Xiaoxiang Zhu

Computer Vision and Pattern Recognition · 2010–2023

A researcher in the Science corpus: 14 papers; trusts 581 researchers and is trusted by 38 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 284 citations into it; #8,127 of 21,575 by trust here, #5,082 by citations
• Artificial Intelligence — 219 citations into it
• Media Technology — 164 citations into it; #418 of 3,621 by trust here, #224 by citations
• Aerospace Engineering — 58 citations into it; #2,274 of 6,421 by trust here, #4,388 by citations
• Probabilistic & Bayesian Methods — 56 citations into it
• Atmospheric Science — 52 citations into it; #1,728 of 4,804 by trust here, #2,243 by citations
• Optimization & Training — 48 citations into it
• Model Architectures — 38 citations into it

Whom Xiaoxiang Zhu trusts overall

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

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

Who trusts Xiaoxiang Zhu overall

1. Richard Bamler
2. Zhenwei Shi
3. Chaopeng Shen
4. Giuseppe Scarpa
5. Andrea Massa
6. Shutao Li
7. Liang Xiao
8. Pedram Ghamisi
9. Qiangqiang Yuan
10. Lichao Mou

Papers from Xiaoxiang Zhu's perspective

Ordered by how strongly the researchers Xiaoxiang Zhu 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. Long Short-Term Memory
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. Attention is All you Need (Neural Information Processing Systems)
9. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Deep learning in remote sensing: a review (2017) — 1,896 citations
• A Sparse Image Fusion Algorithm With Application to Pan-Sharpening (2013) — 436 citations
• Super-Resolution Power and Robustness of Compressive Sensing for Spectral Estimation With Application to Spaceborne Tomographic SAR (2012) — 336 citations
• Tomographic SAR Inversion by $L_{1}$ -Norm Regularization—The Compressive Sensing Approach (2010) — 546 citations
• Very High Resolution Spaceborne SAR Tomography in Urban Environment (2010) — 477 citations
• Cross-City Matters: A Multimodal Remote Sensing Benchmark Dataset for Cross-City Semantic Segmentation using High-Resolution Domain Adaptation Networks (2023) — 511 citations
• A survey of uncertainty in deep neural networks (2021) — 1,852 citations
• Deep Learning Meets SAR: Concepts, models, pitfalls, and perspectives (2020) — 314 citations
• Semantic segmentation of slums in satellite images using transfer learning on fully convolutional neural networks (2019) — 321 citations
• SEN12MS - A Curated Dataset of Georeferenced Multi-Spectral Sentinel-1/2 Imagery for Deep Learning and Data Fusion (2019) — 357 citations
• Learning Spectral-Spatial-Temporal Features via a Recurrent Convolutional Neural Network for Change Detection in Multispectral Imagery (2018) — 559 citations
• Building Instance Classification Using Street View Images (2018) — 351 citations
• An Augmented Linear Mixing Model to Address Spectral Variability for Hyperspectral Unmixing (2018) — 828 citations
• Deep Recurrent Neural Networks for Hyperspectral Image Classification (2017) — 1,124 citations