Yunze Tong

Orcid: 0009-0003-2816-7224

According to our database1, Yunze Tong authored at least 13 papers between 2023 and 2026.

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

Timeline

Legend:

Book  In proceedings  Article  PhD thesis  Dataset  Other 

Links

On csauthors.net:

Bibliography

2026
R-VoxelMap: Accurate Voxel Mapping With Recursive Plane Fitting for Online LiDAR Odometry.
IEEE Robotics Autom. Lett., March, 2026

DOGL-SLAM: Dynamic Object-Level SLAM via Joint Gaussian-Landmark Tracking.
IEEE Robotics Autom. Lett., February, 2026

Alleviating Sparse Rewards by Modeling Step-Wise and Long-Term Sampling Effects in Flow-Based GRPO.
CoRR, February, 2026

2025
SPTL-LCC: Single-Shot, Pixel-Level, Target-Free, and LiDAR-Type Agnostic LiDAR-Camera Extrinsic Self-Calibration.
IEEE Trans. Aerosp. Electron. Syst., December, 2025

Noise Projection: Closing the Prompt-Agnostic Gap Behind Text-to-Image Misalignment in Diffusion Models.
CoRR, October, 2025

Asynchronous Denoising Diffusion Models for Aligning Text-to-Image Generation.
CoRR, October, 2025

Target-Free and User-Friendly Online Extrinsic Calibration of LiDAR-IMU-Camera Systems Guided by Motion Excitation Assessment.
IEEE Trans. Intell. Veh., January, 2025

Latent Score-Based Reweighting for Robust Classification on Imbalanced Tabular Data.
Proceedings of the Forty-second International Conference on Machine Learning, 2025

Decoding Correlation-Induced Misalignment in the Stable Diffusion Workflow for Text-to-Image Generation.
Proceedings of the IEEE/CVF International Conference on Computer Vision, 2025

2024
TC$^{2}$LI-SLAM: A Tightly-Coupled Camera-LiDAR-Inertial SLAM System.
IEEE Robotics Autom. Lett., September, 2024

Explain Temporal Black-Box Models via Functional Decomposition.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023
Stable Prediction on Graphs with Agnostic Distribution Shifts.
Proceedings of the KDD'23 Workshop on Causal Discovery, 2023

Quantitatively Measuring and Contrastively Exploring Heterogeneity for Domain Generalization.
Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023


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