most citedUnsupervised Feature Learning by Autoencoder and Prototypical Contrastive Learning for Hyperspectral Classification

63 citations · 68 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CV2022★ 1 cited

Corn Yield Prediction based on Remotely Sensed Variables Using Variational Autoencoder and Multiple Instance Regression

Zeyu Cao, Yuchi Ma, Zhou Zhang

In the U.S., corn is the most produced crop and has been an essential part of the American diet. To meet the demand for supply chain management and regional food security, accurate…

cs.CV2022★ 4 cited

TORE: Token Reduction for Efficient Human Mesh Recovery with Transformer

Zhiyang Dou, Qingxuan Wu, Cheng Lin +5

In this paper, we introduce a set of simple yet effective TOken REduction (TORE) strategies for Transformer-based Human Mesh Recovery from monocular images. Current SOTA performanc…

cs.LG2022

Vertical Federated Linear Contextual Bandits

Zeyu Cao, Zhipeng Liang, Shu Zhang +5

In this paper, we investigate a novel problem of building contextual bandits in the vertical federated setting, i.e., contextual information is vertically distributed over differen…

cs.LG2022

Physics-informed MTA-UNet: Prediction of Thermal Stress and Thermal Deformation of Satellites

Zeyu Cao, Wen Yao, Wei Peng +2

The rapid analysis of thermal stress and deformation plays a pivotal role in the thermal control measures and optimization of the structural design of satellites. For achieving rea…

cs.CV2022

Cross-View-Prediction: Exploring Contrastive Feature for Hyperspectral Image Classification

Anyu Zhang, Haotian Wu, Zeyu Cao

This paper presents a self-supervised feature learning method for hyperspectral image classification. Our method tries to construct two different views of the raw hyperspectral ima…

cs.CV2020★ 63 cited

Unsupervised Feature Learning by Autoencoder and Prototypical Contrastive Learning for Hyperspectral Classification

Zeyu Cao, Xiaorun Li, Liaoying Zhao

Unsupervised learning methods for feature extraction are becoming more and more popular. We combine the popular contrastive learning method (prototypical contrastive learning) and…