Philipp Henzler
Computer Vision and Pattern Recognition · 2019–2024
A researcher in the Science corpus: 5 papers; trusts 466 researchers and is trusted by 4 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 199 citations into it; #14,770 of 21,575 by trust here, #7,852 by citations
- Computer Graphics and Computer-Aided Design — 120 citations into it
- Computational Mechanics — 91 citations into it; #6,738 of 10,001 by trust here, #1,867 by citations
- 3D Vision & Pose — 62 citations into it; #613 of 689 by trust here, #354 by citations
- Generative Models — 35 citations into it
- Aerospace Engineering — 14 citations into it
- Model Architectures — 13 citations into it
- Geology — 8 citations into it
Whom Philipp Henzler trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Kaiming He
- Karen Simonyan
- Geoffrey E. Hinton
- Alex Krizhevsky
- Diederik P. Kingma
- Andrew Zisserman
- Ross B. Girshick
- Yoshua Bengio
- Yann LeCun
- Ilya Sutskever
Who trusts Philipp Henzler overall
- Chaozheng Wu
- Donny Y. Chen
- Qianqian Wang
- Minghua Liu
- Jeremy Reizenstein
- Luke Melas-Kyriazi
- Pratul P. Srinivasan
- Justin Johnson
- Haofei Xu
- Vincent Leroy
Papers from Philipp Henzler's perspective
Ordered by how strongly the researchers Philipp Henzler trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- ImageNet Large Scale Visual Recognition Challenge
- GENERATIVE ADVERSARIAL NETS
Their papers
- Common Objects in 3D: Large-Scale Learning and Evaluation of Real-life 3D Category Reconstruction (2021) — 744 citations
- Unsupervised Learning of 3D Object Categories from Videos in the Wild (2021) — 81 citations
- Learning a Neural 3D Texture Space From 2D Exemplars (2019) — 95 citations
- ReconFusion: 3D Reconstruction with Diffusion Priors (2023) — 374 citations
- CAT3D: Create Anything in 3D with Multi-View Diffusion Models (2024) — 457 citations