Zhengqi Li

Computer Vision and Pattern Recognition · with Snavely · 2018–2020

A researcher in the Science corpus: 4 papers; trusts 820 researchers and is trusted by 167 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 137 citations into it; #2,782 of 21,575 by trust here, #4,584 by citations
• Aerospace Engineering — 35 citations into it; #568 of 6,421 by trust here, #1,602 by citations
• 3D Vision & Pose — 24 citations into it
• Media Technology — 22 citations into it; #446 of 3,621 by trust here, #809 by citations
• Computer Graphics and Computer-Aided Design — 18 citations into it; #541 of 1,584 by trust here, #455 by citations
• Evaluation & Benchmarks — 12 citations into it
• Computational Mechanics — 10 citations into it
• Artificial Intelligence — 9 citations into it

Whom Zhengqi Li trusts overall

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

1. Kaiming He
2. Diederik P. Kingma
3. Johannes L. Schönberger
4. Geoffrey E. Hinton
5. Alex Krizhevsky
6. Rob Fergus
7. Ross B. Girshick
8. Jitendra Malik
9. Christian Szegedy
10. Yoshua Bengio

Who trusts Zhengqi Li overall

1. Richard Tucker
2. Zhengqin Li
3. Paul-Edouard Sarlin
4. Jia-Bin Huang
5. René Ranftl
6. Vincent Leroy
7. Xuan Luo
8. Noah Snavely
9. Philipp Lindenberger
10. Keunhong Park

Papers from Zhengqi Li's perspective

Ordered by how strongly the researchers Zhengqi Li 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
7. Gradient-based learning applied to document recognition
8. Long Short-Term Memory
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Neural Scene Flow Fields for Space-Time View Synthesis of Dynamic Scenes (2020) — 1,038 citations
• MegaDepth: Learning Single-View Depth Prediction from Internet Photos (2018) — 1,385 citations
• Learning the Depths of Moving People by Watching Frozen People (2019) — 275 citations
• CGIntrinsics: Better Intrinsic Image Decomposition through Physically-Based Rendering (2018) — 202 citations