Zhaoqi Leng

According to our database1, Zhaoqi Leng authored at least 15 papers between 2020 and 2023.

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

Timeline

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Links

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Bibliography

2023
WOMD-LiDAR: Raw Sensor Dataset Benchmark for Motion Forecasting.
CoRR, 2023

LEF: Late-to-Early Temporal Fusion for LiDAR 3D Object Detection.
IROS, 2023

Lidar Augment: Searching for Scalable 3D LiDAR Data Augmentations.
Proceedings of the IEEE International Conference on Robotics and Automation, 2023

2022
A Point-of-Interest Recommendation Method Exploiting Sequential, Category and Geographical Influence.
ISPRS Int. J. Geo Inf., 2022

Long- and Short-Term Preference Modeling Based on Multi-Level Attention for Next POI Recommendation.
ISPRS Int. J. Geo Inf., 2022

LidarAugment: Searching for Scalable 3D LiDAR Data Augmentations.
CoRR, 2022

PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions.
CoRR, 2022

Multi-Class 3D Object Detection with Single-Class Supervision.
Proceedings of the 2022 International Conference on Robotics and Automation, 2022

PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions.
Proceedings of the Tenth International Conference on Learning Representations, 2022

A Long and Short Term Preference Model for Next Point of Interest Recommendation.
Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2022, 2022

SWFormer: Sparse Window Transformer for 3D Object Detection in Point Clouds.
Proceedings of the Computer Vision - ECCV 2022, 2022

LidarNAS: Unifying and Searching Neural Architectures for 3D Point Clouds.
Proceedings of the Computer Vision - ECCV 2022, 2022

PseudoAugment: Learning to Use Unlabeled Data for Data Augmentation in Point Clouds.
Proceedings of the Computer Vision - ECCV 2022, 2022

Next POI Recommendation Method Based on Category Preference and Attention Mechanism in LBSNs.
Proceedings of the Web and Big Data - 6th International Joint Conference, 2022

2020
Improving 3D Object Detection Through Progressive Population Based Augmentation.
Proceedings of the Computer Vision - ECCV 2020, 2020


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