Julian Straub

Computer Vision and Pattern Recognition · 2019–2020

A researcher in the Science corpus: 4 papers; trusts 230 researchers and is trusted by 339 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 111 citations into it; #2,212 of 21,575 by trust here, #1,538 by citations
• Computational Mechanics — 56 citations into it; #1,388 of 10,001 by trust here, #350 by citations
• 3D Vision & Pose — 45 citations into it; #184 of 689 by trust here, #41 by citations
• Aerospace Engineering — 45 citations into it
• Computer Graphics and Computer-Aided Design — 39 citations into it; #229 of 1,584 by trust here, #91 by citations
• Geology — 26 citations into it
• Artificial Intelligence — 23 citations into it; #21,583 of 35,345 by trust here, #5,720 by citations
• Generative Models — 16 citations into it

Whom Julian Straub trusts overall

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

1. Alex Krizhevsky
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Kaiming He
5. Yoshua Bengio
6. Ilya Sutskever
7. Yann LeCun
8. Ross B. Girshick
9. Angela Dai
10. Jitendra Malik

Who trusts Julian Straub overall

1. Michael Niemeyer
2. Vincent Sitzmann
3. Songyou Peng
4. Zehao Yu
5. Andreas Geiger
6. Changan Chen
7. Shuaifeng Zhi
8. Devendra Singh Chaplot
9. Anpei Chen
10. Sosuke Kobayashi

Papers from Julian Straub's perspective

Ordered by how strongly the researchers Julian Straub 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. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation (2019) — 4,653 citations
• Habitat: A Platform for Embodied AI Research (2019) — 2,003 citations
• The Replica Dataset: A Digital Replica of Indoor Spaces (2019) — 1,356 citations
• Deep Local Shapes: Learning Local SDF Priors for Detailed 3D Reconstruction (2020) — 522 citations