Lichao Mou

Computer Vision and Pattern Recognition · 2016–2022

A researcher in the Science corpus: 8 papers; trusts 797 researchers and is trusted by 33 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 260 citations into it; #7,194 of 21,575 by trust here, #5,013 by citations
• Artificial Intelligence — 150 citations into it; #26,746 of 35,345 by trust here, #29,412 by citations
• Media Technology — 133 citations into it; #388 of 3,621 by trust here, #245 by citations
• Representation & Self-Supervised Learning — 86 citations into it
• Atmospheric Science — 53 citations into it; #547 of 4,804 by trust here, #1,548 by citations
• Model Architectures — 35 citations into it
• Aerospace Engineering — 34 citations into it
• Ecology — 19 citations into it; #2,131 of 10,717 by trust here, #6,543 by citations

Whom Lichao Mou trusts overall

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

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

Who trusts Lichao Mou overall

1. Zhenwei Shi
2. Chaopeng Shen
3. Marc Rußwurm
4. Daifeng Peng
5. Xiaoxiang Zhu
6. Siwei Yu
7. Marco Körner
8. Valerio Marsocci
9. Xin He
10. Nicolas Audebert

Papers from Lichao Mou's perspective

Ordered by how strongly the researchers Lichao Mou 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. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
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
• Unsupervised Single-Scene Semantic Segmentation for Earth Observation (2022) — 41 citations
• Self Supervised Learning for Few Shot Hyperspectral Image Classification (2022) — 14 citations
• Self-Supervised Learning in Remote Sensing: A review (2022) — 357 citations
• Deep Learning Meets SAR: Concepts, models, pitfalls, and perspectives (2020) — 314 citations
• Learning Spectral-Spatial-Temporal Features via a Recurrent Convolutional Neural Network for Change Detection in Multispectral Imagery (2018) — 559 citations
• Deep Recurrent Neural Networks for Hyperspectral Image Classification (2017) — 1,124 citations
• Learning a Transferable Change Rule from a Recurrent Neural Network for Land Cover Change Detection (2016) — 327 citations