Julian Chibane

Computer Vision and Pattern Recognition · 2020

A researcher in the Science corpus: 2 papers; trusts 572 researchers and is trusted by 16 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 102 citations into it; #9,205 of 21,575 by trust here, #12,689 by citations
• Computational Mechanics — 88 citations into it; #2,578 of 10,001 by trust here, #3,029 by citations
• 3D Vision & Pose — 64 citations into it
• Computer Graphics and Computer-Aided Design — 49 citations into it; #1,234 of 1,584 by trust here, #1,084 by citations
• Generative Models — 20 citations into it
• Geology — 20 citations into it
• Aerospace Engineering — 14 citations into it

Whom Julian Chibane trusts overall

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

1. Gerard Pons-Moll
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Thiemo Alldieck
5. Kaiming He
6. Diederik P. Kingma
7. Ross B. Girshick
8. Charles Ruizhongtai Qi
9. Yann LeCun
10. Jitendra Malik

Who trusts Julian Chibane overall

1. Bharat Lal Bhatnagar
2. Songyou Peng
3. Zerong Zheng
4. Gerard Pons-Moll
5. Nilesh Kulkarni
6. Biao Zhang
7. Tong He
8. T. Muller
9. Michael Niemeyer
10. Zehao Yu

Papers from Julian Chibane's perspective

Ordered by how strongly the researchers Julian Chibane 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
7. Gradient-based learning applied to document recognition
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

• Implicit Functions in Feature Space for 3D Shape Reconstruction and Completion (2020) — 568 citations
• Neural Unsigned Distance Fields for Implicit Function Learning (2020) — 406 citations