6 citations · 9 across the 6 of their papers we have counts for
6 papers
Center Focusing Network for Real-Time LiDAR Panoptic Segmentation
Xiaoyan Li, Gang Zhang, Boyue Wang +2
LiDAR panoptic segmentation facilitates an autonomous vehicle to comprehensively understand the surrounding objects and scenes and is required to run in real time. The recent propo…
HEDNet: A Hierarchical Encoder-Decoder Network for 3D Object Detection in Point Clouds
Gang Zhang, Junnan Chen, Guohuan Gao +2
3D object detection in point clouds is important for autonomous driving systems. A primary challenge in 3D object detection stems from the sparse distribution of points within the…
Revisiting Multi-modal 3D Semantic Segmentation in Real-world Autonomous Driving
Feng Jiang, Chaoping Tu, Gang Zhang +5
LiDAR and camera are two critical sensors for multi-modal 3D semantic segmentation and are supposed to be fused efficiently and robustly to promise safety in various real-world sce…
Dual Relation Knowledge Distillation for Object Detection
Zhenliang Ni, Fukui Yang, Shengzhao Wen +1
Knowledge distillation is an effective method for model compression. However, it is still a challenging topic to apply knowledge distillation to detection tasks. There are two key…
Active Pointly-Supervised Instance Segmentation
Chufeng Tang, Lingxi Xie, Gang Zhang +3
The requirement of expensive annotations is a major burden for training a well-performed instance segmentation model. In this paper, we present an economic active learning setting,…
UFO: Unified Feature Optimization
Teng Xi, Yifan Sun, Deli Yu +13
This paper proposes a novel Unified Feature Optimization (UFO) paradigm for training and deploying deep models under real-world and large-scale scenarios, which requires a collecti…