Gyeongsik Moon

Computer Vision and Pattern Recognition · 2017–2020

A researcher in the Science corpus: 7 papers; trusts 1,152 researchers and is trusted by 29 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 230 citations into it; #4,677 of 21,575 by trust here, #6,485 by citations
• Human-Computer Interaction — 88 citations into it; #1,269 of 2,253 by trust here, #914 by citations
• Control and Systems Engineering — 56 citations into it; #5,115 of 8,257 by trust here, #2,782 by citations
• Artificial Intelligence — 42 citations into it; #23,688 of 35,345 by trust here, #25,040 by citations
• 3D Vision & Pose — 39 citations into it
• Computational Mechanics — 27 citations into it
• Evaluation & Benchmarks — 23 citations into it
• Media Technology — 21 citations into it

Whom Gyeongsik Moon trusts overall

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

1. Kaiming He
2. Diederik P. Kingma
3. Sergey Ioffe
4. Geoffrey E. Hinton
5. Ross B. Girshick
6. Alex Krizhevsky
7. Yann LeCun
8. Christian Szegedy
9. Georgios Pavlakos
10. Yoshua Bengio

Who trusts Gyeongsik Moon overall

1. Kevin Qinghong Lin
2. Jiefeng Li
3. Lijuan Wang
4. Ce Zheng
5. Kyoung Mu Lee
6. Muhammed Kocabas
7. Wenjie Zhu
8. Linlin Yang
9. Zicheng Liu
10. Georgios Pavlakos

Papers from Gyeongsik Moon's perspective

Ordered by how strongly the researchers Gyeongsik Moon 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. Gradient-based learning applied to document recognition
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. Long Short-Term Memory
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

• V2V-PoseNet: Voxel-to-Voxel Prediction Network for Accurate 3D Hand and Human Pose Estimation from a Single Depth Map (2017) — 470 citations
• InterHand2.6M: A Dataset and Baseline for 3D Interacting Hand Pose Estimation from a Single RGB Image (2020) — 402 citations
• Pose2Mesh: Graph Convolutional Network for 3D Human Pose and Mesh Recovery from a 2D Human Pose (2020) — 479 citations
• I2L-MeshNet: Image-to-Lixel Prediction Network for Accurate 3D Human Pose and Mesh Estimation from a Single RGB Image (2020) — 509 citations
• Camera Distance-Aware Top-Down Approach for 3D Multi-Person Pose Estimation From a Single RGB Image (2019) — 376 citations
• Depth-Based 3D Hand Pose Estimation: From Current Achievements to Future Goals (2017) — 46 citations
• NTIRE 2019 Challenge on Video Deblurring and Super-Resolution: Dataset and Study (2019) — 613 citations