M. Ovsjanikov
Computer Vision and Pattern Recognition · 2010–2020
A researcher in the Science corpus: 12 papers; trusts 616 researchers and is trusted by 49 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 194 citations into it; #4,921 of 21,575 by trust here, #4,807 by citations
- Computational Mechanics — 165 citations into it; #1,619 of 10,001 by trust here, #1,325 by citations
- Computer Graphics and Computer-Aided Design — 79 citations into it; #493 of 1,584 by trust here, #486 by citations
- Aerospace Engineering — 38 citations into it
- Geology — 33 citations into it
- Artificial Intelligence — 30 citations into it
- 3D Vision & Pose — 24 citations into it; #203 of 689 by trust here, #178 by citations
- Model Architectures — 23 citations into it
Whom M. Ovsjanikov trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- A. Bronstein
- Michael M. Bronstein
- Yaron Lipman
- Alex Krizhevsky
- Kaiming He
- Geoffrey E. Hinton
- Diederik P. Kingma
- David G. Lowe
- Yann LeCun
- Jonathan Masci
Who trusts M. Ovsjanikov overall
- Davide Boscaini
- Jonathan Masci
- Emanuele Rodolà
- Michael M. Bronstein
- Matthias Vestner
- Thibault Groueix
- Vladimir G. Kim
- Nicholas Sharp
- Adrien Poulenard
- A. Bronstein
Papers from M. Ovsjanikov's perspective
Ordered by how strongly the researchers M. Ovsjanikov trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- 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
- Shape google: Geometric words and expressions for invariant shape retrieval (2011) — 837 citations
- Unsupervised Multi-class Joint Image Segmentation (2014) — 69 citations
- Image webs: Computing and exploiting connectivity in image collections (2010) — 110 citations
- Functional maps (2012) — 440 citations
- One Point Isometric Matching with the Heat Kernel (2010) — 361 citations
- Multi-directional geodesic neural networks via equivariant convolution (2018) — 103 citations
- DiffusionNet: Discretization Agnostic Learning on Surfaces (2020) — 312 citations
- PointCleanNet: Learning to Denoise and Remove Outliers from Dense Point Clouds (2019) — 406 citations
- PCPNet Learning Local Shape Properties from Raw Point Clouds (2017) — 426 citations
- Map-based exploration of intrinsic shape differences and variability (2013) — 164 citations
- Voronoi-Based Curvature and Feature Estimation from Point Clouds (2011) — 234 citations
- Eurographics Symposium on Geometry Processing 2009 a Concise and Provably Informative Multi-scale Signature Based on Heat Diffusion