Search Results for author:
Found 6 papers, 3 papers with code
SparseAD: Sparse Query-Centric Paradigm for Efficient End-to-End Autonomous Driving
End-to-End paradigms use a unified framework to implement multi-tasks in an autonomous driving system.
MacFormer: Map-Agent Coupled Transformer for Real-time and Robust Trajectory Prediction
Predicting the future behavior of agents is a fundamental task in autonomous vehicle domains.
You Only Need Two Detectors to Achieve Multi-Modal 3D Multi-Object Tracking
In the classical tracking-by-detection (TBD) paradigm, detection and tracking are separately and sequentially conducted, and data association must be properly performed to achieve satisfactory tracking performance.
A Rotation Meanout Network with Invariance for Dermoscopy Image Classification and Retrieval
As such they have made great improvements in many tasks of dermoscopy images.
TENET: Transformer Encoding Network for Effective Temporal Flow on Motion Prediction
This technical report presents an effective method for motion prediction in autonomous driving.
Ranked #12 on
Motion Forecasting
on Argoverse CVPR 2020
Iterative Distance-Aware Similarity Matrix Convolution with Mutual-Supervised Point Elimination for Efficient Point Cloud Registration
In this paper, we propose a novel learning-based pipeline for partially overlapping 3D point cloud registration.
