79 citations · 208 across the 21 of their papers we have counts for
11 papers · 1 filter
RNGDet++: Road Network Graph Detection by Transformer with Instance Segmentation and Multi-scale Features Enhancement
Zhenhua Xu, Yuxuan Liu, Yuxiang Sun +2
The road network graph is a critical component for downstream tasks in autonomous driving, such as global route planning and navigation. In the past years, road network graphs are…
CenterLineDet: CenterLine Graph Detection for Road Lanes with Vehicle-mounted Sensors by Transformer for HD Map Generation
Zhenhua Xu, Yuxuan Liu, Yuxiang Sun +2
With the fast development of autonomous driving technologies, there is an increasing demand for high-definition (HD) maps, which provide reliable and robust prior information about…
Open-world Semantic Segmentation for LIDAR Point Clouds
Jun Cen, Peng Yun, Shiwei Zhang +5
Current methods for LIDAR semantic segmentation are not robust enough for real-world applications, e.g., autonomous driving, since it is closed-set and static. The closed-set assum…
Channel Self-Supervision for Online Knowledge Distillation
Shixiao Fan, Xuan Cheng, Xiaomin Wang +5
Recently, researchers have shown an increased interest in the online knowledge distillation. Adopting an one-stage and end-to-end training fashion, online knowledge distillation us…
RNGDet: Road Network Graph Detection by Transformer in Aerial Images
Zhenhua Xu, Yuxuan Liu, Lu Gan +4
Road network graphs provide critical information for autonomous-vehicle applications, such as drivable areas that can be used for motion planning algorithms. To find road network g…
Why-So-Deep: Towards Boosting Previously Trained Models for Visual Place Recognition
M. Usman Maqbool Bhutta, Yuxiang Sun, Darwin Lau +1
Deep learning-based image retrieval techniques for the loop closure detection demonstrate satisfactory performance. However, it is still challenging to achieve high-level performan…
Open-set 3D Object Detection
Jun Cen, Peng Yun, Junhao Cai +2
3D object detection has been wildly studied in recent years, especially for robot perception systems. However, existing 3D object detection is under a closed-set condition, meaning…
Temporally Resolution Decrement: Utilizing the Shape Consistency for Higher Computational Efficiency
Tianshu Xie, Xuan Cheng, Minghui Liu +3
Image resolution that has close relations with accuracy and computational cost plays a pivotal role in network training. In this paper, we observe that the reduced image retains re…
White Paper Assistance: A Step Forward Beyond the Shortcut Learning
Xuan Cheng, Tianshu Xie, Xiaomin Wang +3
The promising performances of CNNs often overshadow the need to examine whether they are doing in the way we are actually interested. We show through experiments that even over-par…
FocusedDropout for Convolutional Neural Network
Tianshu Xie, Minghui Liu, Jiali Deng +3
In convolutional neural network (CNN), dropout cannot work well because dropped information is not entirely obscured in convolutional layers where features are correlated spatially…
Road Crack Detection Using Deep Convolutional Neural Network and Adaptive Thresholding
Rui Fan, Mohammud Junaid Bocus, Yilong Zhu +5
Crack is one of the most common road distresses which may pose road safety hazards. Generally, crack detection is performed by either certified inspectors or structural engineers.…