Zhengqin Li

Computer Vision and Pattern Recognition · 2015–2020

A researcher in the Science corpus: 5 papers; trusts 447 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 132 citations into it; #7,423 of 21,575 by trust here, #10,905 by citations
• Aerospace Engineering — 33 citations into it
• Computer Graphics and Computer-Aided Design — 33 citations into it
• 3D Vision & Pose — 28 citations into it
• Computational Mechanics — 26 citations into it
• Artificial Intelligence — 11 citations into it
• Media Technology — 11 citations into it; #887 of 3,621 by trust here, #2,954 by citations
• Representation & Self-Supervised Learning — 9 citations into it

Whom Zhengqin Li trusts overall

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

1. Jitendra Malik
2. Jonathan T. Barron
3. Kaiming He
4. Yuri Boykov
5. Geoffrey E. Hinton
6. Alex Krizhevsky
7. Ross B. Girshick
8. Diederik P. Kingma
9. Shuran Song
10. Yann LeCun

Who trusts Zhengqin Li overall

1. Yang Zhang
2. Radhakrishna Achanta
3. Philip David
4. Joel Janai
5. Qichao Liu
6. Tao Lei
7. Kai Zhang
8. Boqing Gong
9. Mark Boss
10. Sabine Süsstrunk

Papers from Zhengqin Li's perspective

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

Their papers

• Learning to reconstruct shape and spatially-varying reflectance from a single image (2018) — 325 citations
• OpenRooms: An Open Framework for Photorealistic Indoor Scene Datasets (2020) — 113 citations
• Inverse Rendering for Complex Indoor Scenes: Shape, Spatially-Varying Lighting and SVBRDF From a Single Image (2019) — 329 citations
• Superpixel segmentation using Linear Spectral Clustering (2015) — 509 citations
• OpenRooms: An End-to-End Open Framework for Photorealistic Indoor Scene Datasets (2020) — 47 citations