Daniel Vlasic

Computer Vision and Pattern Recognition · 2005–2021

A researcher in the Science corpus: 7 papers; trusts 292 researchers and is trusted by 34 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 119 citations into it; #5,435 of 21,575 by trust here, #4,402 by citations
• Computational Mechanics — 40 citations into it; #2,140 of 10,001 by trust here, #803 by citations
• Computer Graphics and Computer-Aided Design — 35 citations into it; #525 of 1,584 by trust here, #435 by citations
• Artificial Intelligence — 19 citations into it
• Control and Systems Engineering — 13 citations into it; #2,935 of 8,257 by trust here, #1,718 by citations
• Signal Processing — 11 citations into it
• Optimization & Training — 9 citations into it
• Representation & Self-Supervised Learning — 6 citations into it

Whom Daniel Vlasic trusts overall

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

1. Brian Curless
2. S. M. Seitz
3. Geoffrey E. Hinton
4. Michael E. Tipping
5. Alex Krizhevsky
6. Ilya Baran
7. Kaiming He
8. Sam T. Roweis
9. Yann LeCun
10. Diederik P. Kingma

Who trusts Daniel Vlasic overall

1. Zerong Zheng
2. Hao Li
3. Hanbyul Joo
4. Zeng Huang
5. Timo Bolkart
6. Yebin Liu
7. Alan Brunton
8. Michael Zollhöfer
9. Justus Thies
10. Michael J. Black

Papers from Daniel Vlasic's perspective

Ordered by how strongly the researchers Daniel Vlasic 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. Gradient-based learning applied to document recognition
5. ImageNet: A large-scale hierarchical image database
6. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
8. ImageNet Large Scale Visual Recognition Challenge
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Going deeper with convolutions

Their papers

• AutoFlow: Learning a Better Training Set for Optical Flow (2021) — 137 citations
• Learning Shape Templates With Structured Implicit Functions (2019) — 432 citations
• Face transfer with multilinear models (2005) — 553 citations
• Unsupervised Training for 3D Morphable Model Regression (2018) — 326 citations
• Dynamic shape capture using multi-view photometric stereo (2009) — 322 citations
• Practical motion capture in everyday surroundings (2007) — 430 citations
• Articulated mesh animation from multi-view silhouettes (2008) — 790 citations