Min Zhang

Orcid: 0000-0002-6940-7146

Affiliations:
  • University of Southern California, Department of Electrical and Computer Engineering, Los Angeles, CA, USA


According to our database1, Min Zhang authored at least 12 papers between 2019 and 2023.

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

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

Online presence:

On csauthors.net:

Bibliography

2023
S3I-PointHop: SO(3)-Invariant PointHop for 3D Point Cloud Classification.
Proceedings of the IEEE International Conference on Acoustics, 2023

2022
R-PointHop: A Green, Accurate, and Unsupervised Point Cloud Registration Method.
IEEE Trans. Image Process., 2022

GSIP: Green Semantic Segmentation of Large-Scale Indoor Point Clouds.
Pattern Recognit. Lett., 2022

GreenPCO: An Unsupervised Lightweight Point Cloud Odometry Method.
Proceedings of the 24th IEEE International Workshop on Multimedia Signal Processing, 2022

Enhancing Image Rescaling using Dual Latent Variables in Invertible Neural Network.
Proceedings of the MM '22: The 30th ACM International Conference on Multimedia, Lisboa, Portugal, October 10, 2022

2021
GPCO: An Unsupervised Green Point Cloud Odometry Method.
CoRR, 2021

2020
PointHop: An Explainable Machine Learning Method for Point Cloud Classification.
IEEE Trans. Multim., 2020

Unsupervised Feedforward Feature (UFF) Learning for Point Cloud Classification and Segmentation.
Proceedings of the 2020 IEEE International Conference on Visual Communications and Image Processing, 2020

Unsupervised Point Cloud Registration via Salient Points Analysis (SPA).
Proceedings of the 2020 IEEE International Conference on Visual Communications and Image Processing, 2020

Pointhop++: A Lightweight Learning Model on Point Sets for 3D Classification.
Proceedings of the IEEE International Conference on Image Processing, 2020

2019
Interpretable convolutional neural networks via feedforward design.
J. Vis. Commun. Image Represent., 2019

Semi-Supervised Learning Via Feedforward-Designed Convolutional Neural Networks.
Proceedings of the 2019 IEEE International Conference on Image Processing, 2019


  Loading...