Iro Armeni
Computer Vision and Pattern Recognition · 2015–2019
A researcher in the Science corpus: 5 papers; trusts 431 researchers and is trusted by 275 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 133 citations into it; #2,389 of 21,575 by trust here, #2,792 by citations
- Aerospace Engineering — 68 citations into it; #433 of 6,421 by trust here, #323 by citations
- Geology — 36 citations into it; #119 of 687 by trust here, #61 by citations
- Computational Mechanics — 31 citations into it; #7,520 of 10,001 by trust here, #2,509 by citations
- Artificial Intelligence — 23 citations into it
- Evaluation & Benchmarks — 23 citations into it
- Model Architectures — 13 citations into it
Whom Iro Armeni trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Kaiming He
- Ross B. Girshick
- Geoffrey E. Hinton
- Diederik P. Kingma
- Alex Krizhevsky
- Yann LeCun
- Yoshua Bengio
- David G. Lowe
- Andrew Zisserman
- Rob Fergus
Who trusts Iro Armeni overall
- Charles Ruizhongtai Qi
- Antoni Rosinol
- Saurabh Gupta
- Angela Dai
- Jitendra Malik
- Tristan Swedish
- Zhijian Liu
- Hengshuang Zhao
- Amir Zamir
- Alberto Garcia-Garcia
Papers from Iro Armeni's perspective
Ordered by how strongly the researchers Iro Armeni 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
- Gradient-based learning applied to document recognition
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- 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
- 3D Semantic Parsing of Large-Scale Indoor Spaces (2016) — 2,075 citations
- Joint 2D-3D-Semantic Data for Indoor Scene Understanding (2017) — 1,038 citations
- SEGCloud: Semantic Segmentation of 3D Point Clouds (2017) — 833 citations
- 3D Scene Graph: A Structure for Unified Semantics, 3D Space, and Camera (2019) — 531 citations
- State of research in automatic as-built modelling (2015) — 341 citations