Shichen Liu

Computer Vision and Pattern Recognition · with Li · 2018–2019

A researcher in the Science corpus: 4 papers; trusts 637 researchers and is trusted by 25 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 139 citations into it; #6,083 of 21,575 by trust here, #7,092 by citations
• Computational Mechanics — 52 citations into it; #2,040 of 10,001 by trust here, #1,624 by citations
• Artificial Intelligence — 46 citations into it
• 3D Vision & Pose — 36 citations into it; #502 of 689 by trust here, #421 by citations
• Computer Graphics and Computer-Aided Design — 35 citations into it; #323 of 1,584 by trust here, #376 by citations
• Representation & Self-Supervised Learning — 19 citations into it; #2,558 of 3,145 by trust here, #2,263 by citations
• Aerospace Engineering — 17 citations into it
• Radiology, Nuclear Medicine and Imaging — 12 citations into it

Whom Shichen Liu trusts overall

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

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

Who trusts Shichen Liu overall

1. Alex Yu
2. Jeremy Reizenstein
3. Lingjie Liu
4. Matthew Tancik
5. Michael Niemeyer
6. Wenzheng Chen
7. Shunsuke Saito
8. Hanbyul Joo
9. Ivan Skorokhodov
10. Zeng Huang

Papers from Shichen Liu's perspective

Ordered by how strongly the researchers Shichen Liu 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. 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

• Soft Rasterizer: A Differentiable Renderer for Image-Based 3D Reasoning (2019) — 812 citations
• Learning to Infer Implicit Surfaces without 3D Supervision (2019) — 213 citations
• Soft Rasterizer: Differentiable Rendering for Unsupervised Single-View Mesh Reconstruction (2019) — 100 citations
• Generalized Zero-Shot Learning with Deep Calibration Network (2018) — 265 citations