Ulrich Neumann

Computer Vision and Pattern Recognition · 2000–2022

A researcher in the Science corpus: 10 papers; trusts 1,048 researchers and is trusted by 60 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 198 citations into it; #4,478 of 21,575 by trust here, #3,222 by citations
• Computational Mechanics — 79 citations into it; #1,779 of 10,001 by trust here, #746 by citations
• 3D Vision & Pose — 61 citations into it; #505 of 689 by trust here, #220 by citations
• Artificial Intelligence — 60 citations into it
• Aerospace Engineering — 48 citations into it
• Computer Graphics and Computer-Aided Design — 47 citations into it; #701 of 1,584 by trust here, #337 by citations
• Geology — 29 citations into it; #89 of 687 by trust here, #72 by citations
• Generative Models — 27 citations into it

Whom Ulrich Neumann trusts overall

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

1. Kaiming He
2. Alex Krizhevsky
3. Diederik P. Kingma
4. Geoffrey E. Hinton
5. Ross B. Girshick
6. Yann LeCun
7. Karen Simonyan
8. Yoshua Bengio
9. Ilya Sutskever
10. Andrew Zisserman

Who trusts Ulrich Neumann overall

1. H. Wang
2. Shaoshuai Shi
3. Jiahui Yu
4. Christopher Xie
5. Hengli Wang
6. Volker Blanz
7. Zhijian Liu
8. Lin-Zhuo Chen
9. Huayao Liu
10. Chen Liu

Papers from Ulrich Neumann's perspective

Ordered by how strongly the researchers Ulrich Neumann 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. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• DISN: Deep Implicit Surface Network for High-quality Single-view 3D Reconstruction (2019) — 619 citations
• SGPN: Similarity Group Proposal Network for 3D Point Cloud Instance Segmentation (2017) — 616 citations
• Expression cloning (2001) — 504 citations
• Shape Inpainting Using 3D Generative Adversarial Network and Recurrent Convolutional Networks (2017) — 186 citations
• Point-NeRF: Point-based Neural Radiance Fields (2022) — 755 citations
• Analysis of emotion recognition using facial expressions, speech and multimodal information (2004) — 952 citations
• Depth-aware CNN for RGB-D Segmentation (2018) — 292 citations
• Learning to Prune Filters in Convolutional Neural Networks (2018) — 186 citations
• Recurrent Slice Networks for 3D Segmentation of Point Clouds (2018) — 484 citations
• The Virtual Classroom: A Virtual Reality Environment for the Assessment and Rehabilitation of Attention Deficits (2000) — 347 citations