116 citations · 430 across the 65 of their papers we have counts for
9 papers · 1 filter
-GAIL: Learning -Divergence for Generative Adversarial Imitation Learning
Xin Zhang, Yanhua Li, Ziming Zhang +1
Imitation learning (IL) aims to learn a policy from expert demonstrations that minimizes the discrepancy between the learner and expert behaviors. Various imitation learning algori…
Aerial Height Prediction and Refinement Neural Networks with Semantic and Geometric Guidance
Elhousni Mahdi, Zhang Ziming, Huang Xinming
Deep learning provides a powerful new approach to many computer vision tasks. Height prediction from aerial images is one of those tasks that benefited greatly from the deployment…
RNN Training along Locally Optimal Trajectories via Frank-Wolfe Algorithm
Yun Yue, Ming Li, Venkatesh Saligrama +1
We propose a novel and efficient training method for RNNs by iteratively seeking a local minima on the loss surface within a small region, and leverage this directional vector for…
Self-supervised Geometric Features Discovery via Interpretable Attention for Vehicle Re-Identification and Beyond
Ming Li, Xinming Huang, Ziming Zhang
To learn distinguishable patterns, most of recent works in vehicle re-identification (ReID) struggled to redevelop official benchmarks to provide various supervisions, which requir…
LodoNet: A Deep Neural Network with 2D Keypoint Matchingfor 3D LiDAR Odometry Estimation
Ce Zheng, Yecheng Lyu, Ming Li +1
Deep learning based LiDAR odometry (LO) estimation attracts increasing research interests in the field of autonomous driving and robotics. Existing works feed consecutive LiDAR fra…
TreeRNN: Topology-Preserving Deep GraphEmbedding and Learning
Yecheng Lyu, Ming Li, Xinming Huang +3
General graphs are difficult for learning due to their irregular structures. Existing works employ message passing along graph edges to extract local patterns using customized grap…
Automatic Building and Labeling of HD Maps with Deep Learning
Mahdi Elhousni, Yecheng Lyu, Ziming Zhang +1
In a world where autonomous driving cars are becoming increasingly more common, creating an adequate infrastructure for this new technology is essential. This includes building and…
Learning to Segment 3D Point Clouds in 2D Image Space
Yecheng Lyu, Xinming Huang, Ziming Zhang
In contrast to the literature where local patterns in 3D point clouds are captured by customized convolutional operators, in this paper we study the problem of how to effectively a…
Self-Orthogonality Module: A Network Architecture Plug-in for Learning Orthogonal Filters
Ziming Zhang, Wenchi Ma, Yuanwei Wu +1
In this paper, we investigate the empirical impact of orthogonality regularization (OR) in deep learning, either solo or collaboratively. Recent works on OR showed some promising r…