activity
20172020
most citedMonoPair: Monocular 3D Object Detection Using Pairwise Spatial Relationships

24 citations · 43 across the 5 of their papers we have counts for

collaborators

13 papers

cs.CV2020

MLOD: Awareness of Extrinsic Perturbation in Multi-LiDAR 3D Object Detection for Autonomous Driving

Jianhao Jiao, Peng Yun, Lei Tai +1

Extrinsic perturbation always exists in multiple sensors. In this paper, we focus on the extrinsic uncertainty in multi-LiDAR systems for 3D object detection. We first analyze the…

cs.CV202024 cited

MonoPair: Monocular 3D Object Detection Using Pairwise Spatial Relationships

Yongjian Chen, Lei Tai, Kai Sun +1

Monocular 3D object detection is an essential component in autonomous driving while challenging to solve, especially for those occluded samples which are only partially visible. Mo…

cs.RO2020

High-speed Autonomous Drifting with Deep Reinforcement Learning

Peide Cai, Xiaodong Mei, Lei Tai +2

Drifting is a complicated task for autonomous vehicle control. Most traditional methods in this area are based on motion equations derived by the understanding of vehicle dynamics,…

cs.CV2019

Utilizing Eye Gaze to Enhance the Generalization of Imitation Networks to Unseen Environments

Congcong Liu, Yuying Chen, Lei Tai +2

Vision-based autonomous driving through imitation learning mimics the behaviors of human drivers by training on pairs of data of raw driver-view images and actions. However, there…

cs.CV2019

Gaze Training by Modulated Dropout Improves Imitation Learning

Yuying Chen, Congcong Liu, Lei Tai +2

Imitation learning by behavioral cloning is a prevalent method that has achieved some success in vision-based autonomous driving. The basic idea behind behavioral cloning is to hav…

cs.CV20193 cited

Fully Using Classifiers for Weakly Supervised Semantic Segmentation with Modified Cues

Ting Sun, Lei Tai, Zhihan Gao +2

This paper proposes a novel weakly-supervised semantic segmentation method using image-level label only. The class-specific activation maps from the well-trained classifiers are us…