Yiyi Liao

Computer Vision and Pattern Recognition · 2015–2021

A researcher in the Science corpus: 7 papers; trusts 988 researchers and is trusted by 54 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 250 citations into it; #4,044 of 21,575 by trust here, #3,747 by citations
• Computational Mechanics — 103 citations into it; #1,883 of 10,001 by trust here, #1,190 by citations
• Computer Graphics and Computer-Aided Design — 96 citations into it; #222 of 1,584 by trust here, #153 by citations
• 3D Vision & Pose — 82 citations into it; #269 of 689 by trust here, #485 by citations
• Aerospace Engineering — 63 citations into it; #4,716 of 6,421 by trust here, #4,762 by citations
• Generative Models — 40 citations into it
• Artificial Intelligence — 33 citations into it
• Geology — 32 citations into it; #213 of 687 by trust here, #360 by citations

Whom Yiyi Liao trusts overall

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

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

Who trusts Yiyi Liao overall

1. Lei Tai
2. Julian Chibane
3. Zhiqin Chen
4. Lars M. Mescheder
5. Michael Niemeyer
6. Andreas Geiger
7. Songyou Peng
8. Rundi Wu
9. Axel Sauer
10. Fangchang Ma

Papers from Yiyi Liao's perspective

Ordered by how strongly the researchers Yiyi Liao 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• GRAF: Generative Radiance Fields for 3D-Aware Image Synthesis (2020) — 989 citations
• KiloNeRF: Speeding up Neural Radiance Fields with Thousands of Tiny MLPs (2021) — 995 citations
• Deep Marching Cubes: Learning Explicit Surface Representations (2018) — 297 citations
• KITTI-360: A Novel Dataset and Benchmarks for Urban Scene Understanding in 2D and 3D (2021) — 956 citations
• Understand scene categories by objects: A semantic regularized scene classifier using Convolutional Neural Networks (2015) — 111 citations
• Shape As Points: A Differentiable Poisson Solver (2021) — 300 citations
• Parse geometry from a line: Monocular depth estimation with partial laser observation (2016) — 108 citations