David Novotný
Computer Vision and Pattern Recognition · 2016–2025
A researcher in the Science corpus: 9 papers; trusts 1,583 researchers and is trusted by 202 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 394 citations into it; #2,663 of 21,575 by trust here, #4,502 by citations
- 3D Vision & Pose — 105 citations into it; #260 of 689 by trust here, #170 by citations
- Computational Mechanics — 102 citations into it; #4,405 of 10,001 by trust here, #1,693 by citations
- Artificial Intelligence — 81 citations into it
- Aerospace Engineering — 79 citations into it; #6,213 of 6,421 by trust here, #3,519 by citations
- Computer Graphics and Computer-Aided Design — 76 citations into it
- Evaluation & Benchmarks — 39 citations into it; #2,648 of 3,147 by trust here, #1,895 by citations
- Geology — 31 citations into it
Whom David Novotný trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Kaiming He
- Ross B. Girshick
- Alex Krizhevsky
- Geoffrey E. Hinton
- Karen Simonyan
- Andrew Zisserman
- David G. Lowe
- Jitendra Malik
- Diederik P. Kingma
- Yann LeCun
Who trusts David Novotný overall
- Andrea Vedaldi
- Ruth C. Fong
- Shangzhe Wu
- Zhengqi Li
- Jiajun Wu
- Davy Neven
- Chaozheng Wu
- Aravindh Mahendran
- Bharat Singh
- Konstantin Sofiiuk
Papers from David Novotný's perspective
Ordered by how strongly the researchers David Novotný 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)
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- Going deeper with convolutions
Their papers
- Common Objects in 3D: Large-Scale Learning and Evaluation of Real-life 3D Category Reconstruction (2021) — 744 citations
- Accelerating 3D deep learning with PyTorch3D (2019) — 1,177 citations
- Learning 3D Object Categories by Looking Around Them (2017) — 88 citations
- Semi-convolutional Operators for Instance Segmentation (2018) — 88 citations
- C3DPO: Canonical 3D Pose Networks for Non-Rigid Structure From Motion (2019) — 129 citations
- Unsupervised Learning of 3D Object Categories from Videos in the Wild (2021) — 81 citations
- Self-Supervised Learning of Geometrically Stable Features Through Probabilistic Introspection (2018) — 73 citations
- VGGT: Visual Geometry Grounded Transformer (2025) — 1,511 citations
- I Have Seen Enough: Transferring Parts Across Categories (2016) — 23 citations