Jiageng Mao

Orcid: 0000-0003-2571-8767

According to our database1, Jiageng Mao authored at least 15 papers between 2019 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

On csauthors.net:

Bibliography

2024
Driving Everywhere with Large Language Model Policy Adaptation.
CoRR, 2024

2023
3D Object Detection for Autonomous Driving: A Comprehensive Survey.
Int. J. Comput. Vis., August, 2023

A Language Agent for Autonomous Driving.
CoRR, 2023

GPT-Driver: Learning to Drive with GPT.
CoRR, 2023

CLIP<sup>2</sup>: Contrastive Language-Image-Point Pretraining from Real-World Point Cloud Data.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022
A survey on deep learning-based single image crowd counting: Network design, loss function and supervisory signal.
Neurocomputing, 2022

3D Object Detection for Autonomous Driving: A Review and New Outlooks.
CoRR, 2022

Point2Seq: Detecting 3D Objects as Sequences.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

2021
One Million Scenes for Autonomous Driving: ONCE Dataset.
CoRR, 2021

One Million Scenes for Autonomous Driving: ONCE Dataset.
Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021

SODA10M: A Large-Scale 2D Self/Semi-Supervised Object Detection Dataset for Autonomous Driving.
Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021

Voxel Transformer for 3D Object Detection.
Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, 2021

Pyramid R-CNN: Towards Better Performance and Adaptability for 3D Object Detection.
Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, 2021

2020
GRNet: Gridding Residual Network for Dense Point Cloud Completion.
Proceedings of the Computer Vision - ECCV 2020, 2020

2019
Interpolated Convolutional Networks for 3D Point Cloud Understanding.
Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision, 2019


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