Shunsuke Saito

Computer Vision and Pattern Recognition · 2016–2021

A researcher in the Science corpus: 8 papers; trusts 625 researchers and is trusted by 91 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 335 citations into it; #3,348 of 21,575 by trust here, #2,926 by citations
• Computational Mechanics — 166 citations into it; #1,437 of 10,001 by trust here, #485 by citations
• Computer Graphics and Computer-Aided Design — 142 citations into it; #1,183 of 1,584 by trust here, #526 by citations
• 3D Vision & Pose — 64 citations into it; #230 of 689 by trust here, #146 by citations
• Generative Models — 41 citations into it
• Artificial Intelligence — 21 citations into it
• Model Architectures — 12 citations into it
• Media Technology — 11 citations into it

Whom Shunsuke Saito trusts overall

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

1. Kaiming He
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Karen Simonyan
5. Diederik P. Kingma
6. Alexei A. Efros
7. Jia Deng
8. Ross B. Girshick
9. Andrew Zisserman
10. Yoshua Bengio

Who trusts Shunsuke Saito overall

1. Michael Niemeyer
2. Zerong Zheng
3. Philipp Henzler
4. Yinbo Chen
5. Yuliang Xiu
6. Lingjie Liu
7. Tianchang Shen
8. Peng Wang
9. Michael Oechsle
10. Julian Chibane

Papers from Shunsuke Saito's perspective

Ordered by how strongly the researchers Shunsuke Saito 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. 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

• PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization (2019) — 1,437 citations
• PIFuHD: Multi-Level Pixel-Aligned Implicit Function for High-Resolution 3D Human Digitization (2020) — 874 citations
• Learning to Infer Implicit Surfaces without 3D Supervision (2019) — 213 citations
• paGAN (2018) — 167 citations
• Photorealistic Facial Texture Inference Using Deep Neural Networks (2016) — 138 citations
• Neural Fields in Visual Computing and Beyond (2021) — 820 citations
• Production-level facial performance capture using deep convolutional neural networks (2016) — 104 citations
• Pixel-aligned Volumetric Avatars (2021) — 52 citations