Lefei Zhang

Computer Vision and Pattern Recognition · 2012–2025

A researcher in the Science corpus: 12 papers; trusts 793 researchers and is trusted by 35 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 235 citations into it; #10,848 of 21,575 by trust here, #6,146 by citations
• Media Technology — 122 citations into it; #480 of 3,621 by trust here, #330 by citations
• Artificial Intelligence — 107 citations into it; #30,641 of 35,345 by trust here, #19,018 by citations
• Atmospheric Science — 61 citations into it; #4,173 of 4,804 by trust here, #2,779 by citations
• Representation & Self-Supervised Learning — 54 citations into it
• Model Architectures — 30 citations into it
• Signal Processing — 23 citations into it
• Optimization & Training — 21 citations into it

Whom Lefei Zhang trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Yoshua Bengio
4. Yann LeCun
5. Kaiming He
6. Diederik P. Kingma
7. David G. Lowe
8. Ilya Sutskever
9. Ross B. Girshick
10. Karen Simonyan

Who trusts Lefei Zhang overall

1. Liangpei Zhang
2. Qiangqiang Yuan
3. Bo Du
4. Ying Li
5. Xiuping Jia
6. Gong Cheng
7. Zilong Zhong
8. Mercedes Eugenia Paoletti
9. Rahaf Aljundi
10. Xiuwen Gong

Papers from Lefei Zhang's perspective

Ordered by how strongly the researchers Lefei Zhang trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. ImageNet: A large-scale hierarchical image database
6. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Going deeper with convolutions
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Regularizing Deep Convolutional Neural Networks with a Structured Decorrelation Constraint (2016) — 40 citations
• Recurrent Feature Reasoning for Image Inpainting (2020) — 445 citations
• Unsupervised Domain Adaptive Re-Identification: Theory and Practice (2018) — 363 citations
• MambaHSI: Spatial–Spectral Mamba for Hyperspectral Image Classification (2025) — 309 citations
• Multiscale Dynamic Graph Convolutional Network for Hyperspectral Image Classification (2019) — 463 citations
• Self-Ensembling Attention Networks: Addressing Domain Shift for Semantic Segmentation (2019) — 75 citations
• Stacked Convolutional Denoising Auto-Encoders for Feature Representation (2017) — 390 citations
• Deep Learning for Remote Sensing Data: A Technical Tutorial on the State of the Art (2016) — 2,002 citations
• Ensemble manifold regularized sparse low-rank approximation for multiview feature embedding (2015) — 302 citations
• Tensor Discriminative Locality Alignment for Hyperspectral Image Spectral–Spatial Feature Extraction (2013) — 308 citations
• Hyperspectral image noise reduction based on rank-1 tensor decomposition (2013) — 112 citations
• On Combining Multiple Features for Hyperspectral Remote Sensing Image Classification (2012) — 406 citations