Fanzhi Zeng

According to our database1, Fanzhi Zeng authored at least 15 papers between 2012 and 2024.

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

Timeline

Legend:

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In proceedings 
Article 
PhD thesis 
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Links

On csauthors.net:

Bibliography

2024
Rethinking Information Structures in RLHF: Reward Generalization from a Graph Theory Perspective.
CoRR, 2024

2023
EFSCNN: Encoded Feature Sphere Convolution Neural Network for fast non-rigid 3D models classification and retrieval.
Comput. Vis. Image Underst., August, 2023

AI Alignment: A Comprehensive Survey.
CoRR, 2023

2019
基于深度学习的三维形状特征提取方法 (3D Shape Feature Extraction Method Based on Deep Learning).
计算机科学, 2019

基于无监督学习的二维工程CAD模型端到端检索算法 (End-to-End Retrieval Algorithm of Two-dimensional Engineering CAD Model Based on Unsupervised Learning).
计算机科学, 2019

3D shape classification and retrieval based on polar view.
Inf. Sci., 2019

FVCNN: Fusion View Convolutional Neural Networks for Non-rigid 3D Shape Classification and Retrieval.
Proceedings of the Image and Graphics - 10th International Conference, 2019

2017
2D compressive sensing and multi-feature fusion for effective 3D shape retrieval.
Inf. Sci., 2017

An Object Detection Algorithm for Deep Learning Based on Batch Normalization.
Proceedings of the Smart Computing and Communication, 2017

Improved Three-Dimensional Model Feature of Non-rigid Based on HKS.
Proceedings of the Smart Computing and Communication, 2017

2016
基于多特征融合的三维模型检索算法 (3D Model Retrieval Algorithm Based on Multi Feature Fusion).
计算机科学, 2016

基于压缩感知的视频双水印算法研究 (Double Video Watermarking Algorithm Based on Compressive Sensing).
计算机科学, 2016

Hierarchical Visual Perception and Two-Dimensional Compressive Sensing for Effective Content-Based Color Image Retrieval.
Cogn. Comput., 2016

A gradient descent sparse adaptive matching pursuit algorithm based on compressive sensing.
Proceedings of the International Conference on Machine Learning and Cybernetics, 2016

2012
The research for tamper forensics on MPEG-2 video based on compressed sensing.
Proceedings of the International Conference on Machine Learning and Cybernetics, 2012


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