Tanner Schmidt

Computer Vision and Pattern Recognition · 2014–2021

A researcher in the Science corpus: 5 papers; trusts 438 researchers and is trusted by 153 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 141 citations into it; #2,860 of 21,575 by trust here, #4,001 by citations
• Aerospace Engineering — 54 citations into it; #1,127 of 6,421 by trust here, #1,738 by citations
• Computer Graphics and Computer-Aided Design — 48 citations into it
• 3D Vision & Pose — 45 citations into it; #262 of 689 by trust here, #287 by citations
• Computational Mechanics — 42 citations into it
• Geology — 20 citations into it
• Control and Systems Engineering — 15 citations into it; #1,892 of 8,257 by trust here, #1,017 by citations
• Artificial Intelligence — 12 citations into it

Whom Tanner Schmidt trusts overall

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

1. David G. Lowe
2. Ross B. Girshick
3. Yann LeCun
4. Jamie Shotton
5. Geoffrey E. Hinton
6. Nitish Srivastava
7. Alex Krizhevsky
8. Andrew William Fitzgibbon
9. Kaiming He
10. Richard A. Newcombe

Who trusts Tanner Schmidt overall

1. Dieter Fox
2. Andrew J. Davison
3. Arunkumar Byravan
4. Peter R. Florence
5. Yu Xiang
6. Xinke Deng
7. Eric Brachmann
8. Lucas Manuelli
9. Connor Schenck
10. Caelan Reed Garrett

Papers from Tanner Schmidt's perspective

Ordered by how strongly the researchers Tanner Schmidt 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. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
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

• PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes (2017) — 2,347 citations
• Self-Supervised Visual Descriptor Learning for Dense Correspondence (2017) — 172 citations
• Deep Local Shapes: Learning Local SDF Priors for Detailed 3D Reconstruction (2020) — 522 citations
• DART: Dense Articulated Real-Time Tracking (2014) — 156 citations
• Neural 3D Video Synthesis from Multi-view Video (2021) — 473 citations