8 citations · 29 across the 11 of their papers we have counts for
11 papers
GroupLane: End-to-End 3D Lane Detection with Channel-wise Grouping
Zhuoling Li, Chunrui Han, Zheng Ge +5
Efficiency is quite important for 3D lane detection due to practical deployment demand. In this work, we propose a simple, fast, and end-to-end detector that still maintains high d…
The 1st-place Solution for CVPR 2023 OpenLane Topology in Autonomous Driving Challenge
Dongming Wu, Fan Jia, Jiahao Chang +7
We present the 1st-place solution of OpenLane Topology in Autonomous Driving Challenge. Considering that topology reasoning is based on centerline detection and traffic element det…
MOTRv3: Release-Fetch Supervision for End-to-End Multi-Object Tracking
En Yu, Tiancai Wang, Zhuoling Li +3
Although end-to-end multi-object trackers like MOTR enjoy the merits of simplicity, they suffer from the conflict between detection and association seriously, resulting in unsatisf…
VoxelFormer: Bird's-Eye-View Feature Generation based on Dual-view Attention for Multi-view 3D Object Detection
Zhuoling Li, Chuanrui Zhang, Wei-Chiu Ma +5
In recent years, transformer-based detectors have demonstrated remarkable performance in 2D visual perception tasks. However, their performance in multi-view 3D object detection re…
Generalizing Multiple Object Tracking to Unseen Domains by Introducing Natural Language Representation
En Yu, Songtao Liu, Zhuoling Li +4
Although existing multi-object tracking (MOT) algorithms have obtained competitive performance on various benchmarks, almost all of them train and validate models on the same domai…
Delving into the Pre-training Paradigm of Monocular 3D Object Detection
Zhuoling Li, Chuanrui Zhang, En Yu +1
The labels of monocular 3D object detection (M3OD) are expensive to obtain. Meanwhile, there usually exists numerous unlabeled data in practical applications, and pre-training is a…
Diversity Matters: Fully Exploiting Depth Clues for Reliable Monocular 3D Object Detection
Zhuoling Li, Zhan Qu, Yang Zhou +3
As an inherently ill-posed problem, depth estimation from single images is the most challenging part of monocular 3D object detection (M3OD). Many existing methods rely on preconce…
Towards Discriminative Representation: Multi-view Trajectory Contrastive Learning for Online Multi-object Tracking
En Yu, Zhuoling Li, Shoudong Han
Discriminative representation is crucial for the association step in multi-object tracking. Recent work mainly utilizes features in single or neighboring frames for constructing me…
Dynamic Graph Learning-Neural Network for Multivariate Time Series Modeling
Zhuoling Li, Gaowei Zhang, Lingyu Xu +1
Multivariate time series forecasting is a challenging task because the data involves a mixture of long- and short-term patterns, with dynamic spatio-temporal dependencies among var…
RelationTrack: Relation-aware Multiple Object Tracking with Decoupled Representation
En Yu, Zhuoling Li, Shoudong Han +1
Existing online multiple object tracking (MOT) algorithms often consist of two subtasks, detection and re-identification (ReID). In order to enhance the inference speed and reduce…
Enabling the Network to Surf the Internet
Zhuoling Li, Haohan Wang, Tymoteusz Swistek +3
Few-shot learning is challenging due to the limited data and labels. Existing algorithms usually resolve this problem by pre-training the model with a considerable amount of annota…