most citedGraph-Segmenter: Graph Transformer with Boundary-aware Attention for Semantic Segmentation

17 citations · 18 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CV2023

PPD: A New Valet Parking Pedestrian Fisheye Dataset for Autonomous Driving

Zizhang Wu, Xinyuan Chen, Fan Song +4

Pedestrian detection under valet parking scenarios is fundamental for autonomous driving. However, the presence of pedestrians can be manifested in a variety of ways and postures u…

cs.CV2023

LineMarkNet: Line Landmark Detection for Valet Parking

Zizhang Wu, Yuanzhu Gan, Tianhao Xu +2

We aim for accurate and efficient line landmark detection for valet parking, which is a long-standing yet unsolved problem in autonomous driving. To this end, we present a deep lin…

cs.CV202317 cited

Graph-Segmenter: Graph Transformer with Boundary-aware Attention for Semantic Segmentation

Zizhang Wu, Yuanzhu Gan, Tianhao Xu +1

The transformer-based semantic segmentation approaches, which divide the image into different regions by sliding windows and model the relation inside each window, have achieved ou…

cs.CV2023

Learning Monocular Depth in Dynamic Environment via Context-aware Temporal Attention

Zizhang Wu, Zhuozheng Li, Zhi-Gang Fan +4

The monocular depth estimation task has recently revealed encouraging prospects, especially for the autonomous driving task. To tackle the ill-posed problem of 3D geometric reasoni…

cs.CV2023

MonoPGC: Monocular 3D Object Detection with Pixel Geometry Contexts

Zizhang Wu, Yuanzhu Gan, Lei Wang +2

Monocular 3D object detection reveals an economical but challenging task in autonomous driving. Recently center-based monocular methods have developed rapidly with a great trade-of…

cs.CV20231 cited

MVFusion: Multi-View 3D Object Detection with Semantic-aligned Radar and Camera Fusion

Zizhang Wu, Guilian Chen, Yuanzhu Gan +2

Multi-view radar-camera fused 3D object detection provides a farther detection range and more helpful features for autonomous driving, especially under adverse weather. The current…