Agathoniki Trigoni

Computer Vision and Pattern Recognition · 2010–2019

A researcher in the Science corpus: 10 papers; trusts 655 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 249 citations into it; #8,223 of 21,575 by trust here, #5,287 by citations
• Computational Mechanics — 125 citations into it
• Aerospace Engineering — 108 citations into it; #1,578 of 6,421 by trust here, #891 by citations
• Geology — 79 citations into it
• 3D Vision & Pose — 75 citations into it
• Artificial Intelligence — 56 citations into it
• Generative Models — 48 citations into it
• Computer Graphics and Computer-Aided Design — 47 citations into it

Whom Agathoniki Trigoni trusts overall

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

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

Who trusts Agathoniki Trigoni overall

1. Devendra Singh Chaplot
2. João F. Henriques
3. Emilio Parisotto
4. Andrew J. Davison

Papers from Agathoniki Trigoni's perspective

Ordered by how strongly the researchers Agathoniki Trigoni trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. ImageNet: A large-scale hierarchical image database
6. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
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

• DeepVO: Towards end-to-end visual odometry with deep Recurrent Convolutional Neural Networks (2017) — 916 citations
• RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds (2019) — 2,046 citations
• VINet: Visual-Inertial Odometry as a Sequence-to-Sequence Learning Problem (2017) — 401 citations
• 3D-PhysNet: Learning the Intuitive Physics of Non-Rigid Object Deformations (2018) — 28 citations
• 3D Object Reconstruction from a Single Depth View with Adversarial Learning (2017) — 208 citations
• IONet: Learning to Cure the Curse of Drift in Inertial Odometry (2018) — 383 citations
• Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds (2019) — 390 citations
• Distilling Knowledge From a Deep Pose Regressor Network (2019) — 118 citations
• 3D Object Dense Reconstruction from a Single Depth View (2018) — 29 citations
• Supporting Search and Rescue Operations with UAVs (2010) — 524 citations