35 citations · 53 across the 12 of their papers we have counts for
12 papers
Multi-Modal Assistance for Unsupervised Domain Adaptation on Point Cloud 3D Object Detection
Shenao Zhao, Pengpeng Liang, Zhoufan Yang
Unsupervised domain adaptation for LiDAR-based 3D object detection (3D UDA) based on the teacher-student architecture with pseudo labels has achieved notable improvements in recent…
Enhancing 3D Object Detection with 2D Detection-Guided Query Anchors
Haoxuanye Ji, Pengpeng Liang, Erkang Cheng
Multi-camera-based 3D object detection has made notable progress in the past several years. However, we observe that there are cases (e.g. faraway regions) in which popular 2D obje…
CurveFormer++: 3D Lane Detection by Curve Propagation with Temporal Curve Queries and Attention
Yifeng Bai, Zhirong Chen, Pengpeng Liang +2
In autonomous driving, accurate 3D lane detection using monocular cameras is important for downstream tasks. Recent CNN and Transformer approaches usually apply a two-stage model d…
Multi-Correlation Siamese Transformer Network with Dense Connection for 3D Single Object Tracking
Shihao Feng, Pengpeng Liang, Jin Gao +1
Point cloud-based 3D object tracking is an important task in autonomous driving. Though great advances regarding Siamese-based 3D tracking have been made recently, it remains chall…
CircleFormer: Circular Nuclei Detection in Whole Slide Images with Circle Queries and Attention
Hengxu Zhang, Pengpeng Liang, Zhiyong Sun +2
Both CNN-based and Transformer-based object detection with bounding box representation have been extensively studied in computer vision and medical image analysis, but circular obj…
CurveFormer: 3D Lane Detection by Curve Propagation with Curve Queries and Attention
Yifeng Bai, Zhirong Chen, Zhangjie Fu +3
3D lane detection is an integral part of autonomous driving systems. Previous CNN and Transformer-based methods usually first generate a bird's-eye-view (BEV) feature map from the…
Traffic Context Aware Data Augmentation for Rare Object Detection in Autonomous Driving
Naifan Li, Fan Song, Ying Zhang +2
Detection of rare objects (e.g., traffic cones, traffic barrels and traffic warning triangles) is an important perception task to improve the safety of autonomous driving. Training…
BEVSegFormer: Bird's Eye View Semantic Segmentation From Arbitrary Camera Rigs
Lang Peng, Zhirong Chen, Zhangjie Fu +2
Semantic segmentation in bird's eye view (BEV) is an important task for autonomous driving. Though this task has attracted a large amount of research efforts, it is still challengi…
Coarse-to-fine Semantic Localization with HD Map for Autonomous Driving in Structural Scenes
Chengcheng Guo, Minjie Lin, Heyang Guo +2
Robust and accurate localization is an essential component for robotic navigation and autonomous driving. The use of cameras for localization with high definition map (HD Map) prov…
A Simple and Strong Baseline for Universal Targeted Attacks on Siamese Visual Tracking
Zhenbang Li, Yaya Shi, Jin Gao +4
Siamese trackers are shown to be vulnerable to adversarial attacks recently. However, the existing attack methods craft the perturbations for each video independently, which comes…
Planar Object Tracking in the Wild: A Benchmark
Pengpeng Liang, Yifan Wu, Hu Lu +3
Planar object tracking is an actively studied problem in vision-based robotic applications. While several benchmarks have been constructed for evaluating state-of-the-art algorithm…
Adaptive Objectness for Object Tracking
Pengpeng Liang, Chunyuan Liao, Xue Mei +1
Object tracking is a long standing problem in vision. While great efforts have been spent to improve tracking performance, a simple yet reliable prior knowledge is left unexploited…