiDAR and 2D bounding boxes from camera that are captured at exactly the same time, and generate 3D bounding boxes efficiently and accurately based on detection results of the previous frames without running 3D detectors.
1 code implementation • 24 May 2022 • Shangyu Wu, Yufei Cui, Jinghuan Yu, Xuan Sun, Tei-Wei Kuo, Chun Jason Xue
Based on the characteristics of the transformed keys, we propose a robust After-Flow Learned Index (AFLI).
1 code implementation • 30 Mar 2022 • Yu Mao, Yufei Cui, Tei-Wei Kuo, Chun Jason Xue
To ease this problem, this paper targets on cutting down the execution time of deep-learning-based compressors.
1 code implementation • CVPR 2021 • Yufei Cui, Yu Mao, Ziquan Liu, Qiao Li, Antoni B. Chan, Xue Liu, Tei-Wei Kuo, Chun Jason Xue
Nested dropout is a variant of dropout operation that is able to order network parameters or features based on the pre-defined importance during training.
no code implementations • 25 Sep 2019 • Yufei Cui, Wuguannan Yao, Qiao Li, Antoni Chan, Chun Jason Xue
In this work, assuming that the exact posterior or a decent approximation is obtained, we propose a generic framework to approximate the output probability distribution induced by model posterior with a parameterized model and in an amortized fashion.
1 code implementation • 29 May 2019 • Yufei Cui, Wuguannan Yao, Qiao Li, Antoni B. Chan, Chun Jason Xue
In this work, assuming that the exact posterior or a decent approximation is obtained, we propose a generic framework to approximate the output probability distribution induced by model posterior with a parameterized model and in an amortized fashion.
no code implementations • 5 Jun 2018 • Jianzhong Sheng, Chuanbo Chen, Chenchen Fu, Chun Jason Xue
Convolution operations dominate the overall execution time of Convolutional Neural Networks (CNNs).