Shikun Liu

Computer Vision and Pattern Recognition · 2017–2021

A researcher in the Science corpus: 4 papers; trusts 709 researchers and is trusted by 33 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 106 citations into it; #5,594 of 21,575 by trust here, #7,092 by citations
• Artificial Intelligence — 69 citations into it; #9,874 of 35,345 by trust here, #13,760 by citations
• Representation & Self-Supervised Learning — 28 citations into it
• Computational Mechanics — 24 citations into it; #4,813 of 10,001 by trust here, #8,258 by citations
• Aerospace Engineering — 23 citations into it; #5,375 of 6,421 by trust here, #3,388 by citations
• 3D Vision & Pose — 18 citations into it; #404 of 689 by trust here, #648 by citations
• Generative Models — 15 citations into it; #1,037 of 1,956 by trust here, #1,843 by citations
• Computer Graphics and Computer-Aided Design — 14 citations into it; #786 of 1,584 by trust here, #1,336 by citations

Whom Shikun Liu trusts overall

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

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

Who trusts Shikun Liu overall

1. Michael Crawshaw
2. Simon Vandenhende
3. Christopher Fifty
4. Yin Li
5. Ximeng Sun
6. Julian Chibane
7. Shagun Sodhani
8. Xian-Feng Han
9. Sébastien Jean
10. Lyle Regenwetter

Papers from Shikun Liu's perspective

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

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

Their papers

• End-To-End Multi-Task Learning With Attention (2018) — 1,381 citations
• iMAP: Implicit Mapping and Positioning in Real-Time (2021) — 888 citations
• Learning a Hierarchical Latent-Variable Model of 3D Shapes (2017) — 66 citations
• Bootstrapping Semantic Segmentation with Regional Contrast (2021) — 159 citations