25 citations · 141 across the 47 of their papers we have counts for
13 papers · 1 filter
Towards Knowledge-driven Autonomous Driving
Xin Li, Yeqi Bai, Pinlong Cai +14
This paper explores the emerging knowledge-driven autonomous driving technologies. Our investigation highlights the limitations of current autonomous driving systems, in particular…
BridgeNet: Comprehensive and Effective Feature Interactions via Bridge Feature for Multi-task Dense Predictions
Jingdong Zhang, Jiayuan Fan, Peng Ye +5
Multi-task dense prediction aims at handling multiple pixel-wise prediction tasks within a unified network simultaneously for visual scene understanding. However, cross-task featur…
Rethinking Cross-Domain Pedestrian Detection: A Background-Focused Distribution Alignment Framework for Instance-Free One-Stage Detectors
Yancheng Cai, Bo Zhang, Baopu Li +4
Cross-domain pedestrian detection aims to generalize pedestrian detectors from one label-rich domain to another label-scarce domain, which is crucial for various real-world applica…
SPOT: Scalable 3D Pre-training via Occupancy Prediction for Learning Transferable 3D Representations
Xiangchao Yan, Runjian Chen, Bo Zhang +11
Annotating 3D LiDAR point clouds for perception tasks is fundamental for many applications e.g., autonomous driving, yet it still remains notoriously labor-intensive. Pretraining-f…
ReSimAD: Zero-Shot 3D Domain Transfer for Autonomous Driving with Source Reconstruction and Target Simulation
Bo Zhang, Xinyu Cai, Jiakang Yuan +10
Domain shifts such as sensor type changes and geographical situation variations are prevalent in Autonomous Driving (AD), which poses a challenge since AD model relying on the prev…
AD-PT: Autonomous Driving Pre-Training with Large-scale Point Cloud Dataset
Jiakang Yuan, Bo Zhang, Xiangchao Yan +4
It is a long-term vision for Autonomous Driving (AD) community that the perception models can learn from a large-scale point cloud dataset, to obtain unified representations that c…
SUG: Single-dataset Unified Generalization for 3D Point Cloud Classification
Siyuan Huang, Bo Zhang, Botian Shi +3
Although Domain Generalization (DG) problem has been fast-growing in the 2D image tasks, its exploration on 3D point cloud data is still insufficient and challenged by more complex…
Uni3D: A Unified Baseline for Multi-dataset 3D Object Detection
Bo Zhang, Jiakang Yuan, Botian Shi +3
Current 3D object detection models follow a single dataset-specific training and testing paradigm, which often faces a serious detection accuracy drop when they are directly deploy…
Generative Diffusion Prior for Unified Image Restoration and Enhancement
Ben Fei, Zhaoyang Lyu, Liang Pan +5
Existing image restoration methods mostly leverage the posterior distribution of natural images. However, they often assume known degradation and also require supervised training,…
What Makes for Effective Few-shot Point Cloud Classification?
Chuangguan Ye, Hongyuan Zhu, Yongbin Liao +3
Due to the emergence of powerful computing resources and large-scale annotated datasets, deep learning has seen wide applications in our daily life. However, most current methods r…
A Closer Look at Few-Shot 3D Point Cloud Classification
Chuangguan Ye, Hongyuan Zhu, Bo Zhang +1
In recent years, research on few-shot learning (FSL) has been fast-growing in the 2D image domain due to the less requirement for labeled training data and greater generalization f…
Performance-aware Approximation of Global Channel Pruning for Multitask CNNs
Hancheng Ye, Bo Zhang, Tao Chen +2
Global channel pruning (GCP) aims to remove a subset of channels (filters) across different layers from a deep model without hurting the performance. Previous works focus on either…
Bi3D: Bi-domain Active Learning for Cross-domain 3D Object Detection
Jiakang Yuan, Bo Zhang, Xiangchao Yan +4
Unsupervised Domain Adaptation (UDA) technique has been explored in 3D cross-domain tasks recently. Though preliminary progress has been made, the performance gap between the UDA-b…