Jhony Kaesemodel Pontes

Computer Vision and Pattern Recognition · 2017–2023

A researcher in the Science corpus: 4 papers; trusts 290 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 104 citations into it
• Computational Mechanics — 50 citations into it; #7,581 of 10,001 by trust here, #2,574 by citations
• Aerospace Engineering — 39 citations into it
• 3D Vision & Pose — 34 citations into it; #561 of 689 by trust here, #380 by citations
• Computer Graphics and Computer-Aided Design — 24 citations into it; #1,441 of 1,584 by trust here, #691 by citations
• Geology — 20 citations into it
• Evaluation & Benchmarks — 10 citations into it
• Representation & Self-Supervised Learning — 10 citations into it

Whom Jhony Kaesemodel Pontes trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Kaiming He
5. Yoshua Bengio
6. Danilo Jimenez Rezende
7. Yann LeCun
8. Ross B. Girshick
9. Jia Deng
10. Jimmy Ba

Who trusts Jhony Kaesemodel Pontes overall

1. Xian-Feng Han
2. Meng Tian
3. Michael Niemeyer
4. Yifan Wang
5. Julian Chibane
6. Yinbo Chen
7. Matthew Tancik
8. Hamid Laga
9. Marcelo H. Ang Jr.
10. Wenqi Xian

Papers from Jhony Kaesemodel Pontes's perspective

Ordered by how strongly the researchers Jhony Kaesemodel Pontes 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. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
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. Going deeper with convolutions

Their papers

• Deep Level Sets: Implicit Surface Representations for 3D Shape Inference (2019) — 131 citations
• Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecasting (2023) — 1,097 citations
• Implicit Surface Representations As Layers in Neural Networks (2019) — 302 citations
• Image2Mesh: A Learning Framework for Single Image 3D Reconstruction (2017) — 138 citations