Kevis-Kokitsi Maninis

Computer Vision and Pattern Recognition · 2016–2019

A researcher in the Science corpus: 7 papers; trusts 1,540 researchers and is trusted by 427 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 292 citations into it; #2,010 of 21,575 by trust here, #3,239 by citations
• Artificial Intelligence — 78 citations into it; #19,820 of 35,345 by trust here, #22,463 by citations
• Model Architectures — 25 citations into it
• Evaluation & Benchmarks — 20 citations into it
• Media Technology — 18 citations into it
• Aerospace Engineering — 15 citations into it
• Radiology, Nuclear Medicine and Imaging — 14 citations into it; #2,624 of 3,265 by trust here, #2,321 by citations
• Ophthalmology — 11 citations into it; #204 of 222 by trust here, #97 by citations

Whom Kevis-Kokitsi Maninis trusts overall

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

1. Ross B. Girshick
2. Kaiming He
3. Alex Krizhevsky
4. Karen Simonyan
5. Geoffrey E. Hinton
6. Jitendra Malik
7. Pablo Arbeláez
8. Andrew Zisserman
9. Piotr Doll'ar
10. Yann LeCun

Who trusts Kevis-Kokitsi Maninis overall

1. Paul Voigtlaender
2. Wenguan Wang
3. Shu Kong
4. Luc Van Gool
5. Simon Vandenhende
6. Seoung Wug Oh
7. Ho Kei Cheng
8. Sergi Caelles
9. Golnaz Ghiasi
10. Rodrigo Benenson

Papers from Kevis-Kokitsi Maninis's perspective

Ordered by how strongly the researchers Kevis-Kokitsi Maninis trusts endorse each paper. Same corpus, a different order for every researcher.

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

Their papers

• One-Shot Video Object Segmentation (2016) — 984 citations
• Deep Extreme Cut: From Extreme Points to Object Segmentation (2017) — 462 citations
• Attentive Single-Tasking of Multiple Tasks (2019) — 292 citations
• Video Object Segmentation without Temporal Information (2017) — 358 citations
• The 2019 DAVIS Challenge on VOS: Unsupervised Multi-Object Segmentation (2019) — 179 citations
• Convolutional Oriented Boundaries: From Image Segmentation to High-Level Tasks (2016) — 249 citations
• Deep Retinal Image Understanding (2016) — 540 citations