103 citations · 108 across the 4 of their papers we have counts for
5 papers
DCANet: Dense Context-Aware Network for Semantic Segmentation
Yifu Liu, Chenfeng Xu, Xinyu Jin
As the superiority of context information gradually manifests in advanced semantic segmentation, learning to capture the compact context relationship can help to understand the com…
You Only Group Once: Efficient Point-Cloud Processing with Token Representation and Relation Inference Module
Chenfeng Xu, Bohan Zhai, Bichen Wu +5
3D point-cloud-based perception is a challenging but crucial computer vision task. A point-cloud consists of a sparse, unstructured, and unordered set of points. To understand a po…
A Simple and Efficient Multi-task Network for 3D Object Detection and Road Understanding
Di Feng, Yiyang Zhou, Chenfeng Xu +2
Detecting dynamic objects and predicting static road information such as drivable areas and ground heights are crucial for safe autonomous driving. Previous works studied each perc…
Sparse R-CNN: End-to-End Object Detection with Learnable Proposals
Peize Sun, Rufeng Zhang, Yi Jiang +8
We present Sparse R-CNN, a purely sparse method for object detection in images. Existing works on object detection heavily rely on dense object candidates, such as anchor boxes…
Learn to Scale: Generating Multipolar Normalized Density Maps for Crowd Counting
Chenfeng Xu, Kai Qiu, Jianlong Fu +3
Dense crowd counting aims to predict thousands of human instances from an image, by calculating integrals of a density map over image pixels. Existing approaches mainly suffer from…